From a58800868022aa4a887dd353cd13e0b0dd21fce0 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 12:25:08 -0400 Subject: [PATCH 01/28] feat: Add DataFusion optimizer for INTERSECTS join algorithm selection Introduces the giql-datafusion Rust crate, a DataFusion PhysicalOptimizerRule that reads Parquet metadata and lightweight sampling to choose between sweep-line and binned equi-join algorithms for interval overlap joins. The optimizer collects statistics in two tiers: - Tier 1 (free): row group column stats, sorting_columns, page index presence from the Parquet file footer - Tier 2 (milliseconds): width distribution (median, p95, p99, CV) from sampling start/end columns of 1-3 representative row groups Decision function short-circuits to sweep line when the width distribution is heavy-tailed (p99/median > 10) or high-variance (CV > 1.5), and falls back to a cost comparison for uniform distributions where binning is viable. Both algorithms are implemented as custom ExecutionPlan nodes: - SweepLineJoinExec: sort + sweep with active set, O(n log n + k) - BinnedJoinExec: bin expansion + hash map probe + dedup, O(n*r + k) --- crates/giql-datafusion/Cargo.lock | 3211 +++++++++++++++++ crates/giql-datafusion/Cargo.toml | 17 + crates/giql-datafusion/src/cost.rs | 267 ++ .../giql-datafusion/src/exec/binned_join.rs | 352 ++ crates/giql-datafusion/src/exec/mod.rs | 5 + crates/giql-datafusion/src/exec/sweep_line.rs | 351 ++ crates/giql-datafusion/src/lib.rs | 79 + crates/giql-datafusion/src/optimizer.rs | 172 + crates/giql-datafusion/src/pattern.rs | 420 +++ crates/giql-datafusion/src/pruning.rs | 140 + crates/giql-datafusion/src/stats/metadata.rs | 273 ++ crates/giql-datafusion/src/stats/mod.rs | 94 + crates/giql-datafusion/src/stats/sampler.rs | 217 ++ crates/giql-datafusion/src/stats/types.rs | 82 + 14 files changed, 5680 insertions(+) create mode 100644 crates/giql-datafusion/Cargo.lock create mode 100644 crates/giql-datafusion/Cargo.toml create mode 100644 crates/giql-datafusion/src/cost.rs create mode 100644 crates/giql-datafusion/src/exec/binned_join.rs create mode 100644 crates/giql-datafusion/src/exec/mod.rs create mode 100644 crates/giql-datafusion/src/exec/sweep_line.rs create mode 100644 crates/giql-datafusion/src/lib.rs create mode 100644 crates/giql-datafusion/src/optimizer.rs create mode 100644 crates/giql-datafusion/src/pattern.rs create mode 100644 crates/giql-datafusion/src/pruning.rs create mode 100644 crates/giql-datafusion/src/stats/metadata.rs create mode 100644 crates/giql-datafusion/src/stats/mod.rs create mode 100644 crates/giql-datafusion/src/stats/sampler.rs create mode 100644 crates/giql-datafusion/src/stats/types.rs diff --git a/crates/giql-datafusion/Cargo.lock b/crates/giql-datafusion/Cargo.lock new file mode 100644 index 0000000..0403fa8 --- /dev/null +++ b/crates/giql-datafusion/Cargo.lock @@ -0,0 +1,3211 @@ +# This file is automatically @generated by Cargo. 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crate::IntersectsOptimizerConfig; + +/// Join algorithm selected by the cost model. +#[derive(Debug, Clone, PartialEq)] +pub enum JoinStrategy { + /// Default nested-loop join (no plan rewrite). + NestedLoop, + /// Sweep-line join: sort both sides by start, sweep with an active + /// set. O((n+m) log(n+m) + k). + SweepLine { + /// True if the input is already sorted and the sort step can + /// be skipped. + skip_sort: bool, + }, + /// Binned equi-join: expand intervals into genome bins, hash-join + /// on bin ID. O(n+m+k) amortized for uniform widths. + BinnedJoin { + /// Bin width in base pairs. + bin_size: usize, + }, +} + +/// Cost model for choosing the optimal INTERSECTS join algorithm. +/// +/// Uses two fast short-circuit signals (p99/median ratio and CV) to +/// detect distributions where binning degrades, and falls back to a +/// cost comparison for ambiguous cases. +pub struct CostModel { + p99_median_threshold: f64, + cv_threshold: f64, +} + +/// Relative cost constants for the cost comparison branch. +/// These are unitless scaling factors, not absolute times. +const HASH_COST: f64 = 1.0; +const COMPARE_COST: f64 = 2.0; + +/// Minimum and maximum bin sizes to clamp the adaptive bin size. +const MIN_BIN_SIZE: usize = 1_000; +const MAX_BIN_SIZE: usize = 1_000_000; + +impl CostModel { + pub fn new(config: &IntersectsOptimizerConfig) -> Self { + Self { + p99_median_threshold: config.p99_median_threshold, + cv_threshold: config.cv_threshold, + } + } + + /// Choose the optimal join strategy based on interval statistics + /// from both sides of the join. + pub fn decide( + &self, + left: &IntervalStats, + right: &IntervalStats, + ) -> JoinStrategy { + // Short-circuit 1: heavy-tailed distribution. + // If p99/median > threshold on either side, wide outliers will + // replicate across many bins, destroying binning performance. + if left.width.p99_median_ratio > self.p99_median_threshold + || right.width.p99_median_ratio > self.p99_median_threshold + { + let skip_sort = left.is_sorted_by_start + || right.is_sorted_by_start; + return JoinStrategy::SweepLine { skip_sort }; + } + + // Short-circuit 2: high width variance. + // No single bin size works well when CV is high. + if left.width.cv > self.cv_threshold + || right.width.cv > self.cv_threshold + { + let skip_sort = left.is_sorted_by_start + || right.is_sorted_by_start; + return JoinStrategy::SweepLine { skip_sort }; + } + + // Cost comparison: estimate binned vs sweep costs. + let bin_size = self.estimate_optimal_bin_size(left, right); + let binned_cost = self.estimate_binned_cost(left, right, bin_size); + let sweep_cost = self.estimate_sweep_cost(left, right); + + if binned_cost < sweep_cost { + JoinStrategy::BinnedJoin { bin_size } + } else { + let skip_sort = left.is_sorted_by_start + || right.is_sorted_by_start; + JoinStrategy::SweepLine { skip_sort } + } + } + + /// Estimate optimal bin size from the width distributions. + /// + /// Uses p95 as the bin width so that ~95% of intervals fit in a + /// single bin (replication factor ≈ 1). + fn estimate_optimal_bin_size( + &self, + left: &IntervalStats, + right: &IntervalStats, + ) -> usize { + // Use the larger p95 so both sides have low replication. + let p95 = left.width.p95.max(right.width.p95); + let raw = p95.ceil() as usize; + raw.clamp(MIN_BIN_SIZE, MAX_BIN_SIZE) + } + + /// Estimate cost of binned equi-join. + /// + /// Each interval is replicated into `mean_width / bin_size + 1` + /// bin entries, then hash-joined. Cost scales with total expanded + /// row count. + fn estimate_binned_cost( + &self, + left: &IntervalStats, + right: &IntervalStats, + bin_size: usize, + ) -> f64 { + let left_replication = + left.width.mean / bin_size as f64 + 1.0; + let right_replication = + right.width.mean / bin_size as f64 + 1.0; + + let expanded_left = + left.row_count as f64 * left_replication; + let expanded_right = + right.row_count as f64 * right_replication; + + (expanded_left + expanded_right) * HASH_COST + } + + /// Estimate cost of sweep-line join. + /// + /// Dominated by sorting both sides: O((n+m) log(n+m)). If either + /// side is already sorted, the cost drops by ~50%. + fn estimate_sweep_cost( + &self, + left: &IntervalStats, + right: &IntervalStats, + ) -> f64 { + let n = left.row_count as f64; + let m = right.row_count as f64; + let total = n + m; + + let mut cost = total * total.log2() * COMPARE_COST; + + // If either side is sorted, we skip one of the two sorts. + if left.is_sorted_by_start || right.is_sorted_by_start { + cost *= 0.5; + } + + cost + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::stats::{RowGroupBounds, WidthStats}; + + fn make_stats( + row_count: usize, + median: f64, + mean: f64, + p95: f64, + p99: f64, + cv: f64, + sorted: bool, + ) -> IntervalStats { + IntervalStats { + row_count, + domain_min: 0, + domain_max: 1_000_000, + is_sorted_by_start: sorted, + row_group_bounds: vec![RowGroupBounds { + min_start: 0, + max_start: 900_000, + min_end: 100, + max_end: 1_000_000, + row_count, + }], + width: WidthStats { + median, + mean, + p95, + p99, + cv, + p99_median_ratio: if median > 0.0 { + p99 / median + } else { + 0.0 + }, + }, + } + } + + fn default_config() -> IntersectsOptimizerConfig { + IntersectsOptimizerConfig::default() + } + + #[test] + fn test_heavy_tailed_selects_sweep_line() { + let model = CostModel::new(&default_config()); + // p99/median = 100/5 = 20 > 10 + let left = make_stats(100_000, 5.0, 10.0, 50.0, 100.0, 0.8, false); + let right = make_stats(100_000, 100.0, 100.0, 100.0, 100.0, 0.0, false); + + match model.decide(&left, &right) { + JoinStrategy::SweepLine { .. } => {} + other => panic!("Expected SweepLine, got {:?}", other), + } + } + + #[test] + fn test_high_cv_selects_sweep_line() { + let model = CostModel::new(&default_config()); + // CV = 2.0 > 1.5 + let left = make_stats(100_000, 100.0, 100.0, 100.0, 100.0, 2.0, false); + let right = make_stats(100_000, 100.0, 100.0, 100.0, 100.0, 0.5, false); + + match model.decide(&left, &right) { + JoinStrategy::SweepLine { .. } => {} + other => panic!("Expected SweepLine, got {:?}", other), + } + } + + #[test] + fn test_uniform_selects_binned() { + let model = CostModel::new(&default_config()); + // Uniform 100bp intervals, low CV, p99/median ≈ 1 + let left = make_stats(1_000_000, 100.0, 100.0, 100.0, 100.0, 0.0, false); + let right = make_stats(1_000_000, 100.0, 100.0, 100.0, 100.0, 0.0, false); + + match model.decide(&left, &right) { + JoinStrategy::BinnedJoin { bin_size } => { + assert!(bin_size >= MIN_BIN_SIZE); + } + other => panic!("Expected BinnedJoin, got {:?}", other), + } + } + + #[test] + fn test_sorted_input_sets_skip_sort() { + let model = CostModel::new(&default_config()); + // High CV triggers sweep line; sorted input should set skip_sort + let left = make_stats(1_000_000, 100.0, 500.0, 1000.0, 5000.0, 2.0, true); + let right = make_stats(1_000_000, 100.0, 500.0, 1000.0, 5000.0, 0.5, false); + + match model.decide(&left, &right) { + JoinStrategy::SweepLine { skip_sort } => { + assert!(skip_sort); + } + other => panic!("Expected SweepLine with skip_sort, got {:?}", other), + } + } + + #[test] + fn test_bin_size_clamped() { + let model = CostModel::new(&default_config()); + // Very small p95 — bin size should clamp to MIN_BIN_SIZE + let left = make_stats(100_000, 10.0, 10.0, 10.0, 10.0, 0.1, false); + let right = make_stats(100_000, 10.0, 10.0, 10.0, 10.0, 0.1, false); + + let bin_size = model.estimate_optimal_bin_size(&left, &right); + assert_eq!(bin_size, MIN_BIN_SIZE); + } +} diff --git a/crates/giql-datafusion/src/exec/binned_join.rs b/crates/giql-datafusion/src/exec/binned_join.rs new file mode 100644 index 0000000..e2616e8 --- /dev/null +++ b/crates/giql-datafusion/src/exec/binned_join.rs @@ -0,0 +1,352 @@ +use std::any::Any; +use std::collections::HashMap; +use std::fmt; +use std::sync::Arc; + +use arrow::array::{Array, ArrayRef, Int64Array, RecordBatch, StringArray}; +use arrow::datatypes::SchemaRef; +use datafusion::common::Result; +use datafusion::execution::SendableRecordBatchStream; +use datafusion::physical_expr::{EquivalenceProperties, Partitioning}; +use datafusion::physical_plan::stream::RecordBatchStreamAdapter; +use datafusion::physical_plan::execution_plan::{ + Boundedness, EmissionType, +}; +use datafusion::physical_plan::{ + DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, +}; + +use crate::pattern::IntervalColumns; + +/// Custom execution plan implementing the binned equi-join for +/// interval overlap. +/// +/// Each interval is expanded into genome bins of fixed size. A hash +/// map is built from the right side keyed by `(chrom, bin_id)`. The +/// left side probes the map, and a post-filter removes false positives. +/// Output is deduplicated to avoid emitting duplicate pairs when an +/// interval spans multiple bins. +/// +/// Complexity: O((n+m) * avg_replication + k) where avg_replication +/// is mean_width / bin_size + 1. +#[derive(Debug)] +pub struct BinnedJoinExec { + left: Arc, + right: Arc, + left_cols: IntervalColumns, + right_cols: IntervalColumns, + bin_size: usize, + schema: SchemaRef, + properties: PlanProperties, +} + +impl BinnedJoinExec { + pub fn new( + left: Arc, + right: Arc, + left_cols: IntervalColumns, + right_cols: IntervalColumns, + schema: SchemaRef, + bin_size: usize, + ) -> Self { + let properties = PlanProperties::new( + EquivalenceProperties::new(schema.clone()), + Partitioning::UnknownPartitioning(1), + EmissionType::Final, + Boundedness::Bounded, + ); + + Self { + left, + right, + left_cols, + right_cols, + bin_size, + schema, + properties, + } + } +} + +impl DisplayAs for BinnedJoinExec { + fn fmt_as( + &self, + _t: DisplayFormatType, + f: &mut fmt::Formatter<'_>, + ) -> fmt::Result { + write!(f, "BinnedJoinExec: bin_size={}", self.bin_size) + } +} + +impl ExecutionPlan for BinnedJoinExec { + fn name(&self) -> &str { + "BinnedJoinExec" + } + + fn as_any(&self) -> &dyn Any { + self + } + + fn schema(&self) -> SchemaRef { + self.schema.clone() + } + + fn properties(&self) -> &PlanProperties { + &self.properties + } + + fn children(&self) -> Vec<&Arc> { + vec![&self.left, &self.right] + } + + fn with_new_children( + self: Arc, + children: Vec>, + ) -> Result> { + Ok(Arc::new(BinnedJoinExec::new( + children[0].clone(), + children[1].clone(), + self.left_cols.clone(), + self.right_cols.clone(), + self.schema.clone(), + self.bin_size, + ))) + } + + fn execute( + &self, + partition: usize, + context: Arc, + ) -> Result { + let left_stream = + self.left.execute(partition, context.clone())?; + let right_stream = self.right.execute(partition, context)?; + + let left_cols = self.left_cols.clone(); + let right_cols = self.right_cols.clone(); + let schema = self.schema.clone(); + let bin_size = self.bin_size; + + let stream = futures::stream::once(async move { + let left_batches = collect_batches(left_stream).await?; + let right_batches = + collect_batches(right_stream).await?; + + binned_join( + &schema, + &left_batches, + &right_batches, + &left_cols, + &right_cols, + bin_size, + ) + }); + + Ok(Box::pin(RecordBatchStreamAdapter::new( + self.schema.clone(), + stream, + ))) + } +} + +/// A reference to a specific row in a batch. +#[derive(Debug, Clone, Hash, PartialEq, Eq)] +struct RowRef { + batch_idx: usize, + row_idx: usize, +} + +/// Interval data extracted from a row. +struct IntervalRow { + chrom: String, + start: i64, + end: i64, + row_ref: RowRef, +} + +/// Core binned join algorithm. +fn binned_join( + schema: &SchemaRef, + left_batches: &[RecordBatch], + right_batches: &[RecordBatch], + left_cols: &IntervalColumns, + right_cols: &IntervalColumns, + bin_size: usize, +) -> Result { + let right_intervals = + extract_interval_rows(right_batches, right_cols)?; + let mut right_map: HashMap<(String, i64), Vec> = + HashMap::new(); + + for (idx, interval) in right_intervals.iter().enumerate() { + let start_bin = interval.start / bin_size as i64; + let end_bin = (interval.end - 1) / bin_size as i64; + for bin in start_bin..=end_bin { + right_map + .entry((interval.chrom.clone(), bin)) + .or_default() + .push(idx); + } + } + + let left_intervals = + extract_interval_rows(left_batches, left_cols)?; + + let mut seen = std::collections::HashSet::new(); + let mut matches: Vec<(RowRef, RowRef)> = Vec::new(); + + for (li, l) in left_intervals.iter().enumerate() { + let start_bin = l.start / bin_size as i64; + let end_bin = (l.end - 1) / bin_size as i64; + + for bin in start_bin..=end_bin { + let key = (l.chrom.clone(), bin); + if let Some(right_indices) = right_map.get(&key) { + for &ri in right_indices { + if seen.contains(&(li, ri)) { + continue; + } + + let r = &right_intervals[ri]; + if l.start < r.end && l.end > r.start { + seen.insert((li, ri)); + matches.push(( + l.row_ref.clone(), + r.row_ref.clone(), + )); + } + } + } + } + } + + build_output(schema, left_batches, right_batches, &matches) +} + +/// Extract interval rows from batches. +fn extract_interval_rows( + batches: &[RecordBatch], + cols: &IntervalColumns, +) -> Result> { + let mut rows = Vec::new(); + + for (batch_idx, batch) in batches.iter().enumerate() { + let chroms = batch + .column(cols.chrom_idx) + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "Chrom column is not StringArray".to_string(), + ) + })?; + + let starts = batch + .column(cols.start_idx) + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "Start column is not Int64Array".to_string(), + ) + })?; + + let ends = batch + .column(cols.end_idx) + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "End column is not Int64Array".to_string(), + ) + })?; + + for row_idx in 0..batch.num_rows() { + if chroms.is_null(row_idx) + || starts.is_null(row_idx) + || ends.is_null(row_idx) + { + continue; + } + rows.push(IntervalRow { + chrom: chroms.value(row_idx).to_string(), + start: starts.value(row_idx), + end: ends.value(row_idx), + row_ref: RowRef { + batch_idx, + row_idx, + }, + }); + } + } + + Ok(rows) +} + +/// Build output RecordBatch from matched row pairs. +fn build_output( + schema: &SchemaRef, + left_batches: &[RecordBatch], + right_batches: &[RecordBatch], + matches: &[(RowRef, RowRef)], +) -> Result { + if matches.is_empty() { + return Ok(RecordBatch::new_empty(schema.clone())); + } + + let left_schema = left_batches[0].schema(); + let right_schema = right_batches[0].schema(); + let num_left_cols = left_schema.fields().len(); + let num_right_cols = right_schema.fields().len(); + + let mut columns: Vec = + Vec::with_capacity(num_left_cols + num_right_cols); + + for col_idx in 0..num_left_cols { + let values: Vec = matches + .iter() + .map(|(lr, _)| { + left_batches[lr.batch_idx] + .column(col_idx) + .slice(lr.row_idx, 1) + }) + .collect(); + + let refs: Vec<&dyn Array> = + values.iter().map(|a| a.as_ref()).collect(); + columns.push(arrow::compute::concat(&refs)?); + } + + for col_idx in 0..num_right_cols { + let values: Vec = matches + .iter() + .map(|(_, rr)| { + right_batches[rr.batch_idx] + .column(col_idx) + .slice(rr.row_idx, 1) + }) + .collect(); + + let refs: Vec<&dyn Array> = + values.iter().map(|a| a.as_ref()).collect(); + columns.push(arrow::compute::concat(&refs)?); + } + + Ok(RecordBatch::try_new(schema.clone(), columns)?) +} + +/// Collect all batches from a stream. +async fn collect_batches( + stream: SendableRecordBatchStream, +) -> Result> { + use futures::StreamExt; + + let mut batches = Vec::new(); + let mut stream = stream; + + while let Some(batch) = stream.next().await { + batches.push(batch?); + } + + Ok(batches) +} diff --git a/crates/giql-datafusion/src/exec/mod.rs b/crates/giql-datafusion/src/exec/mod.rs new file mode 100644 index 0000000..ac45e8d --- /dev/null +++ b/crates/giql-datafusion/src/exec/mod.rs @@ -0,0 +1,5 @@ +pub mod binned_join; +pub mod sweep_line; + +pub use binned_join::BinnedJoinExec; +pub use sweep_line::SweepLineJoinExec; diff --git a/crates/giql-datafusion/src/exec/sweep_line.rs b/crates/giql-datafusion/src/exec/sweep_line.rs new file mode 100644 index 0000000..fd43dc8 --- /dev/null +++ b/crates/giql-datafusion/src/exec/sweep_line.rs @@ -0,0 +1,351 @@ +use std::any::Any; +use std::fmt; +use std::sync::Arc; + +use arrow::array::{Array, ArrayRef, Int64Array, RecordBatch, StringArray}; +use arrow::datatypes::SchemaRef; +use datafusion::common::Result; +use datafusion::execution::SendableRecordBatchStream; +use datafusion::physical_expr::{EquivalenceProperties, Partitioning}; +use datafusion::physical_plan::stream::RecordBatchStreamAdapter; +use datafusion::physical_plan::execution_plan::{ + Boundedness, EmissionType, +}; +use datafusion::physical_plan::{ + DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, +}; + +use crate::pattern::IntervalColumns; + +/// Custom execution plan implementing the sweep-line interval join. +/// +/// Both inputs are sorted by `(chrom, start)`, then swept left to +/// right. For each left interval, all right intervals whose start is +/// less than the left's end are candidates; those whose end is greater +/// than the left's start are matches. +/// +/// Complexity: O((n+m) log(n+m) + k) where k is the output size. +/// If `skip_sort` is true, the sort is assumed already done and the +/// complexity is O(n+m+k). +#[derive(Debug)] +pub struct SweepLineJoinExec { + left: Arc, + right: Arc, + left_cols: IntervalColumns, + right_cols: IntervalColumns, + skip_sort: bool, + schema: SchemaRef, + properties: PlanProperties, +} + +impl SweepLineJoinExec { + pub fn new( + left: Arc, + right: Arc, + left_cols: IntervalColumns, + right_cols: IntervalColumns, + schema: SchemaRef, + skip_sort: bool, + ) -> Self { + let properties = PlanProperties::new( + EquivalenceProperties::new(schema.clone()), + Partitioning::UnknownPartitioning(1), + EmissionType::Final, + Boundedness::Bounded, + ); + + Self { + left, + right, + left_cols, + right_cols, + skip_sort, + schema, + properties, + } + } +} + +impl DisplayAs for SweepLineJoinExec { + fn fmt_as( + &self, + _t: DisplayFormatType, + f: &mut fmt::Formatter<'_>, + ) -> fmt::Result { + write!( + f, + "SweepLineJoinExec: skip_sort={}", + self.skip_sort + ) + } +} + +impl ExecutionPlan for SweepLineJoinExec { + fn name(&self) -> &str { + "SweepLineJoinExec" + } + + fn as_any(&self) -> &dyn Any { + self + } + + fn schema(&self) -> SchemaRef { + self.schema.clone() + } + + fn properties(&self) -> &PlanProperties { + &self.properties + } + + fn children(&self) -> Vec<&Arc> { + vec![&self.left, &self.right] + } + + fn with_new_children( + self: Arc, + children: Vec>, + ) -> Result> { + Ok(Arc::new(SweepLineJoinExec::new( + children[0].clone(), + children[1].clone(), + self.left_cols.clone(), + self.right_cols.clone(), + self.schema.clone(), + self.skip_sort, + ))) + } + + fn execute( + &self, + partition: usize, + context: Arc, + ) -> Result { + let left_stream = + self.left.execute(partition, context.clone())?; + let right_stream = self.right.execute(partition, context)?; + + let left_cols = self.left_cols.clone(); + let right_cols = self.right_cols.clone(); + let schema = self.schema.clone(); + + let stream = futures::stream::once(async move { + let left_batches = collect_batches(left_stream).await?; + let right_batches = collect_batches(right_stream).await?; + + let left_intervals = + extract_intervals(&left_batches, &left_cols)?; + let right_intervals = + extract_intervals(&right_batches, &right_cols)?; + + let matches = sweep_line_join( + &left_intervals, + &right_intervals, + ); + + build_output_batch( + &schema, + &left_batches, + &right_batches, + &matches, + ) + }); + + Ok(Box::pin(RecordBatchStreamAdapter::new( + self.schema.clone(), + stream, + ))) + } +} + +/// A flattened interval with a pointer back to its batch and row. +#[derive(Debug, Clone)] +struct FlatInterval { + chrom: String, + start: i64, + end: i64, + batch_idx: usize, + row_idx: usize, +} + +/// Extract all intervals from record batches into a flat sorted vec. +fn extract_intervals( + batches: &[RecordBatch], + cols: &IntervalColumns, +) -> Result> { + let mut intervals = Vec::new(); + + for (batch_idx, batch) in batches.iter().enumerate() { + let chroms = batch + .column(cols.chrom_idx) + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "Chrom column is not StringArray".to_string(), + ) + })?; + + let starts = batch + .column(cols.start_idx) + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "Start column is not Int64Array".to_string(), + ) + })?; + + let ends = batch + .column(cols.end_idx) + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "End column is not Int64Array".to_string(), + ) + })?; + + for row_idx in 0..batch.num_rows() { + if chroms.is_null(row_idx) + || starts.is_null(row_idx) + || ends.is_null(row_idx) + { + continue; + } + intervals.push(FlatInterval { + chrom: chroms.value(row_idx).to_string(), + start: starts.value(row_idx), + end: ends.value(row_idx), + batch_idx, + row_idx, + }); + } + } + + // Sort by (chrom, start) + intervals.sort_by(|a, b| { + a.chrom.cmp(&b.chrom).then(a.start.cmp(&b.start)) + }); + + Ok(intervals) +} + +/// Core sweep-line algorithm. +/// +/// Both inputs must be sorted by (chrom, start). For each chromosome, +/// maintains an active set of right intervals and sweeps left to right. +fn sweep_line_join( + left: &[FlatInterval], + right: &[FlatInterval], +) -> Vec<(usize, usize, usize, usize)> { + // (left_batch, left_row, right_batch, right_row) + let mut matches = Vec::new(); + + let mut right_idx = 0; + let mut active: Vec = Vec::new(); // indices into right + + for l in left { + + // Advance right_idx to add all right intervals with + // start < left.end on the same chromosome + while right_idx < right.len() { + let r = &right[right_idx]; + if r.chrom < l.chrom { + right_idx += 1; + continue; + } + if r.chrom > l.chrom { + break; + } + // Same chromosome + if r.start >= l.end { + break; + } + active.push(right_idx); + right_idx += 1; + } + + // Remove expired intervals from active set + active.retain(|&ri| { + let r = &right[ri]; + r.chrom == l.chrom && r.end > l.start + }); + + // All remaining active intervals overlap with l + for &ri in &active { + let r = &right[ri]; + matches.push(( + l.batch_idx, + l.row_idx, + r.batch_idx, + r.row_idx, + )); + } + } + + matches +} + +/// Build the output RecordBatch from matched pairs. +fn build_output_batch( + schema: &SchemaRef, + left_batches: &[RecordBatch], + right_batches: &[RecordBatch], + matches: &[(usize, usize, usize, usize)], +) -> Result { + if matches.is_empty() { + return Ok(RecordBatch::new_empty(schema.clone())); + } + + let left_schema = left_batches[0].schema(); + let right_schema = right_batches[0].schema(); + let num_left_cols = left_schema.fields().len(); + let num_right_cols = right_schema.fields().len(); + + let mut columns: Vec = + Vec::with_capacity(num_left_cols + num_right_cols); + + for col_idx in 0..num_left_cols { + let values: Vec = matches + .iter() + .map(|&(lb, lr, _, _)| { + left_batches[lb].column(col_idx).slice(lr, 1) + }) + .collect(); + + let refs: Vec<&dyn Array> = + values.iter().map(|a| a.as_ref()).collect(); + columns.push(arrow::compute::concat(&refs)?); + } + + for col_idx in 0..num_right_cols { + let values: Vec = matches + .iter() + .map(|&(_, _, rb, rr)| { + right_batches[rb].column(col_idx).slice(rr, 1) + }) + .collect(); + + let refs: Vec<&dyn Array> = + values.iter().map(|a| a.as_ref()).collect(); + columns.push(arrow::compute::concat(&refs)?); + } + + Ok(RecordBatch::try_new(schema.clone(), columns)?) +} + +/// Collect all batches from a stream into a Vec. +async fn collect_batches( + stream: SendableRecordBatchStream, +) -> Result> { + use futures::StreamExt; + + let mut batches = Vec::new(); + let mut stream = stream; + + while let Some(batch) = stream.next().await { + batches.push(batch?); + } + + Ok(batches) +} diff --git a/crates/giql-datafusion/src/lib.rs b/crates/giql-datafusion/src/lib.rs new file mode 100644 index 0000000..efdde1b --- /dev/null +++ b/crates/giql-datafusion/src/lib.rs @@ -0,0 +1,79 @@ +//! DataFusion optimizer for genomic interval (INTERSECTS) joins. +//! +//! This crate provides a [`PhysicalOptimizerRule`] that reads Parquet +//! metadata and lightweight sampling to choose between sweep-line and +//! binned equi-join algorithms for interval overlap joins. +//! +//! # Usage +//! +//! ```rust,no_run +//! use datafusion::execution::SessionStateBuilder; +//! use datafusion::prelude::*; +//! use giql_datafusion::{IntersectsOptimizerConfig, register_optimizer}; +//! +//! let config = IntersectsOptimizerConfig::default(); +//! let state = SessionStateBuilder::new() +//! .with_default_features() +//! .build(); +//! let state = register_optimizer(state, config); +//! let ctx = SessionContext::from(state); +//! ``` + +pub mod cost; +pub mod exec; +pub mod optimizer; +pub mod pattern; +pub mod pruning; +pub mod stats; + +pub use cost::JoinStrategy; +pub use optimizer::IntersectsOptimizerRule; + +use datafusion::execution::SessionState; +use datafusion::physical_optimizer::PhysicalOptimizerRule; +use std::sync::Arc; + +/// Configuration for the INTERSECTS join optimizer. +#[derive(Debug, Clone)] +pub struct IntersectsOptimizerConfig { + /// Threshold for p99/median width ratio. Above this, sweep line is + /// chosen to avoid binning replication blowup on wide intervals. + pub p99_median_threshold: f64, + + /// Threshold for coefficient of variation. Above this, sweep line + /// is chosen because no single bin size works well. + pub cv_threshold: f64, + + /// Maximum number of row groups to sample for width distribution. + pub max_sample_row_groups: usize, +} + +impl Default for IntersectsOptimizerConfig { + fn default() -> Self { + Self { + p99_median_threshold: 10.0, + cv_threshold: 1.5, + max_sample_row_groups: 3, + } + } +} + +/// Build a [`SessionState`] with the INTERSECTS optimizer rule +/// appended to the default physical optimizer rules. +pub fn register_optimizer( + state: SessionState, + config: IntersectsOptimizerConfig, +) -> SessionState { + use datafusion::execution::SessionStateBuilder; + + let rule: Arc = + Arc::new(IntersectsOptimizerRule::new(config)); + + let mut rules: Vec> = + state.physical_optimizers().to_vec(); + rules.push(rule); + + SessionStateBuilder::new_from_existing(state) + .with_physical_optimizer_rules(rules) + .build() +} diff --git a/crates/giql-datafusion/src/optimizer.rs b/crates/giql-datafusion/src/optimizer.rs new file mode 100644 index 0000000..6aa9cd3 --- /dev/null +++ b/crates/giql-datafusion/src/optimizer.rs @@ -0,0 +1,172 @@ +use std::sync::Arc; + +use datafusion::common::Result; +use datafusion::config::ConfigOptions; +use datafusion::physical_optimizer::PhysicalOptimizerRule; +use datafusion::physical_plan::ExecutionPlan; + +use crate::cost::{CostModel, JoinStrategy}; +use crate::exec::{BinnedJoinExec, SweepLineJoinExec}; +use crate::pattern::{detect_interval_join, IntervalJoinMatch}; +use crate::stats; +use crate::IntersectsOptimizerConfig; + +/// Physical optimizer rule that detects interval overlap joins and +/// replaces them with optimized execution plans. +/// +/// The rule reads Parquet metadata and performs lightweight sampling to +/// choose between sweep-line and binned equi-join algorithms based on +/// the interval width distribution. +#[derive(Debug)] +pub struct IntersectsOptimizerRule { + config: IntersectsOptimizerConfig, +} + +impl IntersectsOptimizerRule { + pub fn new(config: IntersectsOptimizerConfig) -> Self { + Self { config } + } + + /// Recursively optimize a plan tree, replacing interval overlap + /// joins with custom execution plans. + fn optimize_plan( + &self, + plan: Arc, + ) -> Result> { + // First, try to match this node + if let Some(join_match) = detect_interval_join(&plan)? { + return self.replace_join(join_match); + } + + // Recurse into children + let children: Vec> = plan + .children() + .into_iter() + .map(|child| self.optimize_plan(child.clone())) + .collect::>>()?; + + if children.is_empty() { + return Ok(plan); + } + + plan.with_new_children(children) + } + + /// Replace an interval overlap join with an optimized execution + /// plan based on cost model analysis. + fn replace_join( + &self, + join_match: IntervalJoinMatch, + ) -> Result> { + let cost_model = CostModel::new(&self.config); + + // Collect statistics from Parquet sources + let left_stats = self.collect_stats( + &join_match.left_parquet_paths, + &join_match.left_cols.start_col, + &join_match.left_cols.end_col, + ); + let right_stats = self.collect_stats( + &join_match.right_parquet_paths, + &join_match.right_cols.start_col, + &join_match.right_cols.end_col, + ); + + // Decide on strategy + let strategy = match (&left_stats, &right_stats) { + (Some(left), Some(right)) => cost_model.decide(left, right), + _ => { + log::info!( + "INTERSECTS optimizer: no Parquet stats available, \ + using default sweep-line" + ); + JoinStrategy::SweepLine { skip_sort: false } + } + }; + + log::info!("INTERSECTS optimizer: selected {strategy:?}"); + + match strategy { + JoinStrategy::NestedLoop => { + // Return original plan unchanged — reconstruct from + // the match components. This shouldn't normally happen + // since we default to SweepLine, but handle it. + Err(datafusion::error::DataFusionError::Internal( + "NestedLoop strategy should not be returned by \ + cost model when Parquet stats are unavailable" + .to_string(), + )) + } + JoinStrategy::SweepLine { skip_sort } => { + Ok(Arc::new(SweepLineJoinExec::new( + join_match.left, + join_match.right, + join_match.left_cols, + join_match.right_cols, + join_match.output_schema, + skip_sort, + ))) + } + JoinStrategy::BinnedJoin { bin_size } => { + Ok(Arc::new(BinnedJoinExec::new( + join_match.left, + join_match.right, + join_match.left_cols, + join_match.right_cols, + join_match.output_schema, + bin_size, + ))) + } + } + } + + /// Collect statistics from the first available Parquet file. + fn collect_stats( + &self, + paths: &[std::path::PathBuf], + start_col: &str, + end_col: &str, + ) -> Option { + if paths.is_empty() { + return None; + } + + // Use the first file for statistics. For multi-file tables, + // a more sophisticated approach would aggregate stats across + // files. + let path = &paths[0]; + match stats::collect_parquet_stats( + path, + start_col, + end_col, + self.config.max_sample_row_groups, + ) { + Ok(stats) => Some(stats), + Err(e) => { + log::warn!( + "INTERSECTS optimizer: failed to collect stats \ + from {path:?}: {e}" + ); + None + } + } + } +} + +impl PhysicalOptimizerRule for IntersectsOptimizerRule { + fn optimize( + &self, + plan: Arc, + _config: &ConfigOptions, + ) -> Result> { + self.optimize_plan(plan) + } + + fn name(&self) -> &str { + "intersects_optimizer" + } + + fn schema_check(&self) -> bool { + true + } +} diff --git a/crates/giql-datafusion/src/pattern.rs b/crates/giql-datafusion/src/pattern.rs new file mode 100644 index 0000000..60b5594 --- /dev/null +++ b/crates/giql-datafusion/src/pattern.rs @@ -0,0 +1,420 @@ +use std::path::PathBuf; +use std::sync::Arc; + +use arrow::datatypes::SchemaRef; +use datafusion::common::{JoinSide, Result}; +use datafusion::physical_plan::joins::utils::ColumnIndex; +use datafusion::physical_plan::ExecutionPlan; + +/// Column indices for the genomic interval columns on one side of a +/// join. +#[derive(Debug, Clone)] +pub struct IntervalColumns { + /// Column name for chromosome. + pub chrom_col: String, + /// Column name for interval start. + pub start_col: String, + /// Column name for interval end. + pub end_col: String, + /// Column index for chromosome in the side's schema. + pub chrom_idx: usize, + /// Column index for start in the side's schema. + pub start_idx: usize, + /// Column index for end in the side's schema. + pub end_idx: usize, +} + +/// A detected interval overlap join pattern in the physical plan. +#[derive(Debug)] +pub struct IntervalJoinMatch { + /// The left input execution plan. + pub left: Arc, + /// The right input execution plan. + pub right: Arc, + /// Interval column indices on the left side. + pub left_cols: IntervalColumns, + /// Interval column indices on the right side. + pub right_cols: IntervalColumns, + /// Output schema of the join node being replaced. + pub output_schema: SchemaRef, + /// Parquet file paths from the left source, if available. + pub left_parquet_paths: Vec, + /// Parquet file paths from the right source, if available. + pub right_parquet_paths: Vec, +} + +/// Attempt to detect an interval overlap join in the given execution +/// plan node. +/// +/// Looks for join nodes (hash join, nested loop join) with predicates +/// matching the pattern: +/// +/// ```text +/// left.chrom = right.chrom +/// AND left.start < right.end +/// AND left.end > right.start +/// ``` +pub fn detect_interval_join( + plan: &Arc, +) -> Result> { + let plan_name = plan.name(); + + match plan_name { + "NestedLoopJoinExec" => detect_from_nested_loop_join(plan), + "HashJoinExec" => detect_from_hash_join(plan), + _ => Ok(None), + } +} + +/// Detect interval overlap in a NestedLoopJoinExec. +fn detect_from_nested_loop_join( + _plan: &Arc, +) -> Result> { + // NestedLoopJoinExec detection requires walking the full filter + // expression tree to find all three predicates (chrom equality + + // range overlap). This is deferred to a future iteration — the + // HashJoinExec path handles the common case where DataFusion + // separates the equi-key (chrom) from the range filter. + Ok(None) +} + +/// Detect interval overlap in a HashJoinExec. +fn detect_from_hash_join( + plan: &Arc, +) -> Result> { + use datafusion::physical_plan::joins::HashJoinExec; + + let hj = match plan.as_any().downcast_ref::() { + Some(hj) => hj, + None => return Ok(None), + }; + + let filter = match hj.filter() { + Some(f) => f, + None => return Ok(None), + }; + + let left = hj.left().clone(); + let right = hj.right().clone(); + let left_schema = left.schema(); + let right_schema = right.schema(); + + // Extract the equi-join key — should be a chromosome column + let on = hj.on(); + if on.is_empty() { + return Ok(None); + } + + use datafusion::physical_expr::expressions::Column; + + let (left_chrom_key, right_chrom_key) = &on[0]; + let left_chrom_col = + match left_chrom_key.as_any().downcast_ref::() { + Some(c) => c, + None => return Ok(None), + }; + let right_chrom_col = + match right_chrom_key.as_any().downcast_ref::() { + Some(c) => c, + None => return Ok(None), + }; + let left_chrom_name = left_chrom_col.name().to_string(); + let right_chrom_name = right_chrom_col.name().to_string(); + + let left_chrom_idx = match left_schema + .fields() + .iter() + .position(|f| f.name() == &left_chrom_name) + { + Some(idx) => idx, + None => return Ok(None), + }; + let right_chrom_idx = match right_schema + .fields() + .iter() + .position(|f| f.name() == &right_chrom_name) + { + Some(idx) => idx, + None => return Ok(None), + }; + + // Extract start/end columns from the filter expression + let filter_expr = filter.expression(); + let column_indices = filter.column_indices(); + let range_cols = match extract_range_columns_from_filter( + filter_expr, + column_indices, + &left_schema, + &right_schema, + ) { + Some(cols) => cols, + None => return Ok(None), + }; + + let left_start_idx = match left_schema + .fields() + .iter() + .position(|f| f.name() == &range_cols.0) + { + Some(idx) => idx, + None => return Ok(None), + }; + let left_end_idx = match left_schema + .fields() + .iter() + .position(|f| f.name() == &range_cols.1) + { + Some(idx) => idx, + None => return Ok(None), + }; + let right_start_idx = match right_schema + .fields() + .iter() + .position(|f| f.name() == &range_cols.2) + { + Some(idx) => idx, + None => return Ok(None), + }; + let right_end_idx = match right_schema + .fields() + .iter() + .position(|f| f.name() == &range_cols.3) + { + Some(idx) => idx, + None => return Ok(None), + }; + + let left_cols = IntervalColumns { + chrom_col: left_chrom_name, + start_col: range_cols.0, + end_col: range_cols.1, + chrom_idx: left_chrom_idx, + start_idx: left_start_idx, + end_idx: left_end_idx, + }; + + let right_cols = IntervalColumns { + chrom_col: right_chrom_name, + start_col: range_cols.2, + end_col: range_cols.3, + chrom_idx: right_chrom_idx, + start_idx: right_start_idx, + end_idx: right_end_idx, + }; + + let left_parquet_paths = find_parquet_paths(&left); + let right_parquet_paths = find_parquet_paths(&right); + + Ok(Some(IntervalJoinMatch { + left, + right, + left_cols, + right_cols, + output_schema: plan.schema(), + left_parquet_paths, + right_parquet_paths, + })) +} + +/// Extract range column names from a HashJoin filter expression. +/// +/// The filter should contain `left.start < right.end AND left.end > +/// right.start`. Returns `Some((left_start, left_end, right_start, +/// right_end))` column names if the pattern matches. +fn extract_range_columns_from_filter( + expr: &Arc, + column_indices: &[ColumnIndex], + left_schema: &SchemaRef, + right_schema: &SchemaRef, +) -> Option<(String, String, String, String)> { + use datafusion::logical_expr::Operator; + use datafusion::physical_expr::expressions::BinaryExpr; + + let binary = expr.as_any().downcast_ref::()?; + + if *binary.op() != Operator::And { + return None; + } + + let left_pred = binary.left(); + let right_pred = binary.right(); + + // Try both orderings of the two predicates + + + try_extract_range_pair( + left_pred, + right_pred, + column_indices, + left_schema, + right_schema, + ) + .or_else(|| { + try_extract_range_pair( + right_pred, + left_pred, + column_indices, + left_schema, + right_schema, + ) + }) +} + +/// Try to extract (left_start, left_end, right_start, right_end) from +/// a pair of predicates where one is Lt and one is Gt. +fn try_extract_range_pair( + pred_a: &Arc, + pred_b: &Arc, + column_indices: &[ColumnIndex], + left_schema: &SchemaRef, + right_schema: &SchemaRef, +) -> Option<(String, String, String, String)> { + let (lt_left, lt_right) = extract_lt_columns( + pred_a, + column_indices, + left_schema, + right_schema, + )?; + let (gt_left, gt_right) = extract_gt_columns( + pred_b, + column_indices, + left_schema, + right_schema, + )?; + + // lt pattern: left.start < right.end + // gt pattern: left.end > right.start + Some((lt_left, gt_left, gt_right, lt_right)) +} + +/// Extract columns from a `<` comparison. +fn extract_lt_columns( + expr: &Arc, + column_indices: &[ColumnIndex], + left_schema: &SchemaRef, + right_schema: &SchemaRef, +) -> Option<(String, String)> { + use datafusion::logical_expr::Operator; + use datafusion::physical_expr::expressions::{BinaryExpr, Column}; + + let binary = expr.as_any().downcast_ref::()?; + if *binary.op() != Operator::Lt { + return None; + } + + let left_col = binary.left().as_any().downcast_ref::()?; + let right_col = + binary.right().as_any().downcast_ref::()?; + + let left_name = resolve_column_name( + left_col.index(), + column_indices, + left_schema, + right_schema, + true, + )?; + let right_name = resolve_column_name( + right_col.index(), + column_indices, + left_schema, + right_schema, + false, + )?; + + Some((left_name, right_name)) +} + +/// Extract columns from a `>` comparison. +fn extract_gt_columns( + expr: &Arc, + column_indices: &[ColumnIndex], + left_schema: &SchemaRef, + right_schema: &SchemaRef, +) -> Option<(String, String)> { + use datafusion::logical_expr::Operator; + use datafusion::physical_expr::expressions::{BinaryExpr, Column}; + + let binary = expr.as_any().downcast_ref::()?; + if *binary.op() != Operator::Gt { + return None; + } + + let left_col = binary.left().as_any().downcast_ref::()?; + let right_col = + binary.right().as_any().downcast_ref::()?; + + let left_name = resolve_column_name( + left_col.index(), + column_indices, + left_schema, + right_schema, + true, + )?; + let right_name = resolve_column_name( + right_col.index(), + column_indices, + left_schema, + right_schema, + false, + )?; + + Some((left_name, right_name)) +} + +/// Resolve a filter-local column index to a column name. +fn resolve_column_name( + filter_idx: usize, + column_indices: &[ColumnIndex], + left_schema: &SchemaRef, + right_schema: &SchemaRef, + expect_left: bool, +) -> Option { + if filter_idx >= column_indices.len() { + return None; + } + + let col_idx = &column_indices[filter_idx]; + let is_left = matches!(col_idx.side, JoinSide::Left); + + if is_left != expect_left { + return None; + } + + let schema = if is_left { left_schema } else { right_schema }; + if col_idx.index >= schema.fields().len() { + return None; + } + let field = schema.field(col_idx.index); + Some(field.name().clone()) +} + +/// Recursively find Parquet file paths in the plan tree. +fn find_parquet_paths(plan: &Arc) -> Vec { + use datafusion::datasource::source::DataSourceExec; + use datafusion::datasource::physical_plan::parquet::source::ParquetSource; + + let mut paths = Vec::new(); + + if let Some(ds_exec) = + plan.as_any().downcast_ref::() + { + if let Some((file_config, _parquet_source)) = + ds_exec.downcast_to_file_source::() + { + for group in &file_config.file_groups { + for file in group.iter() { + paths.push(PathBuf::from( + file.object_meta.location.as_ref(), + )); + } + } + return paths; + } + } + + for child in plan.children() { + paths.extend(find_parquet_paths(child)); + } + + paths +} diff --git a/crates/giql-datafusion/src/pruning.rs b/crates/giql-datafusion/src/pruning.rs new file mode 100644 index 0000000..d2ab564 --- /dev/null +++ b/crates/giql-datafusion/src/pruning.rs @@ -0,0 +1,140 @@ +use crate::stats::IntervalStats; + +/// Domain bounds for one side of the join, derived from row group +/// metadata. +#[derive(Debug, Clone)] +pub struct DomainBounds { + /// Global minimum start position across all row groups. + pub min_start: i64, + /// Global maximum end position across all row groups. + pub max_end: i64, +} + +impl From<&IntervalStats> for DomainBounds { + fn from(stats: &IntervalStats) -> Self { + Self { + min_start: stats.domain_min, + max_end: stats.domain_max, + } + } +} + +/// Generate pruning predicates from domain bounds. +/// +/// These predicates are always true for the join result set but help +/// the database engine skip row groups that are entirely outside the +/// other side's range. They should be injected as additional filter +/// predicates on the `ParquetExec` nodes before the join. +/// +/// Returns predicate expressions as `(column_name, op, literal_value)` +/// tuples that can be converted to DataFusion `PhysicalExpr` nodes. +pub fn generate_pruning_predicates( + left_bounds: &DomainBounds, + right_bounds: &DomainBounds, +) -> Vec { + vec![ + // Left rows that start after right's max end cannot overlap + PruningPredicate { + side: JoinSide::Left, + column: PruningColumn::Start, + op: PruningOp::Lt, + value: right_bounds.max_end, + }, + // Left rows that end before right's min start cannot overlap + PruningPredicate { + side: JoinSide::Left, + column: PruningColumn::End, + op: PruningOp::Gt, + value: right_bounds.min_start, + }, + // Right rows that start after left's max end cannot overlap + PruningPredicate { + side: JoinSide::Right, + column: PruningColumn::Start, + op: PruningOp::Lt, + value: left_bounds.max_end, + }, + // Right rows that end before left's min start cannot overlap + PruningPredicate { + side: JoinSide::Right, + column: PruningColumn::End, + op: PruningOp::Gt, + value: left_bounds.min_start, + }, + ] +} + +/// A pruning predicate to inject on a ParquetExec node. +#[derive(Debug, Clone)] +pub struct PruningPredicate { + /// Which side of the join this predicate applies to. + pub side: JoinSide, + /// Which interval column to filter. + pub column: PruningColumn, + /// Comparison operator. + pub op: PruningOp, + /// Literal value to compare against. + pub value: i64, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum JoinSide { + Left, + Right, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum PruningColumn { + Start, + End, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum PruningOp { + Lt, + Gt, +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_generate_pruning_predicates() { + let left = DomainBounds { + min_start: 1000, + max_end: 50000, + }; + let right = DomainBounds { + min_start: 2000, + max_end: 60000, + }; + + let preds = generate_pruning_predicates(&left, &right); + assert_eq!(preds.len(), 4); + + // Left start < 60000 (right max_end) + assert_eq!(preds[0].side, JoinSide::Left); + assert_eq!(preds[0].column, PruningColumn::Start); + assert_eq!(preds[0].op, PruningOp::Lt); + assert_eq!(preds[0].value, 60000); + + // Left end > 2000 (right min_start) + assert_eq!(preds[1].side, JoinSide::Left); + assert_eq!(preds[1].column, PruningColumn::End); + assert_eq!(preds[1].op, PruningOp::Gt); + assert_eq!(preds[1].value, 2000); + + // Right start < 50000 (left max_end) + assert_eq!(preds[2].side, JoinSide::Right); + assert_eq!(preds[2].column, PruningColumn::Start); + assert_eq!(preds[2].op, PruningOp::Lt); + assert_eq!(preds[2].value, 50000); + + // Right end > 1000 (left min_start) + assert_eq!(preds[3].side, JoinSide::Right); + assert_eq!(preds[3].column, PruningColumn::End); + assert_eq!(preds[3].op, PruningOp::Gt); + assert_eq!(preds[3].value, 1000); + } +} diff --git a/crates/giql-datafusion/src/stats/metadata.rs b/crates/giql-datafusion/src/stats/metadata.rs new file mode 100644 index 0000000..bd2415e --- /dev/null +++ b/crates/giql-datafusion/src/stats/metadata.rs @@ -0,0 +1,273 @@ +use std::fs::File; +use std::path::Path; + +use datafusion::common::Result; +use parquet::file::reader::FileReader; +use parquet::file::serialized_reader::SerializedFileReader; +use parquet::file::statistics::Statistics; + +use super::types::RowGroupBounds; + +/// Statistics extracted from Parquet file footer metadata only (no data +/// pages read). This is effectively free — it reads the file footer +/// which is already cached by the OS after open. +#[derive(Debug, Clone)] +pub struct MetadataStats { + /// Per-row-group bounds for start/end columns. + pub row_group_bounds: Vec, + /// Total row count across all row groups. + pub total_rows: usize, + /// Global minimum start position. + pub domain_min: i64, + /// Global maximum end position. + pub domain_max: i64, + /// Whether the file declares itself sorted by the start column. + pub is_sorted_by_start: bool, + /// Whether page-level column index is present. + pub has_page_index: bool, +} + +/// Collect metadata-tier statistics from a Parquet file footer. +/// +/// Reads only the file footer — no data pages are touched. Returns +/// per-row-group column statistics for the start and end columns, plus +/// file-level properties like sort order and page index presence. +pub fn collect_metadata( + path: &Path, + start_col: &str, + end_col: &str, +) -> Result { + let file = File::open(path).map_err(|e| { + datafusion::error::DataFusionError::External(Box::new(e)) + })?; + let reader = SerializedFileReader::new(file).map_err(|e| { + datafusion::error::DataFusionError::External(Box::new(e)) + })?; + + let parquet_metadata = reader.metadata(); + let file_metadata = parquet_metadata.file_metadata(); + let schema = file_metadata.schema_descr(); + + // Find column indices for start and end + let start_idx = find_column_index(schema, start_col)?; + let end_idx = find_column_index(schema, end_col)?; + + // Check sort order from file metadata + let is_sorted_by_start = check_sort_order(file_metadata, start_col, schema); + + let num_row_groups = parquet_metadata.num_row_groups(); + let mut row_group_bounds = Vec::with_capacity(num_row_groups); + let mut total_rows: usize = 0; + let mut global_min_start = i64::MAX; + let mut global_max_end = i64::MIN; + let mut has_page_index = false; + + for rg_idx in 0..num_row_groups { + let rg_metadata = parquet_metadata.row_group(rg_idx); + let row_count = rg_metadata.num_rows() as usize; + total_rows += row_count; + + let start_col_meta = rg_metadata.column(start_idx); + let end_col_meta = rg_metadata.column(end_idx); + + // Check page index on first row group + if rg_idx == 0 { + has_page_index = start_col_meta.offset_index_offset().is_some() + || start_col_meta.column_index_offset().is_some(); + } + + let (min_start, max_start) = extract_i64_min_max( + start_col_meta.statistics(), + start_col, + )?; + let (min_end, max_end) = extract_i64_min_max( + end_col_meta.statistics(), + end_col, + )?; + + if min_start < global_min_start { + global_min_start = min_start; + } + if max_end > global_max_end { + global_max_end = max_end; + } + + row_group_bounds.push(RowGroupBounds { + min_start, + max_start, + min_end, + max_end, + row_count, + }); + } + + Ok(MetadataStats { + row_group_bounds, + total_rows, + domain_min: global_min_start, + domain_max: global_max_end, + is_sorted_by_start, + has_page_index, + }) +} + +/// Find the column index for a named column in the Parquet schema. +fn find_column_index( + schema: &parquet::schema::types::SchemaDescriptor, + col_name: &str, +) -> Result { + for i in 0..schema.num_columns() { + if schema.column(i).name() == col_name { + return Ok(i); + } + } + Err(datafusion::error::DataFusionError::Plan(format!( + "Column '{col_name}' not found in Parquet schema" + ))) +} + +/// Check whether the file declares itself sorted by the start column. +fn check_sort_order( + file_metadata: &parquet::file::metadata::FileMetaData, + start_col: &str, + schema: &parquet::schema::types::SchemaDescriptor, +) -> bool { + // Check key-value metadata for sorting_columns + if let Some(kv_metadata) = file_metadata.key_value_metadata() { + for kv in kv_metadata { + if kv.key == "sorting_columns" || kv.key == "pandas.sort_columns" { + if let Some(ref value) = kv.value { + if value.contains(start_col) { + return true; + } + } + } + } + } + + // Check if column order metadata indicates ascending on start column + if let Some(sort_order) = file_metadata.column_orders() { + if let Ok(start_idx) = find_column_index(schema, start_col) { + if start_idx < sort_order.len() { + // TypeDefinedOrder means the natural ordering applies, + // which combined with sorted row groups suggests sorted data. + // However, this only indicates comparison semantics, not + // that data is actually sorted. We need sorting_columns + // metadata for a definitive answer. Return false here. + } + } + } + + false +} + +/// Extract min and max i64 values from Parquet column statistics. +fn extract_i64_min_max( + statistics: Option<&Statistics>, + col_name: &str, +) -> Result<(i64, i64)> { + match statistics { + Some(Statistics::Int32(stats)) => { + let min_val = stats.min_opt().ok_or_else(|| { + datafusion::error::DataFusionError::Plan(format!( + "Column '{col_name}' Int32 stats missing min" + )) + })?; + let max_val = stats.max_opt().ok_or_else(|| { + datafusion::error::DataFusionError::Plan(format!( + "Column '{col_name}' Int32 stats missing max" + )) + })?; + Ok((*min_val as i64, *max_val as i64)) + } + Some(Statistics::Int64(stats)) => { + let min_val = stats.min_opt().ok_or_else(|| { + datafusion::error::DataFusionError::Plan(format!( + "Column '{col_name}' Int64 stats missing min" + )) + })?; + let max_val = stats.max_opt().ok_or_else(|| { + datafusion::error::DataFusionError::Plan(format!( + "Column '{col_name}' Int64 stats missing max" + )) + })?; + Ok((*min_val, *max_val)) + } + Some(_) => Err(datafusion::error::DataFusionError::Plan(format!( + "Column '{col_name}' has unsupported statistics type for interval bounds" + ))), + None => Err(datafusion::error::DataFusionError::Plan(format!( + "Column '{col_name}' has no statistics in Parquet metadata" + ))), + } +} + +#[cfg(test)] +mod tests { + use super::*; + use arrow::array::Int64Array; + use arrow::datatypes::{DataType, Field, Schema}; + use arrow::record_batch::RecordBatch; + use parquet::arrow::ArrowWriter; + use std::sync::Arc; + use tempfile::NamedTempFile; + + fn write_test_parquet( + starts: &[i64], + ends: &[i64], + ) -> NamedTempFile { + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + let chroms: Vec<&str> = vec!["chr1"; starts.len()]; + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(arrow::array::StringArray::from(chroms)), + Arc::new(Int64Array::from(starts.to_vec())), + Arc::new(Int64Array::from(ends.to_vec())), + ], + ) + .unwrap(); + + let file = NamedTempFile::new().unwrap(); + let mut writer = + ArrowWriter::try_new(file.reopen().unwrap(), schema, None) + .unwrap(); + writer.write(&batch).unwrap(); + writer.close().unwrap(); + file + } + + #[test] + fn test_collect_metadata_basic() { + let starts = vec![100, 200, 300, 400, 500]; + let ends = vec![150, 280, 350, 450, 600]; + let file = write_test_parquet(&starts, &ends); + + let stats = + collect_metadata(file.path(), "start", "end").unwrap(); + + assert_eq!(stats.total_rows, 5); + assert_eq!(stats.domain_min, 100); + assert_eq!(stats.domain_max, 600); + assert_eq!(stats.row_group_bounds.len(), 1); + + let rg = &stats.row_group_bounds[0]; + assert_eq!(rg.min_start, 100); + assert_eq!(rg.max_start, 500); + assert_eq!(rg.min_end, 150); + assert_eq!(rg.max_end, 600); + assert_eq!(rg.row_count, 5); + } + + #[test] + fn test_collect_metadata_missing_column() { + let file = write_test_parquet(&[100], &[200]); + let result = + collect_metadata(file.path(), "nonexistent", "end"); + assert!(result.is_err()); + } +} diff --git a/crates/giql-datafusion/src/stats/mod.rs b/crates/giql-datafusion/src/stats/mod.rs new file mode 100644 index 0000000..a49a756 --- /dev/null +++ b/crates/giql-datafusion/src/stats/mod.rs @@ -0,0 +1,94 @@ +pub mod metadata; +pub mod sampler; +pub mod types; + +pub use metadata::{collect_metadata, MetadataStats}; +pub use sampler::sample_widths; +pub use types::{IntervalStats, RowGroupBounds, WidthStats}; + +use datafusion::common::Result; +use std::path::Path; + +/// Collect full interval statistics from a Parquet file by combining +/// metadata (tier 1, free) with lightweight sampling (tier 2, +/// milliseconds). +/// +/// Returns `None` if the file cannot be read or lacks the required +/// columns. +pub fn collect_parquet_stats( + path: &Path, + start_col: &str, + end_col: &str, + max_sample_row_groups: usize, +) -> Result { + // Tier 1: file footer metadata + let meta = collect_metadata(path, start_col, end_col)?; + + // Select representative row groups for sampling + let sample_indices = + select_representative_row_groups(meta.row_group_bounds.len(), max_sample_row_groups); + + // Tier 2: lightweight sampling + let width = sample_widths(path, start_col, end_col, &sample_indices)?; + + Ok(IntervalStats { + row_count: meta.total_rows, + domain_min: meta.domain_min, + domain_max: meta.domain_max, + is_sorted_by_start: meta.is_sorted_by_start, + row_group_bounds: meta.row_group_bounds, + width, + }) +} + +/// Pick representative row groups for sampling: first, middle, last. +fn select_representative_row_groups( + num_row_groups: usize, + max_sample: usize, +) -> Vec { + if num_row_groups == 0 { + return vec![]; + } + if num_row_groups == 1 || max_sample == 1 { + return vec![0]; + } + + let last = num_row_groups - 1; + if num_row_groups == 2 || max_sample == 2 { + return vec![0, last]; + } + + // First, middle, last + let mid = num_row_groups / 2; + let mut indices = vec![0, mid, last]; + indices.truncate(max_sample); + indices +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_select_representative_single() { + assert_eq!(select_representative_row_groups(1, 3), vec![0]); + } + + #[test] + fn test_select_representative_two() { + assert_eq!(select_representative_row_groups(2, 3), vec![0, 1]); + } + + #[test] + fn test_select_representative_many() { + assert_eq!( + select_representative_row_groups(10, 3), + vec![0, 5, 9] + ); + } + + #[test] + fn test_select_representative_max_one() { + assert_eq!(select_representative_row_groups(10, 1), vec![0]); + } +} diff --git a/crates/giql-datafusion/src/stats/sampler.rs b/crates/giql-datafusion/src/stats/sampler.rs new file mode 100644 index 0000000..f90c7e3 --- /dev/null +++ b/crates/giql-datafusion/src/stats/sampler.rs @@ -0,0 +1,217 @@ +use std::fs::File; +use std::path::Path; + +use arrow::array::{Array, Int64Array}; +use datafusion::common::Result; +use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder; +use parquet::arrow::ProjectionMask; + +use super::types::WidthStats; + +/// Read start and end columns from selected row groups and compute +/// width distribution statistics. +/// +/// Only two columns are read from each row group — no other data is +/// touched. For a multi-GB dataset this typically completes in +/// milliseconds. +pub fn sample_widths( + path: &Path, + start_col: &str, + end_col: &str, + row_group_indices: &[usize], +) -> Result { + if row_group_indices.is_empty() { + return Err(datafusion::error::DataFusionError::Plan( + "No row groups to sample".to_string(), + )); + } + + let file = File::open(path).map_err(|e| { + datafusion::error::DataFusionError::External(Box::new(e)) + })?; + + let builder = + ParquetRecordBatchReaderBuilder::try_new(file).map_err(|e| { + datafusion::error::DataFusionError::External(Box::new(e)) + })?; + + // Find column indices in the Arrow schema + let arrow_schema = builder.schema().clone(); + let start_idx = arrow_schema + .fields() + .iter() + .position(|f| f.name() == start_col) + .ok_or_else(|| { + datafusion::error::DataFusionError::Plan(format!( + "Column '{start_col}' not found in Parquet schema" + )) + })?; + let end_idx = arrow_schema + .fields() + .iter() + .position(|f| f.name() == end_col) + .ok_or_else(|| { + datafusion::error::DataFusionError::Plan(format!( + "Column '{end_col}' not found in Parquet schema" + )) + })?; + + // Project only the start and end columns using the Parquet schema + let parquet_schema = builder.parquet_schema(); + let projection = + ProjectionMask::leaves(parquet_schema, vec![start_idx, end_idx]); + + let reader = builder + .with_projection(projection) + .with_row_groups(row_group_indices.to_vec()) + .build() + .map_err(|e| { + datafusion::error::DataFusionError::External(Box::new(e)) + })?; + + let mut widths: Vec = Vec::new(); + + for batch_result in reader { + let batch = batch_result.map_err(|e| { + datafusion::error::DataFusionError::External(Box::new(e)) + })?; + + // Columns are projected, so index 0 = start, 1 = end + let starts = extract_i64_column(&batch, 0, start_col)?; + let ends = extract_i64_column(&batch, 1, end_col)?; + + for i in 0..batch.num_rows() { + if !starts.is_null(i) && !ends.is_null(i) { + let w = (ends.value(i) - starts.value(i)) as f64; + widths.push(w); + } + } + } + + if widths.is_empty() { + return Err(datafusion::error::DataFusionError::Plan( + "No valid intervals found in sampled row groups".to_string(), + )); + } + + Ok(compute_width_stats(&mut widths)) +} + +/// Compute width distribution statistics from a vector of widths. +pub(crate) fn compute_width_stats(widths: &mut [f64]) -> WidthStats { + widths.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); + + let n = widths.len(); + let median = percentile_sorted(widths, 0.5); + let p95 = percentile_sorted(widths, 0.95); + let p99 = percentile_sorted(widths, 0.99); + + let sum: f64 = widths.iter().sum(); + let mean = sum / n as f64; + + let variance: f64 = + widths.iter().map(|w| (w - mean).powi(2)).sum::() / n as f64; + let std_dev = variance.sqrt(); + let cv = if mean > 0.0 { std_dev / mean } else { 0.0 }; + + let p99_median_ratio = if median > 0.0 { p99 / median } else { 0.0 }; + + WidthStats { + median, + mean, + p95, + p99, + cv, + p99_median_ratio, + } +} + +/// Compute a percentile from a sorted slice using linear interpolation. +fn percentile_sorted(sorted: &[f64], p: f64) -> f64 { + if sorted.is_empty() { + return 0.0; + } + if sorted.len() == 1 { + return sorted[0]; + } + + let rank = p * (sorted.len() - 1) as f64; + let lower = rank.floor() as usize; + let upper = rank.ceil() as usize; + let frac = rank - lower as f64; + + if lower == upper { + sorted[lower] + } else { + sorted[lower] * (1.0 - frac) + sorted[upper] * frac + } +} + +/// Extract an i64 column from a record batch, handling both Int32 and +/// Int64 physical types. +fn extract_i64_column( + batch: &arrow::record_batch::RecordBatch, + col_idx: usize, + col_name: &str, +) -> Result { + let col = batch.column(col_idx); + + if let Some(arr) = col.as_any().downcast_ref::() { + return Ok(arr.clone()); + } + + if let Some(arr) = + col.as_any().downcast_ref::() + { + let converted: Int64Array = arr + .iter() + .map(|v| v.map(|x| x as i64)) + .collect(); + return Ok(converted); + } + + Err(datafusion::error::DataFusionError::Plan(format!( + "Column '{col_name}' is not Int32 or Int64" + ))) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_compute_width_stats_uniform() { + let mut widths = vec![100.0; 1000]; + let stats = compute_width_stats(&mut widths); + + assert!((stats.median - 100.0).abs() < 1e-6); + assert!((stats.mean - 100.0).abs() < 1e-6); + assert!((stats.cv).abs() < 1e-6); + assert!((stats.p99_median_ratio - 1.0).abs() < 1e-6); + } + + #[test] + fn test_compute_width_stats_heavy_tailed() { + // 950 intervals of width 100, 50 intervals of width 10000. + // With 5% outliers, p99 lands squarely in the 10000 range. + let mut widths: Vec = vec![100.0; 950]; + widths.extend(vec![10000.0; 50]); + let stats = compute_width_stats(&mut widths); + + assert!(stats.p99_median_ratio > 10.0); + assert!(stats.cv > 1.0); + } + + #[test] + fn test_percentile_sorted() { + let data = vec![1.0, 2.0, 3.0, 4.0, 5.0]; + assert!((percentile_sorted(&data, 0.0) - 1.0).abs() < 1e-6); + assert!((percentile_sorted(&data, 0.5) - 3.0).abs() < 1e-6); + assert!((percentile_sorted(&data, 1.0) - 5.0).abs() < 1e-6); + } + + #[test] + fn test_percentile_sorted_single() { + assert!((percentile_sorted(&[42.0], 0.5) - 42.0).abs() < 1e-6); + } +} diff --git a/crates/giql-datafusion/src/stats/types.rs b/crates/giql-datafusion/src/stats/types.rs new file mode 100644 index 0000000..beb0b81 --- /dev/null +++ b/crates/giql-datafusion/src/stats/types.rs @@ -0,0 +1,82 @@ +/// Per-row-group statistics derived from Parquet column chunk metadata. +#[derive(Debug, Clone)] +pub struct RowGroupBounds { + /// Minimum value of the start column in this row group. + pub min_start: i64, + /// Maximum value of the start column in this row group. + pub max_start: i64, + /// Minimum value of the end column in this row group. + pub min_end: i64, + /// Maximum value of the end column in this row group. + pub max_end: i64, + /// Number of rows in this row group. + pub row_count: usize, +} + +impl RowGroupBounds { + /// Loose upper bound on interval width within this row group. + /// + /// No interval can be wider than `max(end) - min(start)`. + pub fn width_upper_bound(&self) -> i64 { + self.max_end - self.min_start + } + + /// Width signal from the high end of the sort order. + /// + /// If `max(end) - max(start)` is small, the widest intervals at + /// the end of the row group are narrow. + pub fn width_at_max(&self) -> i64 { + self.max_end - self.max_start + } + + /// Width signal from the low end of the sort order. + /// + /// If `min(end) - min(start)` is small, the earliest intervals + /// are narrow. + pub fn width_at_min(&self) -> i64 { + self.min_end - self.min_start + } +} + +/// Width distribution statistics computed from sampled intervals. +#[derive(Debug, Clone)] +pub struct WidthStats { + /// Median interval width. + pub median: f64, + /// Mean interval width. + pub mean: f64, + /// 95th percentile width. + pub p95: f64, + /// 99th percentile width. + pub p99: f64, + /// Coefficient of variation (std_dev / mean). + pub cv: f64, + /// Ratio of p99 to median width. + pub p99_median_ratio: f64, +} + +/// Aggregate interval statistics for a Parquet file, combining +/// metadata-derived bounds with sampled width distribution. +#[derive(Debug, Clone)] +pub struct IntervalStats { + /// Total row count across all row groups. + pub row_count: usize, + /// Global minimum start position. + pub domain_min: i64, + /// Global maximum end position. + pub domain_max: i64, + /// Whether the file is sorted by the start column + /// (from `sorting_columns` metadata). + pub is_sorted_by_start: bool, + /// Per-row-group bounds from column chunk statistics. + pub row_group_bounds: Vec, + /// Width distribution from sampling. + pub width: WidthStats, +} + +impl IntervalStats { + /// Domain span: total coordinate range covered by the file. + pub fn domain_span(&self) -> i64 { + self.domain_max - self.domain_min + } +} From 86f7545b1d4f6f85f4070976081c36d4b136a106 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 12:59:03 -0400 Subject: [PATCH 02/28] fix: Handle StringViewArray in interval join exec plans DataFusion v47 returns StringViewArray (not StringArray) for string columns read from Parquet. The sweep-line and binned join execution plans were downcasting to StringArray only, causing runtime errors when the optimizer replaced default joins. Now both string types are handled via a fallback chain. --- .../giql-datafusion/src/exec/binned_join.rs | 45 +++++++++++++------ crates/giql-datafusion/src/exec/sweep_line.rs | 45 +++++++++++++------ 2 files changed, 64 insertions(+), 26 deletions(-) diff --git a/crates/giql-datafusion/src/exec/binned_join.rs b/crates/giql-datafusion/src/exec/binned_join.rs index e2616e8..0b8333a 100644 --- a/crates/giql-datafusion/src/exec/binned_join.rs +++ b/crates/giql-datafusion/src/exec/binned_join.rs @@ -3,7 +3,10 @@ use std::collections::HashMap; use std::fmt; use std::sync::Arc; -use arrow::array::{Array, ArrayRef, Int64Array, RecordBatch, StringArray}; +use arrow::array::{ + Array, ArrayRef, Int64Array, RecordBatch, StringArray, + StringViewArray, +}; use arrow::datatypes::SchemaRef; use datafusion::common::Result; use datafusion::execution::SendableRecordBatchStream; @@ -231,16 +234,7 @@ fn extract_interval_rows( let mut rows = Vec::new(); for (batch_idx, batch) in batches.iter().enumerate() { - let chroms = batch - .column(cols.chrom_idx) - .as_any() - .downcast_ref::() - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "Chrom column is not StringArray".to_string(), - ) - })?; - + let chrom_col = batch.column(cols.chrom_idx); let starts = batch .column(cols.start_idx) .as_any() @@ -262,14 +256,21 @@ fn extract_interval_rows( })?; for row_idx in 0..batch.num_rows() { - if chroms.is_null(row_idx) + if chrom_col.is_null(row_idx) || starts.is_null(row_idx) || ends.is_null(row_idx) { continue; } + let chrom = get_string_value(chrom_col.as_ref(), row_idx) + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "Chrom column has unsupported string type" + .to_string(), + ) + })?; rows.push(IntervalRow { - chrom: chroms.value(row_idx).to_string(), + chrom, start: starts.value(row_idx), end: ends.value(row_idx), row_ref: RowRef { @@ -350,3 +351,21 @@ async fn collect_batches( Ok(batches) } + +/// Extract a string value from an array that may be StringArray or +/// StringViewArray (DataFusion v47+ uses StringViewArray by default). +fn get_string_value( + array: &dyn Array, + idx: usize, +) -> Option { + array + .as_any() + .downcast_ref::() + .map(|arr| arr.value(idx).to_string()) + .or_else(|| { + array + .as_any() + .downcast_ref::() + .map(|arr| arr.value(idx).to_string()) + }) +} diff --git a/crates/giql-datafusion/src/exec/sweep_line.rs b/crates/giql-datafusion/src/exec/sweep_line.rs index fd43dc8..97b5728 100644 --- a/crates/giql-datafusion/src/exec/sweep_line.rs +++ b/crates/giql-datafusion/src/exec/sweep_line.rs @@ -2,7 +2,10 @@ use std::any::Any; use std::fmt; use std::sync::Arc; -use arrow::array::{Array, ArrayRef, Int64Array, RecordBatch, StringArray}; +use arrow::array::{ + Array, ArrayRef, Int64Array, RecordBatch, StringArray, + StringViewArray, +}; use arrow::datatypes::SchemaRef; use datafusion::common::Result; use datafusion::execution::SendableRecordBatchStream; @@ -175,16 +178,7 @@ fn extract_intervals( let mut intervals = Vec::new(); for (batch_idx, batch) in batches.iter().enumerate() { - let chroms = batch - .column(cols.chrom_idx) - .as_any() - .downcast_ref::() - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "Chrom column is not StringArray".to_string(), - ) - })?; - + let chrom_col = batch.column(cols.chrom_idx); let starts = batch .column(cols.start_idx) .as_any() @@ -206,14 +200,21 @@ fn extract_intervals( })?; for row_idx in 0..batch.num_rows() { - if chroms.is_null(row_idx) + if chrom_col.is_null(row_idx) || starts.is_null(row_idx) || ends.is_null(row_idx) { continue; } + let chrom = get_string_value(chrom_col.as_ref(), row_idx) + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "Chrom column has unsupported string type" + .to_string(), + ) + })?; intervals.push(FlatInterval { - chrom: chroms.value(row_idx).to_string(), + chrom, start: starts.value(row_idx), end: ends.value(row_idx), batch_idx, @@ -349,3 +350,21 @@ async fn collect_batches( Ok(batches) } + +/// Extract a string value from an array that may be StringArray or +/// StringViewArray (DataFusion v47+ uses StringViewArray by default). +fn get_string_value( + array: &dyn Array, + idx: usize, +) -> Option { + array + .as_any() + .downcast_ref::() + .map(|arr| arr.value(idx).to_string()) + .or_else(|| { + array + .as_any() + .downcast_ref::() + .map(|arr| arr.value(idx).to_string()) + }) +} From 9aaf36e06ec081147f6a0c5c518fb2b54ecc4d67 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 12:59:15 -0400 Subject: [PATCH 03/28] test: Add unit and integration tests for INTERSECTS optimizer Unit tests cover stats types, Parquet sampling, cost model edge cases, config defaults, pruning bounds, and optimizer registration. Integration tests exercise the full pipeline through DataFusion: create Parquet files, register with SessionContext, execute INTERSECTS join SQL, and verify correctness for overlapping, non-overlapping, cross-chromosome, adjacent, and containment interval scenarios. Total: 38 tests (31 unit + 6 integration + 1 doc-test). --- crates/giql-datafusion/src/cost.rs | 39 +++ crates/giql-datafusion/src/lib.rs | 69 +++++ crates/giql-datafusion/src/pruning.rs | 25 ++ crates/giql-datafusion/src/stats/mod.rs | 56 ++++ crates/giql-datafusion/src/stats/sampler.rs | 87 ++++++ crates/giql-datafusion/src/stats/types.rs | 61 ++++ .../giql-datafusion/tests/integration_test.rs | 269 ++++++++++++++++++ 7 files changed, 606 insertions(+) create mode 100644 crates/giql-datafusion/tests/integration_test.rs diff --git a/crates/giql-datafusion/src/cost.rs b/crates/giql-datafusion/src/cost.rs index ba79456..302caf3 100644 --- a/crates/giql-datafusion/src/cost.rs +++ b/crates/giql-datafusion/src/cost.rs @@ -264,4 +264,43 @@ mod tests { let bin_size = model.estimate_optimal_bin_size(&left, &right); assert_eq!(bin_size, MIN_BIN_SIZE); } + + #[test] + fn test_both_sorted_selects_sweep_with_skip() { + let model = CostModel::new(&default_config()); + // CV just above threshold, both sides sorted + let left = make_stats(100_000, 100.0, 200.0, 500.0, 600.0, 1.6, true); + let right = make_stats(100_000, 100.0, 200.0, 500.0, 600.0, 0.5, true); + + match model.decide(&left, &right) { + JoinStrategy::SweepLine { skip_sort } => { + assert!(skip_sort); + } + other => panic!("Expected SweepLine, got {:?}", other), + } + } + + #[test] + fn test_at_threshold_does_not_short_circuit() { + let model = CostModel::new(&default_config()); + // p99/median = exactly 10.0 — threshold is >, so should NOT + // short-circuit to sweep line + let left = make_stats( + 1_000_000, 100.0, 100.0, 500.0, 1000.0, 0.3, false, + ); + let right = make_stats( + 1_000_000, 100.0, 100.0, 500.0, 1000.0, 0.3, false, + ); + + // p99/median = 1000/100 = 10.0, which is NOT > 10.0 + let strategy = model.decide(&left, &right); + // Should reach cost comparison, not short-circuit + match strategy { + JoinStrategy::SweepLine { .. } + | JoinStrategy::BinnedJoin { .. } => {} + JoinStrategy::NestedLoop => { + panic!("Should not return NestedLoop with stats") + } + } + } } diff --git a/crates/giql-datafusion/src/lib.rs b/crates/giql-datafusion/src/lib.rs index efdde1b..c91f65c 100644 --- a/crates/giql-datafusion/src/lib.rs +++ b/crates/giql-datafusion/src/lib.rs @@ -77,3 +77,72 @@ pub fn register_optimizer( .with_physical_optimizer_rules(rules) .build() } + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_default_config() { + let config = IntersectsOptimizerConfig::default(); + assert!((config.p99_median_threshold - 10.0).abs() < f64::EPSILON); + assert!((config.cv_threshold - 1.5).abs() < f64::EPSILON); + assert_eq!(config.max_sample_row_groups, 3); + } + + #[test] + fn test_custom_config_used_by_cost_model() { + let config = IntersectsOptimizerConfig { + p99_median_threshold: 5.0, + cv_threshold: 1.0, + max_sample_row_groups: 1, + }; + let model = cost::CostModel::new(&config); + + // With p99/median = 6.0 > 5.0 (custom threshold), should + // short-circuit to sweep line even though default threshold + // would not trigger. + let stats = stats::IntervalStats { + row_count: 100_000, + domain_min: 0, + domain_max: 1_000_000, + is_sorted_by_start: false, + row_group_bounds: vec![], + width: stats::WidthStats { + median: 100.0, + mean: 120.0, + p95: 500.0, + p99: 600.0, + cv: 0.5, + p99_median_ratio: 6.0, + }, + }; + + match model.decide(&stats, &stats) { + JoinStrategy::SweepLine { .. } => {} + other => panic!( + "Expected SweepLine with custom threshold, got {:?}", + other + ), + } + } + + #[test] + fn test_register_optimizer_adds_rule() { + use datafusion::execution::SessionStateBuilder; + + let state = SessionStateBuilder::new() + .with_default_features() + .build(); + let n_before = state.physical_optimizers().len(); + + let config = IntersectsOptimizerConfig::default(); + let state = register_optimizer(state, config); + let n_after = state.physical_optimizers().len(); + + assert_eq!(n_after, n_before + 1); + + let last_rule = state.physical_optimizers().last().unwrap(); + assert_eq!(last_rule.name(), "intersects_optimizer"); + } +} diff --git a/crates/giql-datafusion/src/pruning.rs b/crates/giql-datafusion/src/pruning.rs index d2ab564..91271ca 100644 --- a/crates/giql-datafusion/src/pruning.rs +++ b/crates/giql-datafusion/src/pruning.rs @@ -137,4 +137,29 @@ mod tests { assert_eq!(preds[3].op, PruningOp::Gt); assert_eq!(preds[3].value, 1000); } + + #[test] + fn test_domain_bounds_from_interval_stats() { + use crate::stats::{IntervalStats, WidthStats}; + + let stats = IntervalStats { + row_count: 1000, + domain_min: 500, + domain_max: 50000, + is_sorted_by_start: false, + row_group_bounds: vec![], + width: WidthStats { + median: 100.0, + mean: 100.0, + p95: 100.0, + p99: 100.0, + cv: 0.0, + p99_median_ratio: 1.0, + }, + }; + + let bounds = DomainBounds::from(&stats); + assert_eq!(bounds.min_start, 500); + assert_eq!(bounds.max_end, 50000); + } } diff --git a/crates/giql-datafusion/src/stats/mod.rs b/crates/giql-datafusion/src/stats/mod.rs index a49a756..6ce8aa3 100644 --- a/crates/giql-datafusion/src/stats/mod.rs +++ b/crates/giql-datafusion/src/stats/mod.rs @@ -91,4 +91,60 @@ mod tests { fn test_select_representative_max_one() { assert_eq!(select_representative_row_groups(10, 1), vec![0]); } + + #[test] + fn test_collect_parquet_stats_uniform() { + use arrow::array::{Int64Array, StringArray}; + use arrow::datatypes::{DataType, Field, Schema}; + use arrow::record_batch::RecordBatch; + use parquet::arrow::ArrowWriter; + use std::sync::Arc; + use tempfile::NamedTempFile; + + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + let starts: Vec = (0..50).map(|i| i * 200).collect(); + let ends: Vec = starts.iter().map(|s| s + 100).collect(); + let chroms: Vec<&str> = vec!["chr1"; 50]; + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(StringArray::from(chroms)), + Arc::new(Int64Array::from(starts.clone())), + Arc::new(Int64Array::from(ends.clone())), + ], + ) + .unwrap(); + + let file = NamedTempFile::new().unwrap(); + let mut writer = + ArrowWriter::try_new(file.reopen().unwrap(), schema, None) + .unwrap(); + writer.write(&batch).unwrap(); + writer.close().unwrap(); + + let stats = + collect_parquet_stats(file.path(), "start", "end", 3) + .unwrap(); + + assert_eq!(stats.row_count, 50); + assert_eq!(stats.domain_min, 0); + assert_eq!(stats.domain_max, *ends.last().unwrap()); + assert!((stats.width.median - 100.0).abs() < 1e-6); + assert!(stats.width.cv < 0.01); + } + + #[test] + fn test_collect_parquet_stats_nonexistent_file() { + let result = collect_parquet_stats( + Path::new("/tmp/nonexistent_file.parquet"), + "start", + "end", + 3, + ); + assert!(result.is_err()); + } } diff --git a/crates/giql-datafusion/src/stats/sampler.rs b/crates/giql-datafusion/src/stats/sampler.rs index f90c7e3..765ef8c 100644 --- a/crates/giql-datafusion/src/stats/sampler.rs +++ b/crates/giql-datafusion/src/stats/sampler.rs @@ -214,4 +214,91 @@ mod tests { fn test_percentile_sorted_single() { assert!((percentile_sorted(&[42.0], 0.5) - 42.0).abs() < 1e-6); } + + #[test] + fn test_sample_widths_uniform_parquet() { + use arrow::array::{Int64Array, StringArray}; + use arrow::datatypes::{DataType, Field, Schema}; + use arrow::record_batch::RecordBatch; + use parquet::arrow::ArrowWriter; + use std::sync::Arc; + use tempfile::NamedTempFile; + + // Write a Parquet file with uniform 100bp intervals + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + let starts: Vec = (0..100).map(|i| i * 200).collect(); + let ends: Vec = starts.iter().map(|s| s + 100).collect(); + let chroms: Vec<&str> = vec!["chr1"; 100]; + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(StringArray::from(chroms)), + Arc::new(Int64Array::from(starts)), + Arc::new(Int64Array::from(ends)), + ], + ) + .unwrap(); + + let file = NamedTempFile::new().unwrap(); + let mut writer = + ArrowWriter::try_new(file.reopen().unwrap(), schema, None) + .unwrap(); + writer.write(&batch).unwrap(); + writer.close().unwrap(); + + let stats = + sample_widths(file.path(), "start", "end", &[0]).unwrap(); + assert!((stats.median - 100.0).abs() < 1e-6); + assert!(stats.cv < 0.01); + } + + #[test] + fn test_sample_widths_missing_column() { + use arrow::array::{Int64Array, StringArray}; + use arrow::datatypes::{DataType, Field, Schema}; + use arrow::record_batch::RecordBatch; + use parquet::arrow::ArrowWriter; + use std::sync::Arc; + use tempfile::NamedTempFile; + + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(StringArray::from(vec!["chr1"])), + Arc::new(Int64Array::from(vec![100])), + Arc::new(Int64Array::from(vec![200])), + ], + ) + .unwrap(); + let file = NamedTempFile::new().unwrap(); + let mut writer = + ArrowWriter::try_new(file.reopen().unwrap(), schema, None) + .unwrap(); + writer.write(&batch).unwrap(); + writer.close().unwrap(); + + let result = + sample_widths(file.path(), "nonexistent", "end", &[0]); + assert!(result.is_err()); + } + + #[test] + fn test_sample_widths_empty_row_groups() { + let result = sample_widths( + std::path::Path::new("/tmp/fake.parquet"), + "start", + "end", + &[], + ); + assert!(result.is_err()); + } } diff --git a/crates/giql-datafusion/src/stats/types.rs b/crates/giql-datafusion/src/stats/types.rs index beb0b81..44c7f9e 100644 --- a/crates/giql-datafusion/src/stats/types.rs +++ b/crates/giql-datafusion/src/stats/types.rs @@ -80,3 +80,64 @@ impl IntervalStats { self.domain_max - self.domain_min } } + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_width_upper_bound() { + let rg = RowGroupBounds { + min_start: 100, + max_start: 400, + min_end: 200, + max_end: 500, + row_count: 10, + }; + assert_eq!(rg.width_upper_bound(), 400); + } + + #[test] + fn test_width_at_max() { + let rg = RowGroupBounds { + min_start: 100, + max_start: 400, + min_end: 200, + max_end: 500, + row_count: 10, + }; + assert_eq!(rg.width_at_max(), 100); + } + + #[test] + fn test_width_at_min() { + let rg = RowGroupBounds { + min_start: 100, + max_start: 400, + min_end: 200, + max_end: 500, + row_count: 10, + }; + assert_eq!(rg.width_at_min(), 100); + } + + #[test] + fn test_domain_span() { + let stats = IntervalStats { + row_count: 1000, + domain_min: 0, + domain_max: 1_000_000, + is_sorted_by_start: false, + row_group_bounds: vec![], + width: WidthStats { + median: 100.0, + mean: 100.0, + p95: 100.0, + p99: 100.0, + cv: 0.0, + p99_median_ratio: 1.0, + }, + }; + assert_eq!(stats.domain_span(), 1_000_000); + } +} diff --git a/crates/giql-datafusion/tests/integration_test.rs b/crates/giql-datafusion/tests/integration_test.rs new file mode 100644 index 0000000..85ba526 --- /dev/null +++ b/crates/giql-datafusion/tests/integration_test.rs @@ -0,0 +1,269 @@ +//! Integration tests for the INTERSECTS join optimizer. +//! +//! These tests exercise the full pipeline: create Parquet files → +//! register with DataFusion → add optimizer rule → execute +//! INTERSECTS join SQL → verify results. + +use std::path::Path; +use std::sync::Arc; + +use arrow::array::{Int64Array, StringArray}; +use arrow::datatypes::{DataType, Field, Schema}; +use arrow::record_batch::RecordBatch; +use datafusion::execution::SessionStateBuilder; +use datafusion::prelude::*; +use parquet::arrow::ArrowWriter; +use tempfile::TempDir; + +use giql_datafusion::{IntersectsOptimizerConfig, register_optimizer}; + +/// Write a Parquet file with the given genomic intervals. +fn write_intervals_parquet( + dir: &Path, + filename: &str, + chroms: &[&str], + starts: &[i64], + ends: &[i64], +) -> std::path::PathBuf { + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(StringArray::from(chroms.to_vec())), + Arc::new(Int64Array::from(starts.to_vec())), + Arc::new(Int64Array::from(ends.to_vec())), + ], + ) + .unwrap(); + + let path = dir.join(filename); + let file = std::fs::File::create(&path).unwrap(); + let mut writer = + ArrowWriter::try_new(file, schema, None).unwrap(); + writer.write(&batch).unwrap(); + writer.close().unwrap(); + path +} + +/// Create a SessionContext with the INTERSECTS optimizer registered. +fn make_ctx_with_optimizer() -> SessionContext { + let config = IntersectsOptimizerConfig::default(); + let state = SessionStateBuilder::new() + .with_default_features() + .build(); + let state = register_optimizer(state, config); + SessionContext::from(state) +} + +/// The standard INTERSECTS join SQL using the chrom/start/end +/// predicate pattern that the optimizer detects. +const INTERSECTS_SQL: &str = "\ + SELECT a.chrom, a.start, a.\"end\", \ + b.chrom AS chrom_b, b.start AS start_b, b.\"end\" AS end_b \ + FROM a JOIN b \ + ON a.chrom = b.chrom \ + AND a.start < b.\"end\" \ + AND a.\"end\" > b.start"; + +// ── Correctness tests ────────────────────────────────────────── + +#[tokio::test] +async fn test_overlapping_intervals_returns_pairs() { + let dir = TempDir::new().unwrap(); + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1", "chr1"], + &[100, 300, 600], + &[250, 500, 800], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1"], + &[200, 700], + &[400, 900], + ); + + let ctx = make_ctx_with_optimizer(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // Expected overlaps: + // a[100,250) overlaps b[200,400) ✓ + // a[300,500) overlaps b[200,400) ✓ + // a[600,800) overlaps b[700,900) ✓ + assert_eq!(total_rows, 3); +} + +#[tokio::test] +async fn test_no_overlapping_intervals_returns_empty() { + let dir = TempDir::new().unwrap(); + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1"], + &[100, 300], + &[200, 400], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1"], + &[500, 700], + &[600, 800], + ); + + let ctx = make_ctx_with_optimizer(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 0); +} + +#[tokio::test] +async fn test_different_chromosomes_returns_empty() { + let dir = TempDir::new().unwrap(); + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1"], + &[100, 300], + &[500, 600], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr2", "chr2"], + &[100, 300], + &[500, 600], + ); + + let ctx = make_ctx_with_optimizer(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 0); +} + +#[tokio::test] +async fn test_adjacent_intervals_no_overlap() { + // Half-open interval semantics: [100,200) and [200,300) do NOT + // overlap because 200 is not < 200. + let dir = TempDir::new().unwrap(); + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1"], + &[100], + &[200], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1"], + &[200], + &[300], + ); + + let ctx = make_ctx_with_optimizer(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 0); +} + +#[tokio::test] +async fn test_containment_counts_as_overlap() { + // [100,500) fully contains [200,300) + let dir = TempDir::new().unwrap(); + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1"], + &[100], + &[500], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1"], + &[200], + &[300], + ); + + let ctx = make_ctx_with_optimizer(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 1); +} + +// ── Optimizer registration test ──────────────────────────────── + +#[tokio::test] +async fn test_register_optimizer_present_in_state() { + let state = SessionStateBuilder::new() + .with_default_features() + .build(); + let n_before = state.physical_optimizers().len(); + + let config = IntersectsOptimizerConfig::default(); + let state = register_optimizer(state, config); + + assert_eq!(state.physical_optimizers().len(), n_before + 1); + assert_eq!( + state.physical_optimizers().last().unwrap().name(), + "intersects_optimizer" + ); +} From 42e90549657885d4ce333cd38cf54670af7a40c5 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 15:40:43 -0400 Subject: [PATCH 04/28] fix: Correct sweep-line active set, partition collection, and filter detection Three correctness bugs fixed: 1. Sweep-line active set: the retain predicate was removing wide right intervals based on r.start >= l.end, but those intervals could still overlap a later, wider left interval. Now only truly expired intervals (r.end <= l.start) are removed, and the full overlap check is done inline when emitting matches. 2. Multi-partition collection: RepartitionExec uses shared channels that break under sequential partition reads. Replaced manual sequential collection with datafusion::physical_plan::collect which spawns all partitions concurrently. 3. Filter column resolution: DataFusion may order filter operands as (right.start < left.end) instead of (left.start < right.end). The pattern detector now resolves columns by name and join side rather than assuming positional semantics. Also handles Int32 and StringViewArray column types that DataFusion v47 produces from Parquet reads. --- .../giql-datafusion/src/exec/binned_join.rs | 92 +++---- crates/giql-datafusion/src/exec/sweep_line.rs | 132 ++++++---- crates/giql-datafusion/src/pattern.rs | 232 +++++++++--------- 3 files changed, 238 insertions(+), 218 deletions(-) diff --git a/crates/giql-datafusion/src/exec/binned_join.rs b/crates/giql-datafusion/src/exec/binned_join.rs index 0b8333a..6117a90 100644 --- a/crates/giql-datafusion/src/exec/binned_join.rs +++ b/crates/giql-datafusion/src/exec/binned_join.rs @@ -4,7 +4,7 @@ use std::fmt; use std::sync::Arc; use arrow::array::{ - Array, ArrayRef, Int64Array, RecordBatch, StringArray, + Array, ArrayRef, Int32Array, Int64Array, RecordBatch, StringArray, StringViewArray, }; use arrow::datatypes::SchemaRef; @@ -118,22 +118,22 @@ impl ExecutionPlan for BinnedJoinExec { fn execute( &self, - partition: usize, + _partition: usize, context: Arc, ) -> Result { - let left_stream = - self.left.execute(partition, context.clone())?; - let right_stream = self.right.execute(partition, context)?; - + let left = self.left.clone(); + let right = self.right.clone(); let left_cols = self.left_cols.clone(); let right_cols = self.right_cols.clone(); let schema = self.schema.clone(); let bin_size = self.bin_size; + let ctx = context; let stream = futures::stream::once(async move { - let left_batches = collect_batches(left_stream).await?; + let left_batches = + collect_all_partitions(&left, &ctx).await?; let right_batches = - collect_batches(right_stream).await?; + collect_all_partitions(&right, &ctx).await?; binned_join( &schema, @@ -235,30 +235,13 @@ fn extract_interval_rows( for (batch_idx, batch) in batches.iter().enumerate() { let chrom_col = batch.column(cols.chrom_idx); - let starts = batch - .column(cols.start_idx) - .as_any() - .downcast_ref::() - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "Start column is not Int64Array".to_string(), - ) - })?; - - let ends = batch - .column(cols.end_idx) - .as_any() - .downcast_ref::() - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "End column is not Int64Array".to_string(), - ) - })?; + let start_col = batch.column(cols.start_idx); + let end_col = batch.column(cols.end_idx); for row_idx in 0..batch.num_rows() { if chrom_col.is_null(row_idx) - || starts.is_null(row_idx) - || ends.is_null(row_idx) + || start_col.is_null(row_idx) + || end_col.is_null(row_idx) { continue; } @@ -269,10 +252,22 @@ fn extract_interval_rows( .to_string(), ) })?; + let start = get_i64_value(start_col.as_ref(), row_idx) + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "Start column is not Int32 or Int64".to_string(), + ) + })?; + let end = get_i64_value(end_col.as_ref(), row_idx) + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "End column is not Int32 or Int64".to_string(), + ) + })?; rows.push(IntervalRow { chrom, - start: starts.value(row_idx), - end: ends.value(row_idx), + start, + end, row_ref: RowRef { batch_idx, row_idx, @@ -336,20 +331,31 @@ fn build_output( Ok(RecordBatch::try_new(schema.clone(), columns)?) } -/// Collect all batches from a stream. -async fn collect_batches( - stream: SendableRecordBatchStream, +/// Collect all record batches from all partitions concurrently. +async fn collect_all_partitions( + plan: &Arc, + context: &Arc, ) -> Result> { - use futures::StreamExt; - - let mut batches = Vec::new(); - let mut stream = stream; - - while let Some(batch) = stream.next().await { - batches.push(batch?); - } + datafusion::physical_plan::collect( + plan.clone(), + context.clone(), + ) + .await +} - Ok(batches) +/// Extract an i64 value from an array that may be Int32Array or +/// Int64Array. +fn get_i64_value(array: &dyn Array, idx: usize) -> Option { + array + .as_any() + .downcast_ref::() + .map(|arr| arr.value(idx)) + .or_else(|| { + array + .as_any() + .downcast_ref::() + .map(|arr| arr.value(idx) as i64) + }) } /// Extract a string value from an array that may be StringArray or diff --git a/crates/giql-datafusion/src/exec/sweep_line.rs b/crates/giql-datafusion/src/exec/sweep_line.rs index 97b5728..f075201 100644 --- a/crates/giql-datafusion/src/exec/sweep_line.rs +++ b/crates/giql-datafusion/src/exec/sweep_line.rs @@ -3,7 +3,7 @@ use std::fmt; use std::sync::Arc; use arrow::array::{ - Array, ArrayRef, Int64Array, RecordBatch, StringArray, + Array, ArrayRef, Int32Array, Int64Array, RecordBatch, StringArray, StringViewArray, }; use arrow::datatypes::SchemaRef; @@ -120,20 +120,24 @@ impl ExecutionPlan for SweepLineJoinExec { fn execute( &self, - partition: usize, + _partition: usize, context: Arc, ) -> Result { - let left_stream = - self.left.execute(partition, context.clone())?; - let right_stream = self.right.execute(partition, context)?; - + // Collect ALL partitions from both children. DataFusion may + // split inputs across multiple partitions (default = + // num_cpus). We must read every partition to get all rows. + let left = self.left.clone(); + let right = self.right.clone(); let left_cols = self.left_cols.clone(); let right_cols = self.right_cols.clone(); let schema = self.schema.clone(); + let ctx = context; let stream = futures::stream::once(async move { - let left_batches = collect_batches(left_stream).await?; - let right_batches = collect_batches(right_stream).await?; + let left_batches = + collect_all_partitions(&left, &ctx).await?; + let right_batches = + collect_all_partitions(&right, &ctx).await?; let left_intervals = extract_intervals(&left_batches, &left_cols)?; @@ -179,30 +183,13 @@ fn extract_intervals( for (batch_idx, batch) in batches.iter().enumerate() { let chrom_col = batch.column(cols.chrom_idx); - let starts = batch - .column(cols.start_idx) - .as_any() - .downcast_ref::() - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "Start column is not Int64Array".to_string(), - ) - })?; - - let ends = batch - .column(cols.end_idx) - .as_any() - .downcast_ref::() - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "End column is not Int64Array".to_string(), - ) - })?; + let start_col = batch.column(cols.start_idx); + let end_col = batch.column(cols.end_idx); for row_idx in 0..batch.num_rows() { if chrom_col.is_null(row_idx) - || starts.is_null(row_idx) - || ends.is_null(row_idx) + || start_col.is_null(row_idx) + || end_col.is_null(row_idx) { continue; } @@ -213,10 +200,22 @@ fn extract_intervals( .to_string(), ) })?; + let start = get_i64_value(start_col.as_ref(), row_idx) + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "Start column is not Int32 or Int64".to_string(), + ) + })?; + let end = get_i64_value(end_col.as_ref(), row_idx) + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "End column is not Int32 or Int64".to_string(), + ) + })?; intervals.push(FlatInterval { chrom, - start: starts.value(row_idx), - end: ends.value(row_idx), + start, + end, batch_idx, row_idx, }); @@ -247,8 +246,11 @@ fn sweep_line_join( for l in left { - // Advance right_idx to add all right intervals with - // start < left.end on the same chromosome + // Advance right_idx to add right intervals that START before + // ANY future left interval could end. Since left is sorted by + // start, and a future left interval's end could be arbitrarily + // large, we add all right intervals with start < l.end. + // This is conservative — we check exact overlap below. while right_idx < right.len() { let r = &right[right_idx]; if r.chrom < l.chrom { @@ -258,7 +260,6 @@ fn sweep_line_join( if r.chrom > l.chrom { break; } - // Same chromosome if r.start >= l.end { break; } @@ -266,21 +267,30 @@ fn sweep_line_join( right_idx += 1; } - // Remove expired intervals from active set + // Remove only truly expired intervals: those whose END is at + // or before the current left START. Since left is sorted by + // start, any interval with end <= l.start can never overlap + // any future left interval either. + // + // We do NOT filter on r.start < l.end here because a wide + // right interval (r.start before a previous narrow left's + // end) may still be needed by a LATER wider left interval. active.retain(|&ri| { let r = &right[ri]; r.chrom == l.chrom && r.end > l.start }); - // All remaining active intervals overlap with l + // Emit matches: check the full overlap condition inline. for &ri in &active { let r = &right[ri]; - matches.push(( - l.batch_idx, - l.row_idx, - r.batch_idx, - r.row_idx, - )); + if r.start < l.end { + matches.push(( + l.batch_idx, + l.row_idx, + r.batch_idx, + r.row_idx, + )); + } } } @@ -335,20 +345,34 @@ fn build_output_batch( Ok(RecordBatch::try_new(schema.clone(), columns)?) } -/// Collect all batches from a stream into a Vec. -async fn collect_batches( - stream: SendableRecordBatchStream, +/// Collect all record batches from all partitions of an execution +/// plan. Uses DataFusion's `collect` which spawns partition tasks +/// concurrently — required because RepartitionExec uses shared +/// channels that break under sequential execution. +async fn collect_all_partitions( + plan: &Arc, + context: &Arc, ) -> Result> { - use futures::StreamExt; - - let mut batches = Vec::new(); - let mut stream = stream; - - while let Some(batch) = stream.next().await { - batches.push(batch?); - } + datafusion::physical_plan::collect( + plan.clone(), + context.clone(), + ) + .await +} - Ok(batches) +/// Extract an i64 value from an array that may be Int32Array or +/// Int64Array. +fn get_i64_value(array: &dyn Array, idx: usize) -> Option { + array + .as_any() + .downcast_ref::() + .map(|arr| arr.value(idx)) + .or_else(|| { + array + .as_any() + .downcast_ref::() + .map(|arr| arr.value(idx) as i64) + }) } /// Extract a string value from an array that may be StringArray or diff --git a/crates/giql-datafusion/src/pattern.rs b/crates/giql-datafusion/src/pattern.rs index 60b5594..8d1e065 100644 --- a/crates/giql-datafusion/src/pattern.rs +++ b/crates/giql-datafusion/src/pattern.rs @@ -148,7 +148,14 @@ fn detect_from_hash_join( &right_schema, ) { Some(cols) => cols, - None => return Ok(None), + None => { + eprintln!( + "INTERSECTS optimizer: HashJoinExec filter didn't \ + match. filter={:?}, indices={:?}", + filter_expr, column_indices, + ); + return Ok(None); + } }; let left_start_idx = match left_schema @@ -218,9 +225,12 @@ fn detect_from_hash_join( /// Extract range column names from a HashJoin filter expression. /// -/// The filter should contain `left.start < right.end AND left.end > -/// right.start`. Returns `Some((left_start, left_end, right_start, -/// right_end))` column names if the pattern matches. +/// The filter should contain the interval overlap condition +/// `A.start < B.end AND A.end > B.start`, but DataFusion may reorder +/// the operands arbitrarily. We resolve all four columns by name and +/// side, then match them to the canonical form. +/// +/// Returns `Some((left_start, left_end, right_start, right_end))`. fn extract_range_columns_from_filter( expr: &Arc, column_indices: &[ColumnIndex], @@ -228,170 +238,144 @@ fn extract_range_columns_from_filter( right_schema: &SchemaRef, ) -> Option<(String, String, String, String)> { use datafusion::logical_expr::Operator; - use datafusion::physical_expr::expressions::BinaryExpr; + use datafusion::physical_expr::expressions::{BinaryExpr, Column}; let binary = expr.as_any().downcast_ref::()?; - if *binary.op() != Operator::And { return None; } - let left_pred = binary.left(); - let right_pred = binary.right(); - - // Try both orderings of the two predicates - + // Collect all four column references from both predicates. + // Each predicate is either Lt or Gt with two Column operands. + let pred_a = binary.left(); + let pred_b = binary.right(); - try_extract_range_pair( - left_pred, - right_pred, - column_indices, - left_schema, - right_schema, - ) - .or_else(|| { - try_extract_range_pair( - right_pred, - left_pred, - column_indices, - left_schema, - right_schema, - ) - }) -} - -/// Try to extract (left_start, left_end, right_start, right_end) from -/// a pair of predicates where one is Lt and one is Gt. -fn try_extract_range_pair( - pred_a: &Arc, - pred_b: &Arc, - column_indices: &[ColumnIndex], - left_schema: &SchemaRef, - right_schema: &SchemaRef, -) -> Option<(String, String, String, String)> { - let (lt_left, lt_right) = extract_lt_columns( - pred_a, - column_indices, - left_schema, - right_schema, + let cols_a = extract_comparison_columns( + pred_a, column_indices, left_schema, right_schema, )?; - let (gt_left, gt_right) = extract_gt_columns( - pred_b, - column_indices, - left_schema, - right_schema, + let cols_b = extract_comparison_columns( + pred_b, column_indices, left_schema, right_schema, )?; - // lt pattern: left.start < right.end - // gt pattern: left.end > right.start - Some((lt_left, gt_left, gt_right, lt_right)) + // We have two predicates, each with a "lesser" and "greater" side: + // Lt(A, B) means A < B + // Gt(A, B) means A > B + // + // For interval overlap, the two predicates are (in any order): + // some_start < some_end (one from left, one from right) + // some_end > some_start (one from left, one from right) + // + // Rather than parsing the comparison semantics, we simply collect + // all four resolved columns, then identify left_start, left_end, + // right_start, right_end by matching (name, side). + + let all_cols = [&cols_a.0, &cols_a.1, &cols_b.0, &cols_b.1]; + + let left_start = all_cols.iter().find(|c| is_left(&c.side) && is_start_col(&c.name))?; + let left_end = all_cols.iter().find(|c| is_left(&c.side) && is_end_col(&c.name))?; + let right_start = all_cols.iter().find(|c| is_right(&c.side) && is_start_col(&c.name))?; + let right_end = all_cols.iter().find(|c| is_right(&c.side) && is_end_col(&c.name))?; + + Some(( + left_start.name.clone(), + left_end.name.clone(), + right_start.name.clone(), + right_end.name.clone(), + )) } -/// Extract columns from a `<` comparison. -fn extract_lt_columns( - expr: &Arc, - column_indices: &[ColumnIndex], - left_schema: &SchemaRef, - right_schema: &SchemaRef, -) -> Option<(String, String)> { - use datafusion::logical_expr::Operator; - use datafusion::physical_expr::expressions::{BinaryExpr, Column}; +fn is_start_col(name: &str) -> bool { + let lower = name.to_lowercase(); + lower == "start" || lower == "chromstart" || lower == "pos_start" || lower == "begin" +} - let binary = expr.as_any().downcast_ref::()?; - if *binary.op() != Operator::Lt { - return None; - } +fn is_end_col(name: &str) -> bool { + let lower = name.to_lowercase(); + lower == "end" || lower == "chromend" || lower == "pos_end" || lower == "stop" +} - let left_col = binary.left().as_any().downcast_ref::()?; - let right_col = - binary.right().as_any().downcast_ref::()?; +/// A resolved column: name + which side of the join it's on. +#[derive(Debug)] +struct ResolvedColumn { + name: String, + side: JoinSide, +} - let left_name = resolve_column_name( - left_col.index(), - column_indices, - left_schema, - right_schema, - true, - )?; - let right_name = resolve_column_name( - right_col.index(), - column_indices, - left_schema, - right_schema, - false, - )?; +fn is_left(side: &JoinSide) -> bool { + matches!(side, JoinSide::Left) +} - Some((left_name, right_name)) +fn is_right(side: &JoinSide) -> bool { + matches!(side, JoinSide::Right) } -/// Extract columns from a `>` comparison. -fn extract_gt_columns( +/// Extract the two column operands of a Lt or Gt comparison. +fn extract_comparison_columns( expr: &Arc, column_indices: &[ColumnIndex], left_schema: &SchemaRef, right_schema: &SchemaRef, -) -> Option<(String, String)> { +) -> Option<(ResolvedColumn, ResolvedColumn)> { use datafusion::logical_expr::Operator; use datafusion::physical_expr::expressions::{BinaryExpr, Column}; let binary = expr.as_any().downcast_ref::()?; - if *binary.op() != Operator::Gt { - return None; + match binary.op() { + Operator::Lt | Operator::Gt | Operator::LtEq | Operator::GtEq => {} + _ => return None, } - let left_col = binary.left().as_any().downcast_ref::()?; - let right_col = - binary.right().as_any().downcast_ref::()?; + let lhs = binary.left().as_any().downcast_ref::()?; + let rhs = binary.right().as_any().downcast_ref::()?; - let left_name = resolve_column_name( - left_col.index(), - column_indices, - left_schema, - right_schema, - true, - )?; - let right_name = resolve_column_name( - right_col.index(), - column_indices, - left_schema, - right_schema, - false, - )?; + let lhs_resolved = + resolve_column(lhs.index(), column_indices, left_schema, right_schema)?; + let rhs_resolved = + resolve_column(rhs.index(), column_indices, left_schema, right_schema)?; + + // Ensure the two columns are from different sides + if std::mem::discriminant(&lhs_resolved.side) + == std::mem::discriminant(&rhs_resolved.side) + { + return None; + } - Some((left_name, right_name)) + Some((lhs_resolved, rhs_resolved)) } -/// Resolve a filter-local column index to a column name. -fn resolve_column_name( +/// Resolve a filter-local column index to a name and join side. +fn resolve_column( filter_idx: usize, column_indices: &[ColumnIndex], left_schema: &SchemaRef, right_schema: &SchemaRef, - expect_left: bool, -) -> Option { +) -> Option { if filter_idx >= column_indices.len() { return None; } let col_idx = &column_indices[filter_idx]; - let is_left = matches!(col_idx.side, JoinSide::Left); - - if is_left != expect_left { - return None; - } + let (schema, side) = match col_idx.side { + JoinSide::Left => (left_schema, JoinSide::Left), + JoinSide::Right => (right_schema, JoinSide::Right), + _ => return None, + }; - let schema = if is_left { left_schema } else { right_schema }; if col_idx.index >= schema.fields().len() { return None; } - let field = schema.field(col_idx.index); - Some(field.name().clone()) + let name = schema.field(col_idx.index).name().clone(); + Some(ResolvedColumn { name, side }) } /// Recursively find Parquet file paths in the plan tree. +/// +/// DataFusion's object store stores paths relative to the filesystem +/// root (no leading `/`). We prepend `/` to reconstruct the absolute +/// path so that `File::open` works. fn find_parquet_paths(plan: &Arc) -> Vec { - use datafusion::datasource::source::DataSourceExec; use datafusion::datasource::physical_plan::parquet::source::ParquetSource; + use datafusion::datasource::source::DataSourceExec; let mut paths = Vec::new(); @@ -403,9 +387,15 @@ fn find_parquet_paths(plan: &Arc) -> Vec { { for group in &file_config.file_groups { for file in group.iter() { - paths.push(PathBuf::from( - file.object_meta.location.as_ref(), - )); + let loc = file.object_meta.location.as_ref(); + // object_store strips the leading / from absolute + // paths. Reconstruct it for filesystem access. + let fs_path = if loc.starts_with('/') { + PathBuf::from(loc) + } else { + PathBuf::from(format!("/{loc}")) + }; + paths.push(fs_path); } } return paths; From fc4b43b8e371ed28abcd0521fc3902199b5bded1 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 15:40:57 -0400 Subject: [PATCH 05/28] feat: Rewrite binned join to use DataFusion parallel HashJoinExec MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Instead of a single-threaded custom BinnedJoinExec, the optimizer now rewrites the physical plan to compose DataFusion's own parallel infrastructure: BinExpandExec(left) ─┐ ├─ HashJoinExec(on=[chrom, __giql_bin]) BinExpandExec(right) ─┘ │ FilterExec (canonical-bin dedup) │ ProjectionExec (strip extra cols) BinExpandExec is a stateless per-partition node that replicates each interval into rows for every genome bin it touches, adding __giql_bin and __giql_first_bin columns. The canonical-bin filter ensures each pair is emitted exactly once by keeping only the match from max(left_first_bin, right_first_bin). When no Parquet stats are available, the optimizer now defers to DataFusion's built-in join rather than defaulting to sweep line. --- crates/giql-datafusion/src/exec/bin_expand.rs | 249 +++++++++++++++++ crates/giql-datafusion/src/exec/mod.rs | 2 + crates/giql-datafusion/src/optimizer.rs | 258 +++++++++++++++--- 3 files changed, 473 insertions(+), 36 deletions(-) create mode 100644 crates/giql-datafusion/src/exec/bin_expand.rs diff --git a/crates/giql-datafusion/src/exec/bin_expand.rs b/crates/giql-datafusion/src/exec/bin_expand.rs new file mode 100644 index 0000000..b520b3e --- /dev/null +++ b/crates/giql-datafusion/src/exec/bin_expand.rs @@ -0,0 +1,249 @@ +use std::any::Any; +use std::fmt; +use std::sync::Arc; + +use arrow::array::{ + Array, ArrayRef, Int32Array, Int64Array, RecordBatch, + UInt64Array, +}; +use arrow::datatypes::{DataType, Field, SchemaRef}; +use datafusion::common::Result; +use datafusion::execution::SendableRecordBatchStream; +use datafusion::physical_expr::EquivalenceProperties; +use datafusion::physical_plan::execution_plan::{ + Boundedness, EmissionType, +}; +use datafusion::physical_plan::stream::RecordBatchStreamAdapter; +use datafusion::physical_plan::{ + DisplayAs, DisplayFormatType, ExecutionPlan, Partitioning, + PlanProperties, +}; + +/// Stateless per-partition exec that expands each interval into rows +/// for each genome bin it touches. +/// +/// Appends two columns: +/// - `__giql_bin`: the bin ID for this expanded row +/// - `__giql_first_bin`: `start / bin_size` — the interval's first bin +/// +/// The `__giql_first_bin` column enables the canonical-bin +/// deduplication trick: after the hash join, a filter keeps only +/// matches where `__giql_bin == max(left.__giql_first_bin, +/// right.__giql_first_bin)`, ensuring each pair is emitted exactly +/// once. +#[derive(Debug)] +pub struct BinExpandExec { + input: Arc, + start_col_idx: usize, + end_col_idx: usize, + bin_size: usize, + schema: SchemaRef, + properties: PlanProperties, +} + +impl BinExpandExec { + pub fn new( + input: Arc, + start_col_idx: usize, + end_col_idx: usize, + bin_size: usize, + ) -> Self { + let input_schema = input.schema(); + let mut fields: Vec> = + input_schema.fields().iter().cloned().collect(); + fields.push(Arc::new(Field::new( + "__giql_bin", + DataType::Int64, + false, + ))); + fields.push(Arc::new(Field::new( + "__giql_first_bin", + DataType::Int64, + false, + ))); + let schema = + Arc::new(arrow::datatypes::Schema::new(fields)); + + let properties = PlanProperties::new( + EquivalenceProperties::new(schema.clone()), + input.properties().partitioning.clone(), + EmissionType::Incremental, + Boundedness::Bounded, + ); + + Self { + input, + start_col_idx, + end_col_idx, + bin_size, + schema, + properties, + } + } + + /// Number of columns added by this exec (bin + first_bin). + pub const EXTRA_COLS: usize = 2; +} + +impl DisplayAs for BinExpandExec { + fn fmt_as( + &self, + _t: DisplayFormatType, + f: &mut fmt::Formatter<'_>, + ) -> fmt::Result { + write!( + f, + "BinExpandExec: start_col={}, end_col={}, bin_size={}", + self.start_col_idx, self.end_col_idx, self.bin_size + ) + } +} + +impl ExecutionPlan for BinExpandExec { + fn name(&self) -> &str { + "BinExpandExec" + } + + fn as_any(&self) -> &dyn Any { + self + } + + fn schema(&self) -> SchemaRef { + self.schema.clone() + } + + fn properties(&self) -> &PlanProperties { + &self.properties + } + + fn children(&self) -> Vec<&Arc> { + vec![&self.input] + } + + fn with_new_children( + self: Arc, + children: Vec>, + ) -> Result> { + Ok(Arc::new(BinExpandExec::new( + children[0].clone(), + self.start_col_idx, + self.end_col_idx, + self.bin_size, + ))) + } + + fn execute( + &self, + partition: usize, + context: Arc, + ) -> Result { + let input_stream = + self.input.execute(partition, context)?; + let start_idx = self.start_col_idx; + let end_idx = self.end_col_idx; + let bin_size = self.bin_size as i64; + let schema = self.schema.clone(); + + let output_stream = futures::stream::unfold( + input_stream, + move |mut stream| { + let schema = schema.clone(); + async move { + use futures::StreamExt; + match stream.next().await { + Some(Ok(batch)) => { + let result = expand_batch( + &batch, start_idx, end_idx, + bin_size, &schema, + ); + Some((result, stream)) + } + Some(Err(e)) => Some((Err(e), stream)), + None => None, + } + } + }, + ); + + Ok(Box::pin(RecordBatchStreamAdapter::new( + self.schema.clone(), + output_stream, + ))) + } +} + +/// Expand a single batch: replicate each row for each bin it touches, +/// adding `__giql_bin` and `__giql_first_bin` columns. +fn expand_batch( + batch: &RecordBatch, + start_idx: usize, + end_idx: usize, + bin_size: i64, + output_schema: &SchemaRef, +) -> Result { + let num_rows = batch.num_rows(); + if num_rows == 0 { + return Ok(RecordBatch::new_empty(output_schema.clone())); + } + + let start_col = batch.column(start_idx); + let end_col = batch.column(end_idx); + + let mut bin_ranges: Vec<(i64, i64)> = Vec::with_capacity(num_rows); + let mut total_expanded = 0usize; + + for row in 0..num_rows { + let start = get_i64(start_col.as_ref(), row).unwrap_or(0); + let end = get_i64(end_col.as_ref(), row).unwrap_or(0); + let first_bin = start / bin_size; + let last_bin = (end - 1).max(0) / bin_size; + let n_bins = (last_bin - first_bin + 1).max(0) as usize; + bin_ranges.push((first_bin, last_bin)); + total_expanded += n_bins; + } + + let mut row_indices: Vec = + Vec::with_capacity(total_expanded); + let mut bin_ids: Vec = Vec::with_capacity(total_expanded); + let mut first_bins: Vec = + Vec::with_capacity(total_expanded); + + for (row, &(first_bin, last_bin)) in + bin_ranges.iter().enumerate() + { + for bin in first_bin..=last_bin { + row_indices.push(row as u64); + bin_ids.push(bin); + first_bins.push(first_bin); + } + } + + let row_indices_arr = UInt64Array::from(row_indices); + let mut columns: Vec = + Vec::with_capacity(batch.num_columns() + BinExpandExec::EXTRA_COLS); + + for col_idx in 0..batch.num_columns() { + let col = batch.column(col_idx); + let taken = + arrow::compute::take(col.as_ref(), &row_indices_arr, None)?; + columns.push(taken); + } + + columns.push(Arc::new(Int64Array::from(bin_ids))); + columns.push(Arc::new(Int64Array::from(first_bins))); + + Ok(RecordBatch::try_new(output_schema.clone(), columns)?) +} + +fn get_i64(array: &dyn Array, idx: usize) -> Option { + array + .as_any() + .downcast_ref::() + .map(|arr| arr.value(idx)) + .or_else(|| { + array + .as_any() + .downcast_ref::() + .map(|arr| arr.value(idx) as i64) + }) +} diff --git a/crates/giql-datafusion/src/exec/mod.rs b/crates/giql-datafusion/src/exec/mod.rs index ac45e8d..c554fa5 100644 --- a/crates/giql-datafusion/src/exec/mod.rs +++ b/crates/giql-datafusion/src/exec/mod.rs @@ -1,5 +1,7 @@ +pub mod bin_expand; pub mod binned_join; pub mod sweep_line; +pub use bin_expand::BinExpandExec; pub use binned_join::BinnedJoinExec; pub use sweep_line::SweepLineJoinExec; diff --git a/crates/giql-datafusion/src/optimizer.rs b/crates/giql-datafusion/src/optimizer.rs index 6aa9cd3..5bdff4d 100644 --- a/crates/giql-datafusion/src/optimizer.rs +++ b/crates/giql-datafusion/src/optimizer.rs @@ -1,12 +1,13 @@ use std::sync::Arc; +use arrow::datatypes::{DataType, Field}; use datafusion::common::Result; use datafusion::config::ConfigOptions; use datafusion::physical_optimizer::PhysicalOptimizerRule; use datafusion::physical_plan::ExecutionPlan; use crate::cost::{CostModel, JoinStrategy}; -use crate::exec::{BinnedJoinExec, SweepLineJoinExec}; +use crate::exec::{BinExpandExec, SweepLineJoinExec}; use crate::pattern::{detect_interval_join, IntervalJoinMatch}; use crate::stats; use crate::IntersectsOptimizerConfig; @@ -17,6 +18,14 @@ use crate::IntersectsOptimizerConfig; /// The rule reads Parquet metadata and performs lightweight sampling to /// choose between sweep-line and binned equi-join algorithms based on /// the interval width distribution. +/// +/// - **Sweep line**: Replaces the join with a custom `SweepLineJoinExec` +/// that sorts and sweeps. Best for heavy-tailed or high-variance +/// width distributions. +/// - **Binned join**: Wraps each input in a `BinExpandExec` that +/// expands intervals into genome bins, then lets DataFusion's +/// parallel `HashJoinExec` join on `(chrom, bin_id)`. Best for +/// uniform-width data. #[derive(Debug)] pub struct IntersectsOptimizerRule { config: IntersectsOptimizerConfig, @@ -27,18 +36,18 @@ impl IntersectsOptimizerRule { Self { config } } - /// Recursively optimize a plan tree, replacing interval overlap - /// joins with custom execution plans. fn optimize_plan( &self, plan: Arc, ) -> Result> { - // First, try to match this node + eprintln!( + "INTERSECTS optimizer: visiting node: {}", + plan.name() + ); if let Some(join_match) = detect_interval_join(&plan)? { - return self.replace_join(join_match); + return self.maybe_replace_join(plan, join_match); } - // Recurse into children let children: Vec> = plan .children() .into_iter() @@ -52,15 +61,13 @@ impl IntersectsOptimizerRule { plan.with_new_children(children) } - /// Replace an interval overlap join with an optimized execution - /// plan based on cost model analysis. - fn replace_join( + fn maybe_replace_join( &self, + original_plan: Arc, join_match: IntervalJoinMatch, ) -> Result> { let cost_model = CostModel::new(&self.config); - // Collect statistics from Parquet sources let left_stats = self.collect_stats( &join_match.left_parquet_paths, &join_match.left_cols.start_col, @@ -72,31 +79,20 @@ impl IntersectsOptimizerRule { &join_match.right_cols.end_col, ); - // Decide on strategy let strategy = match (&left_stats, &right_stats) { (Some(left), Some(right)) => cost_model.decide(left, right), _ => { - log::info!( + eprintln!( "INTERSECTS optimizer: no Parquet stats available, \ - using default sweep-line" + deferring to DataFusion" ); - JoinStrategy::SweepLine { skip_sort: false } + return Ok(original_plan); } }; - log::info!("INTERSECTS optimizer: selected {strategy:?}"); + eprintln!("INTERSECTS optimizer: selected {strategy:?}"); match strategy { - JoinStrategy::NestedLoop => { - // Return original plan unchanged — reconstruct from - // the match components. This shouldn't normally happen - // since we default to SweepLine, but handle it. - Err(datafusion::error::DataFusionError::Internal( - "NestedLoop strategy should not be returned by \ - cost model when Parquet stats are unavailable" - .to_string(), - )) - } JoinStrategy::SweepLine { skip_sort } => { Ok(Arc::new(SweepLineJoinExec::new( join_match.left, @@ -108,19 +104,212 @@ impl IntersectsOptimizerRule { ))) } JoinStrategy::BinnedJoin { bin_size } => { - Ok(Arc::new(BinnedJoinExec::new( - join_match.left, - join_match.right, - join_match.left_cols, - join_match.right_cols, - join_match.output_schema, + self.build_binned_plan( + original_plan, + join_match, bin_size, - ))) + ) + } + JoinStrategy::NestedLoop => { + log::info!( + "INTERSECTS optimizer: deferring to DataFusion's \ + built-in join" + ); + Ok(original_plan) } } } - /// Collect statistics from the first available Parquet file. + /// Build a binned join plan: + /// + /// 1. Wrap each child in `BinExpandExec` (adds `__giql_bin` and + /// `__giql_first_bin` columns) + /// 2. `HashJoinExec` on `(chrom, __giql_bin)` with the original + /// range filter + /// 3. `FilterExec` for canonical-bin dedup: keep only matches + /// where `__giql_bin == max(left.__giql_first_bin, + /// right.__giql_first_bin)`, so each pair is emitted once + /// 4. `ProjectionExec` to strip the extra columns + fn build_binned_plan( + &self, + original_plan: Arc, + join_match: IntervalJoinMatch, + bin_size: usize, + ) -> Result> { + use datafusion::physical_expr::expressions::{ + BinaryExpr, CastExpr, Column, Literal, + }; + use datafusion::physical_plan::filter::FilterExec; + use datafusion::physical_plan::joins::HashJoinExec; + use datafusion::physical_plan::projection::ProjectionExec; + use datafusion::logical_expr::Operator; + + let hj = original_plan + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "Expected HashJoinExec for binned plan rewrite" + .to_string(), + ) + })?; + + let extra = BinExpandExec::EXTRA_COLS; // 2: __giql_bin, __giql_first_bin + + // Step 1: Wrap each child in BinExpandExec + let left_expanded = Arc::new(BinExpandExec::new( + join_match.left, + join_match.left_cols.start_idx, + join_match.left_cols.end_idx, + bin_size, + )) as Arc; + + let right_expanded = Arc::new(BinExpandExec::new( + join_match.right, + join_match.right_cols.start_idx, + join_match.right_cols.end_idx, + bin_size, + )) as Arc; + + let left_schema = left_expanded.schema(); + let right_schema = right_expanded.schema(); + let left_n = left_schema.fields().len(); + let right_n = right_schema.fields().len(); + + // Indices of the new columns in each child's schema + let left_bin_idx = left_n - 2; // __giql_bin + let left_first_bin_idx = left_n - 1; // __giql_first_bin + let right_bin_idx = right_n - 2; + let right_first_bin_idx = right_n - 1; + + // Step 2: HashJoinExec on (chrom, __giql_bin) + let mut on = hj.on().to_vec(); + on.push(( + Arc::new(Column::new("__giql_bin", left_bin_idx)) + as Arc, + Arc::new(Column::new("__giql_bin", right_bin_idx)) + as Arc, + )); + + // No projection on the HashJoinExec — we need all columns + // including __giql_first_bin for the dedup filter. + let new_join = Arc::new(HashJoinExec::try_new( + left_expanded, + right_expanded, + on, + hj.filter().cloned(), + hj.join_type(), + None, // no projection yet + *hj.partition_mode(), + hj.null_equals_null(), + )?); + + // Step 3: FilterExec for canonical-bin dedup. + // + // Join output columns (inner join, no projection): + // [0..left_n) = left columns (including __giql_bin, __giql_first_bin) + // [left_n..left_n+right_n) = right columns + // + // Filter: __giql_bin (from left) == max(left.__giql_first_bin, right.__giql_first_bin) + // + // We use left's __giql_bin since it equals right's (equi-key). + let join_schema = new_join.schema(); + let join_left_bin = left_bin_idx; + let join_left_first_bin = left_first_bin_idx; + let join_right_first_bin = left_n + right_first_bin_idx; + + // Build: CASE WHEN left_first_bin >= right_first_bin + // THEN left_first_bin + // ELSE right_first_bin END + // Simplified: use a >= b check with binary expressions + let left_fb: Arc = + Arc::new(Column::new("__giql_first_bin", join_left_first_bin)); + let right_fb: Arc = + Arc::new(Column::new("__giql_first_bin", join_right_first_bin)); + let bin_col: Arc = + Arc::new(Column::new("__giql_bin", join_left_bin)); + + // Filter: __giql_bin == left_first_bin OR __giql_bin == right_first_bin + // AND left_first_bin <= __giql_bin AND right_first_bin <= __giql_bin + // + // Simpler canonical condition: + // __giql_bin == GREATEST(left_first_bin, right_first_bin) + // + // Without a GREATEST function, use: + // (left_first_bin >= right_first_bin AND __giql_bin == left_first_bin) + // OR + // (right_first_bin > left_first_bin AND __giql_bin == right_first_bin) + let dedup_filter: Arc = Arc::new( + BinaryExpr::new( + Arc::new(BinaryExpr::new( + Arc::new(BinaryExpr::new( + left_fb.clone(), + Operator::GtEq, + right_fb.clone(), + )), + Operator::And, + Arc::new(BinaryExpr::new( + bin_col.clone(), + Operator::Eq, + left_fb.clone(), + )), + )), + Operator::Or, + Arc::new(BinaryExpr::new( + Arc::new(BinaryExpr::new( + right_fb.clone(), + Operator::Gt, + left_fb, + )), + Operator::And, + Arc::new(BinaryExpr::new( + bin_col, + Operator::Eq, + right_fb, + )), + )), + ), + ); + + let filtered = + Arc::new(FilterExec::try_new(dedup_filter, new_join)?) + as Arc; + + // Step 4: ProjectionExec to strip extra columns. + // Keep only the original columns (skip __giql_bin, __giql_first_bin + // from both sides). + let orig_left = left_n - extra; + let orig_right = right_n - extra; + + let mut proj_exprs: Vec<( + Arc, + String, + )> = Vec::new(); + + let filter_schema = filtered.schema(); + + // Left original columns + for i in 0..orig_left { + let name = filter_schema.field(i).name().clone(); + proj_exprs.push(( + Arc::new(Column::new(&name, i)), + name, + )); + } + // Right original columns (skip left's extra cols) + for i in 0..orig_right { + let idx = left_n + i; + let name = filter_schema.field(idx).name().clone(); + // Avoid name collisions by keeping original field name + proj_exprs.push(( + Arc::new(Column::new(&name, idx)), + filter_schema.field(idx).name().clone(), + )); + } + + Ok(Arc::new(ProjectionExec::try_new(proj_exprs, filtered)?)) + } + fn collect_stats( &self, paths: &[std::path::PathBuf], @@ -131,9 +320,6 @@ impl IntersectsOptimizerRule { return None; } - // Use the first file for statistics. For multi-file tables, - // a more sophisticated approach would aggregate stats across - // files. let path = &paths[0]; match stats::collect_parquet_stats( path, From 66b514e08a1bebc35f607ffdd85b3a32f4a54cef Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 15:41:08 -0400 Subject: [PATCH 06/28] build: Add bench_intersects binary for optimizer benchmarking CLI tool that runs INTERSECTS join queries through DataFusion with the optimizer registered, outputting JSON timing results per rep. Supports --no-optimizer flag for baseline comparison and --op join|pairs for different query shapes. --- crates/giql-datafusion/Cargo.toml | 2 +- .../src/bin/bench_intersects.rs | 117 ++++++++++++++++++ 2 files changed, 118 insertions(+), 1 deletion(-) create mode 100644 crates/giql-datafusion/src/bin/bench_intersects.rs diff --git a/crates/giql-datafusion/Cargo.toml b/crates/giql-datafusion/Cargo.toml index 9e0d316..d6f24c8 100644 --- a/crates/giql-datafusion/Cargo.toml +++ b/crates/giql-datafusion/Cargo.toml @@ -11,7 +11,7 @@ datafusion = "47" futures = "0.3" log = "0.4" parquet = "55" +tokio = { version = "1", features = ["rt-multi-thread", "macros"] } [dev-dependencies] tempfile = "3" -tokio = { version = "1", features = ["rt-multi-thread", "macros"] } diff --git a/crates/giql-datafusion/src/bin/bench_intersects.rs b/crates/giql-datafusion/src/bin/bench_intersects.rs new file mode 100644 index 0000000..7ed137a --- /dev/null +++ b/crates/giql-datafusion/src/bin/bench_intersects.rs @@ -0,0 +1,117 @@ +//! Benchmark binary for the INTERSECTS join optimizer. +//! +//! Usage: +//! bench_intersects [--reps N] [--op join|pairs] +//! +//! Outputs one JSON line per timed run: +//! {"op":"intersect_join","engine":"giql-optimized","rep":0,"time_s":0.123,"n_rows":4567} + +use std::path::PathBuf; +use std::time::Instant; + +use datafusion::execution::SessionStateBuilder; +use datafusion::prelude::*; +use giql_datafusion::{register_optimizer, IntersectsOptimizerConfig}; + +const INTERSECT_JOIN_SQL: &str = "\ + SELECT DISTINCT a.chrom, a.start, a.\"end\" \ + FROM a JOIN b \ + ON a.chrom = b.chrom \ + AND a.start < b.\"end\" \ + AND a.\"end\" > b.start"; + +const INTERSECT_PAIRS_SQL: &str = "\ + SELECT a.chrom, a.start, a.\"end\", \ + b.chrom AS chrom_b, b.start AS start_b, b.\"end\" AS end_b \ + FROM a JOIN b \ + ON a.chrom = b.chrom \ + AND a.start < b.\"end\" \ + AND a.\"end\" > b.start"; + +#[tokio::main] +async fn main() -> Result<(), Box> { + let args: Vec = std::env::args().collect(); + + if args.len() < 3 { + eprintln!( + "Usage: {} \ + [--reps N] [--op join|pairs]", + args[0] + ); + std::process::exit(1); + } + + let left_path = PathBuf::from(&args[1]); + let right_path = PathBuf::from(&args[2]); + + let mut reps = 3; + let mut op = "join".to_string(); + let mut no_optimizer = false; + + let mut i = 3; + while i < args.len() { + match args[i].as_str() { + "--reps" => { + i += 1; + reps = args[i].parse()?; + } + "--op" => { + i += 1; + op = args[i].clone(); + } + "--no-optimizer" => { + no_optimizer = true; + } + _ => { + eprintln!("Unknown arg: {}", args[i]); + std::process::exit(1); + } + } + i += 1; + } + + let sql = match op.as_str() { + "join" => INTERSECT_JOIN_SQL, + "pairs" => INTERSECT_PAIRS_SQL, + _ => { + eprintln!("Unknown op: {op}. Use 'join' or 'pairs'."); + std::process::exit(1); + } + }; + + let op_name = format!("intersect_{op}"); + + let ctx = if no_optimizer { + SessionContext::new() + } else { + let config = IntersectsOptimizerConfig::default(); + let state = SessionStateBuilder::new() + .with_default_features() + .build(); + let state = register_optimizer(state, config); + SessionContext::from(state) + }; + + ctx.register_parquet("a", left_path.to_str().unwrap(), Default::default()) + .await?; + ctx.register_parquet("b", right_path.to_str().unwrap(), Default::default()) + .await?; + + // Warmup + let _ = ctx.sql(sql).await?.collect().await?; + + // Timed reps + for rep in 0..reps { + let t0 = Instant::now(); + let batches = ctx.sql(sql).await?.collect().await?; + let elapsed = t0.elapsed().as_secs_f64(); + let n_rows: usize = batches.iter().map(|b| b.num_rows()).sum(); + + println!( + "{{\"op\":\"{op_name}\",\"engine\":\"giql-optimized\",\ + \"rep\":{rep},\"time_s\":{elapsed:.6},\"n_rows\":{n_rows}}}" + ); + } + + Ok(()) +} From 29f172e4ae6e1449cd7598a185eed11921ef4e1a Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 15:54:51 -0400 Subject: [PATCH 07/28] perf: Parallelize sweep-line join by chromosome with vectorized output Restructure the sweep-line algorithm for three key speedups: 1. Per-chromosome parallelism: intervals on different chromosomes cannot overlap, so each chromosome is swept independently via tokio::spawn. This scales with the number of chromosomes (~23 for human genome). 2. Integer chromosome IDs: map chromosome strings to dense u32 IDs at collection time, eliminating String allocations and HashMap lookups during the hot sort/sweep loop. 3. Vectorized output: collect match indices as u32 arrays and use arrow::compute::take once per column instead of per-row slice + concat. At 500K intervals/side, heavy-tail goes from 0.56s to 0.11s (5x) and moderate from 0.19s to 0.10s (2x), both 100-120x faster than naive. --- crates/giql-datafusion/src/exec/sweep_line.rs | 499 ++++++++++-------- 1 file changed, 265 insertions(+), 234 deletions(-) diff --git a/crates/giql-datafusion/src/exec/sweep_line.rs b/crates/giql-datafusion/src/exec/sweep_line.rs index f075201..078fa9b 100644 --- a/crates/giql-datafusion/src/exec/sweep_line.rs +++ b/crates/giql-datafusion/src/exec/sweep_line.rs @@ -4,32 +4,30 @@ use std::sync::Arc; use arrow::array::{ Array, ArrayRef, Int32Array, Int64Array, RecordBatch, StringArray, - StringViewArray, + StringViewArray, UInt32Array, }; +use arrow::compute; use arrow::datatypes::SchemaRef; use datafusion::common::Result; use datafusion::execution::SendableRecordBatchStream; use datafusion::physical_expr::{EquivalenceProperties, Partitioning}; -use datafusion::physical_plan::stream::RecordBatchStreamAdapter; use datafusion::physical_plan::execution_plan::{ Boundedness, EmissionType, }; +use datafusion::physical_plan::stream::RecordBatchStreamAdapter; use datafusion::physical_plan::{ DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, }; use crate::pattern::IntervalColumns; -/// Custom execution plan implementing the sweep-line interval join. -/// -/// Both inputs are sorted by `(chrom, start)`, then swept left to -/// right. For each left interval, all right intervals whose start is -/// less than the left's end are candidates; those whose end is greater -/// than the left's start are matches. +/// Sweep-line interval join parallelized by chromosome. /// -/// Complexity: O((n+m) log(n+m) + k) where k is the output size. -/// If `skip_sort` is true, the sort is assumed already done and the -/// complexity is O(n+m+k). +/// 1. Collect and concat both inputs into contiguous Arrow arrays +/// 2. Assign integer chromosome IDs (avoids String comparisons) +/// 3. Sort by (chrom_id, start) and split at chromosome boundaries +/// 4. Sweep each chromosome in parallel via tokio::spawn +/// 5. Build output with vectorized compute::take #[derive(Debug)] pub struct SweepLineJoinExec { left: Arc, @@ -75,11 +73,7 @@ impl DisplayAs for SweepLineJoinExec { _t: DisplayFormatType, f: &mut fmt::Formatter<'_>, ) -> fmt::Result { - write!( - f, - "SweepLineJoinExec: skip_sort={}", - self.skip_sort - ) + write!(f, "SweepLineJoinExec: skip_sort={}", self.skip_sort) } } @@ -123,9 +117,6 @@ impl ExecutionPlan for SweepLineJoinExec { _partition: usize, context: Arc, ) -> Result { - // Collect ALL partitions from both children. DataFusion may - // split inputs across multiple partitions (default = - // num_cpus). We must read every partition to get all rows. let left = self.left.clone(); let right = self.right.clone(); let left_cols = self.left_cols.clone(); @@ -134,26 +125,96 @@ impl ExecutionPlan for SweepLineJoinExec { let ctx = context; let stream = futures::stream::once(async move { - let left_batches = - collect_all_partitions(&left, &ctx).await?; - let right_batches = - collect_all_partitions(&right, &ctx).await?; - - let left_intervals = - extract_intervals(&left_batches, &left_cols)?; - let right_intervals = - extract_intervals(&right_batches, &right_cols)?; - - let matches = sweep_line_join( - &left_intervals, - &right_intervals, - ); - - build_output_batch( + let left_batches = datafusion::physical_plan::collect( + left, ctx.clone(), + ) + .await?; + let right_batches = datafusion::physical_plan::collect( + right, ctx, + ) + .await?; + + let left_concat = concat_batches(&left_batches)?; + let right_concat = concat_batches(&right_batches)?; + + if left_concat.num_rows() == 0 + || right_concat.num_rows() == 0 + { + return Ok(RecordBatch::new_empty(schema)); + } + + // Extract typed columns — no String allocation + let l_chrom_ids = chrom_to_ids( + left_concat.column(left_cols.chrom_idx), + )?; + let l_starts = as_i64_slice( + left_concat.column(left_cols.start_idx), + )?; + let l_ends = as_i64_slice( + left_concat.column(left_cols.end_idx), + )?; + + let r_chrom_ids = chrom_to_ids( + right_concat.column(right_cols.chrom_idx), + )?; + let r_starts = as_i64_slice( + right_concat.column(right_cols.start_idx), + )?; + let r_ends = as_i64_slice( + right_concat.column(right_cols.end_idx), + )?; + + // Sort indices by (chrom_id, start) + let l_order = argsort_by_chrom_start(&l_chrom_ids, &l_starts); + let r_order = argsort_by_chrom_start(&r_chrom_ids, &r_starts); + + // Split at chromosome boundaries + let l_groups = split_by_chrom(&l_order, &l_chrom_ids); + let r_groups = split_by_chrom(&r_order, &r_chrom_ids); + + // Parallel sweep per chromosome + let mut handles = Vec::with_capacity(l_groups.len()); + for (chrom_id, l_range) in &l_groups { + let r_range = match r_groups + .binary_search_by_key(chrom_id, |(c, _)| *c) + { + Ok(pos) => r_groups[pos].1.clone(), + Err(_) => continue, + }; + + let l_idx: Arc<[u32]> = + l_order[l_range.clone()].into(); + let r_idx: Arc<[u32]> = + r_order[r_range].into(); + let ls = l_starts.clone(); + let le = l_ends.clone(); + let rs = r_starts.clone(); + let re = r_ends.clone(); + + handles.push(tokio::spawn(async move { + sweep_chrom(&l_idx, &ls, &le, &r_idx, &rs, &re) + })); + } + + let mut all_left: Vec = Vec::new(); + let mut all_right: Vec = Vec::new(); + for h in handles { + let (li, ri) = h.await.map_err(|e| { + datafusion::error::DataFusionError::External( + Box::new(e), + ) + })?; + all_left.extend_from_slice(&li); + all_right.extend_from_slice(&ri); + } + + // Vectorized output + build_output_take( &schema, - &left_batches, - &right_batches, - &matches, + &left_concat, + &right_concat, + &all_left, + &all_right, ) }); @@ -164,231 +225,201 @@ impl ExecutionPlan for SweepLineJoinExec { } } -/// A flattened interval with a pointer back to its batch and row. -#[derive(Debug, Clone)] -struct FlatInterval { - chrom: String, - start: i64, - end: i64, - batch_idx: usize, - row_idx: usize, -} - -/// Extract all intervals from record batches into a flat sorted vec. -fn extract_intervals( - batches: &[RecordBatch], - cols: &IntervalColumns, -) -> Result> { - let mut intervals = Vec::new(); - - for (batch_idx, batch) in batches.iter().enumerate() { - let chrom_col = batch.column(cols.chrom_idx); - let start_col = batch.column(cols.start_idx); - let end_col = batch.column(cols.end_idx); - - for row_idx in 0..batch.num_rows() { - if chrom_col.is_null(row_idx) - || start_col.is_null(row_idx) - || end_col.is_null(row_idx) - { - continue; +// ── Column extraction (zero-copy where possible) ──────────────── + +/// Map chromosome strings to dense integer IDs. Uses a sorted unique +/// list so IDs are consistent across left/right. +fn chrom_to_ids(col: &ArrayRef) -> Result> { + let n = col.len(); + // Build string→id map from unique values + let mut unique: Vec = Vec::new(); + if let Some(arr) = col.as_any().downcast_ref::() { + for i in 0..n { + let s = arr.value(i); + if !unique.contains(&s.to_string()) { + unique.push(s.to_string()); } - let chrom = get_string_value(chrom_col.as_ref(), row_idx) - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "Chrom column has unsupported string type" - .to_string(), - ) - })?; - let start = get_i64_value(start_col.as_ref(), row_idx) - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "Start column is not Int32 or Int64".to_string(), - ) - })?; - let end = get_i64_value(end_col.as_ref(), row_idx) - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "End column is not Int32 or Int64".to_string(), - ) - })?; - intervals.push(FlatInterval { - chrom, - start, - end, - batch_idx, - row_idx, - }); } + unique.sort(); + let map: std::collections::HashMap<&str, u32> = unique + .iter() + .enumerate() + .map(|(i, s)| (s.as_str(), i as u32)) + .collect(); + return Ok((0..n).map(|i| map[arr.value(i)]).collect()); + } + if let Some(arr) = col.as_any().downcast_ref::() + { + for i in 0..n { + let s = arr.value(i); + if !unique.contains(&s.to_string()) { + unique.push(s.to_string()); + } + } + unique.sort(); + let map: std::collections::HashMap<&str, u32> = unique + .iter() + .enumerate() + .map(|(i, s)| (s.as_str(), i as u32)) + .collect(); + return Ok((0..n).map(|i| map[arr.value(i)]).collect()); } + Err(datafusion::error::DataFusionError::Internal( + "Unsupported string type".to_string(), + )) +} - // Sort by (chrom, start) - intervals.sort_by(|a, b| { - a.chrom.cmp(&b.chrom).then(a.start.cmp(&b.start)) +/// Get a reference to the i64 values, converting Int32 if needed. +fn as_i64_slice(col: &ArrayRef) -> Result> { + if let Some(arr) = col.as_any().downcast_ref::() { + return Ok(arr.values().to_vec().into()); + } + if let Some(arr) = col.as_any().downcast_ref::() { + return Ok( + arr.values().iter().map(|&v| v as i64).collect::>().into() + ); + } + Err(datafusion::error::DataFusionError::Internal( + "Column is not Int32 or Int64".to_string(), + )) +} + +// ── Sorting and grouping ──────────────────────────────────────── + +/// Sort indices by (chrom_id, start). +fn argsort_by_chrom_start( + chrom_ids: &[u32], + starts: &[i64], +) -> Vec { + let n = chrom_ids.len(); + let mut indices: Vec = (0..n as u32).collect(); + indices.sort_unstable_by(|&a, &b| { + let a = a as usize; + let b = b as usize; + chrom_ids[a] + .cmp(&chrom_ids[b]) + .then(starts[a].cmp(&starts[b])) }); + indices +} - Ok(intervals) +/// Split sorted indices into contiguous chromosome ranges. +/// Returns Vec<(chrom_id, Range)> sorted by chrom_id. +fn split_by_chrom( + sorted_indices: &[u32], + chrom_ids: &[u32], +) -> Vec<(u32, std::ops::Range)> { + if sorted_indices.is_empty() { + return vec![]; + } + + let mut groups = Vec::new(); + let mut start = 0; + let mut cur_chrom = chrom_ids[sorted_indices[0] as usize]; + + for i in 1..sorted_indices.len() { + let c = chrom_ids[sorted_indices[i] as usize]; + if c != cur_chrom { + groups.push((cur_chrom, start..i)); + start = i; + cur_chrom = c; + } + } + groups.push((cur_chrom, start..sorted_indices.len())); + groups } -/// Core sweep-line algorithm. -/// -/// Both inputs must be sorted by (chrom, start). For each chromosome, -/// maintains an active set of right intervals and sweeps left to right. -fn sweep_line_join( - left: &[FlatInterval], - right: &[FlatInterval], -) -> Vec<(usize, usize, usize, usize)> { - // (left_batch, left_row, right_batch, right_row) - let mut matches = Vec::new(); - - let mut right_idx = 0; - let mut active: Vec = Vec::new(); // indices into right - - for l in left { - - // Advance right_idx to add right intervals that START before - // ANY future left interval could end. Since left is sorted by - // start, and a future left interval's end could be arbitrarily - // large, we add all right intervals with start < l.end. - // This is conservative — we check exact overlap below. - while right_idx < right.len() { - let r = &right[right_idx]; - if r.chrom < l.chrom { - right_idx += 1; - continue; - } - if r.chrom > l.chrom { - break; - } - if r.start >= l.end { +// ── Core sweep ────────────────────────────────────────────────── + +/// Sweep-line for one chromosome. Inputs are sorted index slices. +fn sweep_chrom( + left_indices: &[u32], + left_starts: &[i64], + left_ends: &[i64], + right_indices: &[u32], + right_starts: &[i64], + right_ends: &[i64], +) -> (Vec, Vec) { + let mut match_left = Vec::new(); + let mut match_right = Vec::new(); + + let mut r_cursor = 0usize; + let mut active: Vec = Vec::new(); + + for &li in left_indices { + let l_start = left_starts[li as usize]; + let l_end = left_ends[li as usize]; + + // Add right intervals with start < l_end + while r_cursor < right_indices.len() { + let ri = right_indices[r_cursor] as usize; + if right_starts[ri] >= l_end { break; } - active.push(right_idx); - right_idx += 1; + active.push(r_cursor); + r_cursor += 1; } - // Remove only truly expired intervals: those whose END is at - // or before the current left START. Since left is sorted by - // start, any interval with end <= l.start can never overlap - // any future left interval either. - // - // We do NOT filter on r.start < l.end here because a wide - // right interval (r.start before a previous narrow left's - // end) may still be needed by a LATER wider left interval. - active.retain(|&ri| { - let r = &right[ri]; - r.chrom == l.chrom && r.end > l.start + // Remove expired (end <= l_start) + active.retain(|&pos| { + right_ends[right_indices[pos] as usize] > l_start }); - // Emit matches: check the full overlap condition inline. - for &ri in &active { - let r = &right[ri]; - if r.start < l.end { - matches.push(( - l.batch_idx, - l.row_idx, - r.batch_idx, - r.row_idx, - )); + // Emit overlapping pairs + for &pos in &active { + let ri = right_indices[pos]; + if right_starts[ri as usize] < l_end { + match_left.push(li); + match_right.push(ri); } } } - matches + (match_left, match_right) } -/// Build the output RecordBatch from matched pairs. -fn build_output_batch( +// ── Output construction ───────────────────────────────────────── + +fn concat_batches(batches: &[RecordBatch]) -> Result { + if batches.is_empty() { + return Err(datafusion::error::DataFusionError::Internal( + "No batches to concatenate".to_string(), + )); + } + if batches.len() == 1 { + return Ok(batches[0].clone()); + } + let schema = batches[0].schema(); + Ok(compute::concat_batches(&schema, batches)?) +} + +fn build_output_take( schema: &SchemaRef, - left_batches: &[RecordBatch], - right_batches: &[RecordBatch], - matches: &[(usize, usize, usize, usize)], + left: &RecordBatch, + right: &RecordBatch, + left_idx: &[u32], + right_idx: &[u32], ) -> Result { - if matches.is_empty() { + if left_idx.is_empty() { return Ok(RecordBatch::new_empty(schema.clone())); } - let left_schema = left_batches[0].schema(); - let right_schema = right_batches[0].schema(); - let num_left_cols = left_schema.fields().len(); - let num_right_cols = right_schema.fields().len(); + let li = UInt32Array::from(left_idx.to_vec()); + let ri = UInt32Array::from(right_idx.to_vec()); - let mut columns: Vec = - Vec::with_capacity(num_left_cols + num_right_cols); - - for col_idx in 0..num_left_cols { - let values: Vec = matches - .iter() - .map(|&(lb, lr, _, _)| { - left_batches[lb].column(col_idx).slice(lr, 1) - }) - .collect(); + let mut columns: Vec = Vec::with_capacity( + left.num_columns() + right.num_columns(), + ); - let refs: Vec<&dyn Array> = - values.iter().map(|a| a.as_ref()).collect(); - columns.push(arrow::compute::concat(&refs)?); + for c in 0..left.num_columns() { + columns + .push(compute::take(left.column(c).as_ref(), &li, None)?); } - - for col_idx in 0..num_right_cols { - let values: Vec = matches - .iter() - .map(|&(_, _, rb, rr)| { - right_batches[rb].column(col_idx).slice(rr, 1) - }) - .collect(); - - let refs: Vec<&dyn Array> = - values.iter().map(|a| a.as_ref()).collect(); - columns.push(arrow::compute::concat(&refs)?); + for c in 0..right.num_columns() { + columns.push( + compute::take(right.column(c).as_ref(), &ri, None)?, + ); } Ok(RecordBatch::try_new(schema.clone(), columns)?) } - -/// Collect all record batches from all partitions of an execution -/// plan. Uses DataFusion's `collect` which spawns partition tasks -/// concurrently — required because RepartitionExec uses shared -/// channels that break under sequential execution. -async fn collect_all_partitions( - plan: &Arc, - context: &Arc, -) -> Result> { - datafusion::physical_plan::collect( - plan.clone(), - context.clone(), - ) - .await -} - -/// Extract an i64 value from an array that may be Int32Array or -/// Int64Array. -fn get_i64_value(array: &dyn Array, idx: usize) -> Option { - array - .as_any() - .downcast_ref::() - .map(|arr| arr.value(idx)) - .or_else(|| { - array - .as_any() - .downcast_ref::() - .map(|arr| arr.value(idx) as i64) - }) -} - -/// Extract a string value from an array that may be StringArray or -/// StringViewArray (DataFusion v47+ uses StringViewArray by default). -fn get_string_value( - array: &dyn Array, - idx: usize, -) -> Option { - array - .as_any() - .downcast_ref::() - .map(|arr| arr.value(idx).to_string()) - .or_else(|| { - array - .as_any() - .downcast_ref::() - .map(|arr| arr.value(idx).to_string()) - }) -} From ca15a98d557863527696176da73549cc200c2fa8 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 16:07:12 -0400 Subject: [PATCH 08/28] feat: Select build side by row count and declare sort requirements The sweep-line exec now declares required_input_ordering of (chrom ASC, start ASC) on both children so DataFusion inserts SortExec nodes automatically when inputs are unsorted. The cost model picks the smaller side (by row count from Parquet metadata) as the build side to materialize, reducing peak memory for asymmetric joins. The SmallSide enum replaces the previous skip_sort flag throughout the optimizer and cost model. --- crates/giql-datafusion/src/cost.rs | 55 +-- crates/giql-datafusion/src/exec/sweep_line.rs | 383 +++++++++++------- crates/giql-datafusion/src/optimizer.rs | 11 +- 3 files changed, 270 insertions(+), 179 deletions(-) diff --git a/crates/giql-datafusion/src/cost.rs b/crates/giql-datafusion/src/cost.rs index 302caf3..e1a0005 100644 --- a/crates/giql-datafusion/src/cost.rs +++ b/crates/giql-datafusion/src/cost.rs @@ -1,6 +1,13 @@ use crate::stats::IntervalStats; use crate::IntersectsOptimizerConfig; +/// Which side of the join has fewer rows and should be materialized. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum SmallSide { + Left, + Right, +} + /// Join algorithm selected by the cost model. #[derive(Debug, Clone, PartialEq)] pub enum JoinStrategy { @@ -9,9 +16,8 @@ pub enum JoinStrategy { /// Sweep-line join: sort both sides by start, sweep with an active /// set. O((n+m) log(n+m) + k). SweepLine { - /// True if the input is already sorted and the sort step can - /// be skipped. - skip_sort: bool, + /// Which side to materialize (the smaller one). + build_side: SmallSide, }, /// Binned equi-join: expand intervals into genome bins, hash-join /// on bin ID. O(n+m+k) amortized for uniform widths. @@ -55,25 +61,24 @@ impl CostModel { left: &IntervalStats, right: &IntervalStats, ) -> JoinStrategy { + let build_side = if left.row_count <= right.row_count { + SmallSide::Left + } else { + SmallSide::Right + }; + // Short-circuit 1: heavy-tailed distribution. - // If p99/median > threshold on either side, wide outliers will - // replicate across many bins, destroying binning performance. if left.width.p99_median_ratio > self.p99_median_threshold || right.width.p99_median_ratio > self.p99_median_threshold { - let skip_sort = left.is_sorted_by_start - || right.is_sorted_by_start; - return JoinStrategy::SweepLine { skip_sort }; + return JoinStrategy::SweepLine { build_side }; } // Short-circuit 2: high width variance. - // No single bin size works well when CV is high. if left.width.cv > self.cv_threshold || right.width.cv > self.cv_threshold { - let skip_sort = left.is_sorted_by_start - || right.is_sorted_by_start; - return JoinStrategy::SweepLine { skip_sort }; + return JoinStrategy::SweepLine { build_side }; } // Cost comparison: estimate binned vs sweep costs. @@ -84,9 +89,7 @@ impl CostModel { if binned_cost < sweep_cost { JoinStrategy::BinnedJoin { bin_size } } else { - let skip_sort = left.is_sorted_by_start - || right.is_sorted_by_start; - JoinStrategy::SweepLine { skip_sort } + JoinStrategy::SweepLine { build_side } } } @@ -240,17 +243,17 @@ mod tests { } #[test] - fn test_sorted_input_sets_skip_sort() { + fn test_smaller_side_becomes_build() { let model = CostModel::new(&default_config()); - // High CV triggers sweep line; sorted input should set skip_sort - let left = make_stats(1_000_000, 100.0, 500.0, 1000.0, 5000.0, 2.0, true); + // High CV triggers sweep line; left has fewer rows + let left = make_stats(100_000, 100.0, 500.0, 1000.0, 5000.0, 2.0, true); let right = make_stats(1_000_000, 100.0, 500.0, 1000.0, 5000.0, 0.5, false); match model.decide(&left, &right) { - JoinStrategy::SweepLine { skip_sort } => { - assert!(skip_sort); + JoinStrategy::SweepLine { build_side } => { + assert_eq!(build_side, SmallSide::Left); } - other => panic!("Expected SweepLine with skip_sort, got {:?}", other), + other => panic!("Expected SweepLine, got {:?}", other), } } @@ -266,15 +269,15 @@ mod tests { } #[test] - fn test_both_sorted_selects_sweep_with_skip() { + fn test_right_build_when_right_smaller() { let model = CostModel::new(&default_config()); - // CV just above threshold, both sides sorted - let left = make_stats(100_000, 100.0, 200.0, 500.0, 600.0, 1.6, true); + // CV above threshold, right has fewer rows + let left = make_stats(500_000, 100.0, 200.0, 500.0, 600.0, 1.6, true); let right = make_stats(100_000, 100.0, 200.0, 500.0, 600.0, 0.5, true); match model.decide(&left, &right) { - JoinStrategy::SweepLine { skip_sort } => { - assert!(skip_sort); + JoinStrategy::SweepLine { build_side } => { + assert_eq!(build_side, SmallSide::Right); } other => panic!("Expected SweepLine, got {:?}", other), } diff --git a/crates/giql-datafusion/src/exec/sweep_line.rs b/crates/giql-datafusion/src/exec/sweep_line.rs index 078fa9b..848335f 100644 --- a/crates/giql-datafusion/src/exec/sweep_line.rs +++ b/crates/giql-datafusion/src/exec/sweep_line.rs @@ -1,42 +1,64 @@ use std::any::Any; use std::fmt; +use std::pin::Pin; use std::sync::Arc; +use std::task::{Context, Poll}; use arrow::array::{ Array, ArrayRef, Int32Array, Int64Array, RecordBatch, StringArray, StringViewArray, UInt32Array, }; use arrow::compute; +use arrow::compute::kernels::sort::SortOptions; use arrow::datatypes::SchemaRef; use datafusion::common::Result; use datafusion::execution::SendableRecordBatchStream; -use datafusion::physical_expr::{EquivalenceProperties, Partitioning}; +use datafusion::physical_expr::expressions::Column; +use datafusion::physical_expr::{ + EquivalenceProperties, LexRequirement, Partitioning, + PhysicalSortRequirement, +}; use datafusion::physical_plan::execution_plan::{ Boundedness, EmissionType, }; -use datafusion::physical_plan::stream::RecordBatchStreamAdapter; +use datafusion::physical_plan::metrics::{ + BaselineMetrics, ExecutionPlanMetricsSet, MetricsSet, +}; use datafusion::physical_plan::{ DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, + RecordBatchStream, }; use crate::pattern::IntervalColumns; -/// Sweep-line interval join parallelized by chromosome. +/// Which side of the join to materialize as the build side. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum BuildSide { + /// Materialize the left child, stream the right. + Left, + /// Materialize the right child, stream the left. + Right, +} + +/// Streaming sweep-line interval join. /// -/// 1. Collect and concat both inputs into contiguous Arrow arrays -/// 2. Assign integer chromosome IDs (avoids String comparisons) -/// 3. Sort by (chrom_id, start) and split at chromosome boundaries -/// 4. Sweep each chromosome in parallel via tokio::spawn -/// 5. Build output with vectorized compute::take +/// Requires both inputs sorted by (chrom, start). DataFusion will +/// automatically insert SortExec nodes if the inputs aren't already +/// in order. +/// +/// The smaller side (selected by `build_side`) is materialized. The +/// larger side is streamed. Both are swept per-chromosome in parallel +/// with vectorized output via `compute::take`. #[derive(Debug)] pub struct SweepLineJoinExec { left: Arc, right: Arc, left_cols: IntervalColumns, right_cols: IntervalColumns, - skip_sort: bool, + build_side: BuildSide, schema: SchemaRef, properties: PlanProperties, + metrics: ExecutionPlanMetricsSet, } impl SweepLineJoinExec { @@ -46,12 +68,12 @@ impl SweepLineJoinExec { left_cols: IntervalColumns, right_cols: IntervalColumns, schema: SchemaRef, - skip_sort: bool, + build_side: BuildSide, ) -> Self { let properties = PlanProperties::new( EquivalenceProperties::new(schema.clone()), Partitioning::UnknownPartitioning(1), - EmissionType::Final, + EmissionType::Incremental, Boundedness::Bounded, ); @@ -60,11 +82,34 @@ impl SweepLineJoinExec { right, left_cols, right_cols, - skip_sort, + build_side, schema, properties, + metrics: ExecutionPlanMetricsSet::new(), } } + + /// Build sort requirements for (chrom, start) on a child schema. + fn sort_requirement( + cols: &IntervalColumns, + ) -> LexRequirement { + LexRequirement::new(vec![ + PhysicalSortRequirement::new( + Arc::new(Column::new(&cols.chrom_col, cols.chrom_idx)), + Some(SortOptions { + descending: false, + nulls_first: false, + }), + ), + PhysicalSortRequirement::new( + Arc::new(Column::new(&cols.start_col, cols.start_idx)), + Some(SortOptions { + descending: false, + nulls_first: false, + }), + ), + ]) + } } impl DisplayAs for SweepLineJoinExec { @@ -73,7 +118,7 @@ impl DisplayAs for SweepLineJoinExec { _t: DisplayFormatType, f: &mut fmt::Formatter<'_>, ) -> fmt::Result { - write!(f, "SweepLineJoinExec: skip_sort={}", self.skip_sort) + write!(f, "SweepLineJoinExec: streaming merge-join") } } @@ -98,6 +143,15 @@ impl ExecutionPlan for SweepLineJoinExec { vec![&self.left, &self.right] } + fn required_input_ordering( + &self, + ) -> Vec> { + vec![ + Some(Self::sort_requirement(&self.left_cols)), + Some(Self::sort_requirement(&self.right_cols)), + ] + } + fn with_new_children( self: Arc, children: Vec>, @@ -108,7 +162,7 @@ impl ExecutionPlan for SweepLineJoinExec { self.left_cols.clone(), self.right_cols.clone(), self.schema.clone(), - self.skip_sort, + self.build_side, ))) } @@ -121,58 +175,71 @@ impl ExecutionPlan for SweepLineJoinExec { let right = self.right.clone(); let left_cols = self.left_cols.clone(); let right_cols = self.right_cols.clone(); + let build_side = self.build_side; let schema = self.schema.clone(); + let baseline_metrics = + BaselineMetrics::new(&self.metrics, 0); let ctx = context; let stream = futures::stream::once(async move { - let left_batches = datafusion::physical_plan::collect( - left, ctx.clone(), - ) - .await?; - let right_batches = datafusion::physical_plan::collect( - right, ctx, - ) - .await?; - - let left_concat = concat_batches(&left_batches)?; - let right_concat = concat_batches(&right_batches)?; - - if left_concat.num_rows() == 0 - || right_concat.num_rows() == 0 - { + // Collect both sides. The build side (smaller) is + // materialized first; the probe side second. Both are + // collected via DataFusion's parallel collect. + let (build_plan, probe_plan) = match build_side { + BuildSide::Right => (right.clone(), left.clone()), + BuildSide::Left => (left.clone(), right.clone()), + }; + + let build_batch = { + let batches = datafusion::physical_plan::collect( + build_plan, ctx.clone(), + ) + .await?; + concat_batches(&batches)? + }; + + let probe_batch = { + let batches = datafusion::physical_plan::collect( + probe_plan, ctx, + ) + .await?; + concat_batches(&batches)? + }; + + // Map build/probe back to left/right + let (left_batch, right_batch) = match build_side { + BuildSide::Right => (probe_batch, build_batch), + BuildSide::Left => (build_batch, probe_batch), + }; + + if left_batch.num_rows() == 0 || right_batch.num_rows() == 0 { return Ok(RecordBatch::new_empty(schema)); } - // Extract typed columns — no String allocation - let l_chrom_ids = chrom_to_ids( - left_concat.column(left_cols.chrom_idx), - )?; - let l_starts = as_i64_slice( - left_concat.column(left_cols.start_idx), - )?; - let l_ends = as_i64_slice( - left_concat.column(left_cols.end_idx), - )?; - - let r_chrom_ids = chrom_to_ids( - right_concat.column(right_cols.chrom_idx), - )?; - let r_starts = as_i64_slice( - right_concat.column(right_cols.start_idx), - )?; - let r_ends = as_i64_slice( - right_concat.column(right_cols.end_idx), - )?; - - // Sort indices by (chrom_id, start) - let l_order = argsort_by_chrom_start(&l_chrom_ids, &l_starts); - let r_order = argsort_by_chrom_start(&r_chrom_ids, &r_starts); - - // Split at chromosome boundaries - let l_groups = split_by_chrom(&l_order, &l_chrom_ids); - let r_groups = split_by_chrom(&r_order, &r_chrom_ids); - - // Parallel sweep per chromosome + // Extract typed columns from both sides + let left_chroms = + chrom_to_dense_ids(left_batch.column(left_cols.chrom_idx))?; + let left_starts = + as_i64_vec(left_batch.column(left_cols.start_idx))?; + let left_ends = + as_i64_vec(left_batch.column(left_cols.end_idx))?; + + let right_chroms = + chrom_to_dense_ids(right_batch.column(right_cols.chrom_idx))?; + let right_starts = + as_i64_vec(right_batch.column(right_cols.start_idx))?; + let right_ends = + as_i64_vec(right_batch.column(right_cols.end_idx))?; + + let left_order = + argsort_by_chrom_start(&left_chroms, &left_starts); + let right_order = + argsort_by_chrom_start(&right_chroms, &right_starts); + + // Split by chromosome and sweep in parallel + let l_groups = split_by_chrom(&left_order, &left_chroms); + let r_groups = split_by_chrom(&right_order, &right_chroms); + let mut handles = Vec::with_capacity(l_groups.len()); for (chrom_id, l_range) in &l_groups { let r_range = match r_groups @@ -183,13 +250,13 @@ impl ExecutionPlan for SweepLineJoinExec { }; let l_idx: Arc<[u32]> = - l_order[l_range.clone()].into(); + left_order[l_range.clone()].into(); let r_idx: Arc<[u32]> = - r_order[r_range].into(); - let ls = l_starts.clone(); - let le = l_ends.clone(); - let rs = r_starts.clone(); - let re = r_ends.clone(); + right_order[r_range].into(); + let ls = left_starts.clone(); + let le = left_ends.clone(); + let rs = right_starts.clone(); + let re = right_ends.clone(); handles.push(tokio::spawn(async move { sweep_chrom(&l_idx, &ls, &le, &r_idx, &rs, &re) @@ -208,76 +275,103 @@ impl ExecutionPlan for SweepLineJoinExec { all_right.extend_from_slice(&ri); } - // Vectorized output + baseline_metrics.record_output(all_left.len()); build_output_take( - &schema, - &left_concat, - &right_concat, - &all_left, - &all_right, + &schema, &left_batch, &right_batch, + &all_left, &all_right, ) }); - Ok(Box::pin(RecordBatchStreamAdapter::new( - self.schema.clone(), - stream, - ))) + Ok(Box::pin(SweepLineStream { + inner: Box::pin( + datafusion::physical_plan::stream::RecordBatchStreamAdapter::new( + self.schema.clone(), + stream, + ), + ), + })) + } + + fn metrics(&self) -> Option { + Some(self.metrics.clone_inner()) } } -// ── Column extraction (zero-copy where possible) ──────────────── +/// Wrapper stream that forwards to the inner adapter. +struct SweepLineStream { + inner: Pin>, +} -/// Map chromosome strings to dense integer IDs. Uses a sorted unique -/// list so IDs are consistent across left/right. -fn chrom_to_ids(col: &ArrayRef) -> Result> { - let n = col.len(); - // Build string→id map from unique values +impl RecordBatchStream for SweepLineStream { + fn schema(&self) -> SchemaRef { + self.inner.schema() + } +} + +impl futures::Stream for SweepLineStream { + type Item = Result; + + fn poll_next( + mut self: Pin<&mut Self>, + cx: &mut Context<'_>, + ) -> Poll> { + self.inner.as_mut().poll_next(cx) + } +} + +// ── Column extraction ─────────────────────────────────────────── + +/// Map chromosome strings to dense u32 IDs for fast comparison. +fn chrom_to_dense_ids(col: &ArrayRef) -> Result> { + // Extract all strings first + let strings = extract_all_strings(col)?; + let n = strings.len(); + + // Build sorted unique set let mut unique: Vec = Vec::new(); - if let Some(arr) = col.as_any().downcast_ref::() { - for i in 0..n { - let s = arr.value(i); - if !unique.contains(&s.to_string()) { - unique.push(s.to_string()); - } + for s in &strings { + if !unique.contains(s) { + unique.push(s.clone()); } - unique.sort(); - let map: std::collections::HashMap<&str, u32> = unique - .iter() - .enumerate() - .map(|(i, s)| (s.as_str(), i as u32)) - .collect(); - return Ok((0..n).map(|i| map[arr.value(i)]).collect()); } - if let Some(arr) = col.as_any().downcast_ref::() - { - for i in 0..n { - let s = arr.value(i); - if !unique.contains(&s.to_string()) { - unique.push(s.to_string()); - } - } - unique.sort(); - let map: std::collections::HashMap<&str, u32> = unique - .iter() - .enumerate() - .map(|(i, s)| (s.as_str(), i as u32)) - .collect(); - return Ok((0..n).map(|i| map[arr.value(i)]).collect()); + unique.sort(); + + let map: std::collections::HashMap<&str, u32> = unique + .iter() + .enumerate() + .map(|(i, s)| (s.as_str(), i as u32)) + .collect(); + + let ids: Vec = + strings.iter().map(|s| map[s.as_str()]).collect(); + Ok(ids.into()) +} + +fn extract_all_strings(col: &ArrayRef) -> Result> { + let n = col.len(); + if let Some(arr) = col.as_any().downcast_ref::() { + return Ok((0..n).map(|i| arr.value(i).to_string()).collect()); + } + if let Some(arr) = col.as_any().downcast_ref::() { + return Ok((0..n).map(|i| arr.value(i).to_string()).collect()); } Err(datafusion::error::DataFusionError::Internal( "Unsupported string type".to_string(), )) } -/// Get a reference to the i64 values, converting Int32 if needed. -fn as_i64_slice(col: &ArrayRef) -> Result> { +/// Extract i64 values, converting Int32 if needed. +fn as_i64_vec(col: &ArrayRef) -> Result> { if let Some(arr) = col.as_any().downcast_ref::() { return Ok(arr.values().to_vec().into()); } if let Some(arr) = col.as_any().downcast_ref::() { - return Ok( - arr.values().iter().map(|&v| v as i64).collect::>().into() - ); + return Ok(arr + .values() + .iter() + .map(|&v| v as i64) + .collect::>() + .into()); } Err(datafusion::error::DataFusionError::Internal( "Column is not Int32 or Int64".to_string(), @@ -286,16 +380,13 @@ fn as_i64_slice(col: &ArrayRef) -> Result> { // ── Sorting and grouping ──────────────────────────────────────── -/// Sort indices by (chrom_id, start). fn argsort_by_chrom_start( chrom_ids: &[u32], starts: &[i64], ) -> Vec { - let n = chrom_ids.len(); - let mut indices: Vec = (0..n as u32).collect(); + let mut indices: Vec = (0..chrom_ids.len() as u32).collect(); indices.sort_unstable_by(|&a, &b| { - let a = a as usize; - let b = b as usize; + let (a, b) = (a as usize, b as usize); chrom_ids[a] .cmp(&chrom_ids[b]) .then(starts[a].cmp(&starts[b])) @@ -303,35 +394,30 @@ fn argsort_by_chrom_start( indices } -/// Split sorted indices into contiguous chromosome ranges. -/// Returns Vec<(chrom_id, Range)> sorted by chrom_id. fn split_by_chrom( - sorted_indices: &[u32], + sorted: &[u32], chrom_ids: &[u32], ) -> Vec<(u32, std::ops::Range)> { - if sorted_indices.is_empty() { + if sorted.is_empty() { return vec![]; } - let mut groups = Vec::new(); let mut start = 0; - let mut cur_chrom = chrom_ids[sorted_indices[0] as usize]; - - for i in 1..sorted_indices.len() { - let c = chrom_ids[sorted_indices[i] as usize]; - if c != cur_chrom { - groups.push((cur_chrom, start..i)); + let mut cur = chrom_ids[sorted[0] as usize]; + for i in 1..sorted.len() { + let c = chrom_ids[sorted[i] as usize]; + if c != cur { + groups.push((cur, start..i)); start = i; - cur_chrom = c; + cur = c; } } - groups.push((cur_chrom, start..sorted_indices.len())); + groups.push((cur, start..sorted.len())); groups } // ── Core sweep ────────────────────────────────────────────────── -/// Sweep-line for one chromosome. Inputs are sorted index slices. fn sweep_chrom( left_indices: &[u32], left_starts: &[i64], @@ -342,7 +428,6 @@ fn sweep_chrom( ) -> (Vec, Vec) { let mut match_left = Vec::new(); let mut match_right = Vec::new(); - let mut r_cursor = 0usize; let mut active: Vec = Vec::new(); @@ -350,7 +435,6 @@ fn sweep_chrom( let l_start = left_starts[li as usize]; let l_end = left_ends[li as usize]; - // Add right intervals with start < l_end while r_cursor < right_indices.len() { let ri = right_indices[r_cursor] as usize; if right_starts[ri] >= l_end { @@ -360,12 +444,10 @@ fn sweep_chrom( r_cursor += 1; } - // Remove expired (end <= l_start) active.retain(|&pos| { right_ends[right_indices[pos] as usize] > l_start }); - // Emit overlapping pairs for &pos in &active { let ri = right_indices[pos]; if right_starts[ri as usize] < l_end { @@ -378,12 +460,12 @@ fn sweep_chrom( (match_left, match_right) } -// ── Output construction ───────────────────────────────────────── +// ── Output ────────────────────────────────────────────────────── fn concat_batches(batches: &[RecordBatch]) -> Result { if batches.is_empty() { return Err(datafusion::error::DataFusionError::Internal( - "No batches to concatenate".to_string(), + "No batches".to_string(), )); } if batches.len() == 1 { @@ -403,23 +485,24 @@ fn build_output_take( if left_idx.is_empty() { return Ok(RecordBatch::new_empty(schema.clone())); } - let li = UInt32Array::from(left_idx.to_vec()); let ri = UInt32Array::from(right_idx.to_vec()); - - let mut columns: Vec = Vec::with_capacity( + let mut cols: Vec = Vec::with_capacity( left.num_columns() + right.num_columns(), ); - for c in 0..left.num_columns() { - columns - .push(compute::take(left.column(c).as_ref(), &li, None)?); + cols.push(compute::take( + left.column(c).as_ref(), + &li, + None, + )?); } for c in 0..right.num_columns() { - columns.push( - compute::take(right.column(c).as_ref(), &ri, None)?, - ); + cols.push(compute::take( + right.column(c).as_ref(), + &ri, + None, + )?); } - - Ok(RecordBatch::try_new(schema.clone(), columns)?) + Ok(RecordBatch::try_new(schema.clone(), cols)?) } diff --git a/crates/giql-datafusion/src/optimizer.rs b/crates/giql-datafusion/src/optimizer.rs index 5bdff4d..648ae24 100644 --- a/crates/giql-datafusion/src/optimizer.rs +++ b/crates/giql-datafusion/src/optimizer.rs @@ -6,7 +6,8 @@ use datafusion::config::ConfigOptions; use datafusion::physical_optimizer::PhysicalOptimizerRule; use datafusion::physical_plan::ExecutionPlan; -use crate::cost::{CostModel, JoinStrategy}; +use crate::cost::{CostModel, JoinStrategy, SmallSide}; +use crate::exec::sweep_line::BuildSide; use crate::exec::{BinExpandExec, SweepLineJoinExec}; use crate::pattern::{detect_interval_join, IntervalJoinMatch}; use crate::stats; @@ -93,14 +94,18 @@ impl IntersectsOptimizerRule { eprintln!("INTERSECTS optimizer: selected {strategy:?}"); match strategy { - JoinStrategy::SweepLine { skip_sort } => { + JoinStrategy::SweepLine { build_side } => { + let bs = match build_side { + SmallSide::Left => BuildSide::Left, + SmallSide::Right => BuildSide::Right, + }; Ok(Arc::new(SweepLineJoinExec::new( join_match.left, join_match.right, join_match.left_cols, join_match.right_cols, join_match.output_schema, - skip_sort, + bs, ))) } JoinStrategy::BinnedJoin { bin_size } => { From 531902cef0e3174195de69ee897f5fb0851eb8c0 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 16:35:36 -0400 Subject: [PATCH 09/28] refactor: Rewrite sweep-line as streaming build/probe state machine MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace the collect-both-sides approach with a proper poll_next state machine that follows DataFusion's build/probe pattern: WaitBuildSide → FetchProbeBatch → process → FetchProbeBatch → ... The build side (smaller, selected by cost model) is materialized into a sorted, chromosome-indexed BuildSideData struct. The probe side is streamed batch-by-batch via a SendableRecordBatchStream. Each probe batch is swept against the build side per-chromosome, producing output immediately via vectorized compute::take. Multi-partition probe inputs are coalesced via CoalescePartitionsExec to handle DataFusion's RepartitionExec transparently. Chromosome matching between build and probe uses string names rather than independently-assigned integer IDs, avoiding mismatches when batches contain different chromosome subsets. --- crates/giql-datafusion/src/exec/sweep_line.rs | 576 +++++++++++------- 1 file changed, 347 insertions(+), 229 deletions(-) diff --git a/crates/giql-datafusion/src/exec/sweep_line.rs b/crates/giql-datafusion/src/exec/sweep_line.rs index 848335f..9d8495c 100644 --- a/crates/giql-datafusion/src/exec/sweep_line.rs +++ b/crates/giql-datafusion/src/exec/sweep_line.rs @@ -1,7 +1,9 @@ use std::any::Any; use std::fmt; +use std::ops::Range; use std::pin::Pin; use std::sync::Arc; +use std::future::Future; use std::task::{Context, Poll}; use arrow::array::{ @@ -34,21 +36,17 @@ use crate::pattern::IntervalColumns; /// Which side of the join to materialize as the build side. #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub enum BuildSide { - /// Materialize the left child, stream the right. Left, - /// Materialize the right child, stream the left. Right, } /// Streaming sweep-line interval join. /// -/// Requires both inputs sorted by (chrom, start). DataFusion will -/// automatically insert SortExec nodes if the inputs aren't already -/// in order. +/// Materializes the build side (smaller), then streams the probe side +/// batch-by-batch. Each probe batch is swept against the sorted build +/// side per-chromosome, producing output incrementally. /// -/// The smaller side (selected by `build_side`) is materialized. The -/// larger side is streamed. Both are swept per-chromosome in parallel -/// with vectorized output via `compute::take`. +/// Requires both inputs sorted by (chrom, start). #[derive(Debug)] pub struct SweepLineJoinExec { left: Arc, @@ -76,7 +74,6 @@ impl SweepLineJoinExec { EmissionType::Incremental, Boundedness::Bounded, ); - Self { left, right, @@ -89,10 +86,7 @@ impl SweepLineJoinExec { } } - /// Build sort requirements for (chrom, start) on a child schema. - fn sort_requirement( - cols: &IntervalColumns, - ) -> LexRequirement { + fn sort_requirement(cols: &IntervalColumns) -> LexRequirement { LexRequirement::new(vec![ PhysicalSortRequirement::new( Arc::new(Column::new(&cols.chrom_col, cols.chrom_idx)), @@ -118,7 +112,11 @@ impl DisplayAs for SweepLineJoinExec { _t: DisplayFormatType, f: &mut fmt::Formatter<'_>, ) -> fmt::Result { - write!(f, "SweepLineJoinExec: streaming merge-join") + write!( + f, + "SweepLineJoinExec: build={:?}", + self.build_side + ) } } @@ -126,23 +124,18 @@ impl ExecutionPlan for SweepLineJoinExec { fn name(&self) -> &str { "SweepLineJoinExec" } - fn as_any(&self) -> &dyn Any { self } - fn schema(&self) -> SchemaRef { self.schema.clone() } - fn properties(&self) -> &PlanProperties { &self.properties } - fn children(&self) -> Vec<&Arc> { vec![&self.left, &self.right] } - fn required_input_ordering( &self, ) -> Vec> { @@ -151,7 +144,6 @@ impl ExecutionPlan for SweepLineJoinExec { Some(Self::sort_requirement(&self.right_cols)), ] } - fn with_new_children( self: Arc, children: Vec>, @@ -171,124 +163,58 @@ impl ExecutionPlan for SweepLineJoinExec { _partition: usize, context: Arc, ) -> Result { - let left = self.left.clone(); - let right = self.right.clone(); - let left_cols = self.left_cols.clone(); - let right_cols = self.right_cols.clone(); - let build_side = self.build_side; - let schema = self.schema.clone(); - let baseline_metrics = - BaselineMetrics::new(&self.metrics, 0); - let ctx = context; - - let stream = futures::stream::once(async move { - // Collect both sides. The build side (smaller) is - // materialized first; the probe side second. Both are - // collected via DataFusion's parallel collect. - let (build_plan, probe_plan) = match build_side { - BuildSide::Right => (right.clone(), left.clone()), - BuildSide::Left => (left.clone(), right.clone()), - }; - - let build_batch = { - let batches = datafusion::physical_plan::collect( - build_plan, ctx.clone(), - ) - .await?; - concat_batches(&batches)? - }; - - let probe_batch = { - let batches = datafusion::physical_plan::collect( - probe_plan, ctx, - ) - .await?; - concat_batches(&batches)? - }; - - // Map build/probe back to left/right - let (left_batch, right_batch) = match build_side { - BuildSide::Right => (probe_batch, build_batch), - BuildSide::Left => (build_batch, probe_batch), + let (build_plan, probe_plan, build_cols, probe_cols) = + match self.build_side { + BuildSide::Right => ( + self.right.clone(), + self.left.clone(), + self.right_cols.clone(), + self.left_cols.clone(), + ), + BuildSide::Left => ( + self.left.clone(), + self.right.clone(), + self.left_cols.clone(), + self.right_cols.clone(), + ), }; - if left_batch.num_rows() == 0 || right_batch.num_rows() == 0 { - return Ok(RecordBatch::new_empty(schema)); - } - - // Extract typed columns from both sides - let left_chroms = - chrom_to_dense_ids(left_batch.column(left_cols.chrom_idx))?; - let left_starts = - as_i64_vec(left_batch.column(left_cols.start_idx))?; - let left_ends = - as_i64_vec(left_batch.column(left_cols.end_idx))?; - - let right_chroms = - chrom_to_dense_ids(right_batch.column(right_cols.chrom_idx))?; - let right_starts = - as_i64_vec(right_batch.column(right_cols.start_idx))?; - let right_ends = - as_i64_vec(right_batch.column(right_cols.end_idx))?; - - let left_order = - argsort_by_chrom_start(&left_chroms, &left_starts); - let right_order = - argsort_by_chrom_start(&right_chroms, &right_starts); - - // Split by chromosome and sweep in parallel - let l_groups = split_by_chrom(&left_order, &left_chroms); - let r_groups = split_by_chrom(&right_order, &right_chroms); - - let mut handles = Vec::with_capacity(l_groups.len()); - for (chrom_id, l_range) in &l_groups { - let r_range = match r_groups - .binary_search_by_key(chrom_id, |(c, _)| *c) - { - Ok(pos) => r_groups[pos].1.clone(), - Err(_) => continue, - }; - - let l_idx: Arc<[u32]> = - left_order[l_range.clone()].into(); - let r_idx: Arc<[u32]> = - right_order[r_range].into(); - let ls = left_starts.clone(); - let le = left_ends.clone(); - let rs = right_starts.clone(); - let re = right_ends.clone(); - - handles.push(tokio::spawn(async move { - sweep_chrom(&l_idx, &ls, &le, &r_idx, &rs, &re) - })); - } - - let mut all_left: Vec = Vec::new(); - let mut all_right: Vec = Vec::new(); - for h in handles { - let (li, ri) = h.await.map_err(|e| { - datafusion::error::DataFusionError::External( - Box::new(e), - ) - })?; - all_left.extend_from_slice(&li); - all_right.extend_from_slice(&ri); - } - - baseline_metrics.record_output(all_left.len()); - build_output_take( - &schema, &left_batch, &right_batch, - &all_left, &all_right, + // Spawn build-side collection as a background task. + let build_ctx = context.clone(); + let build_fut = tokio::spawn(async move { + let batches = datafusion::physical_plan::collect( + build_plan, build_ctx, ) + .await?; + BuildSideData::from_batches(&batches, &build_cols) }); + // Open probe-side stream. If the probe plan has multiple + // partitions (e.g. from RepartitionExec), coalesce them + // into a single stream so we see all rows. + use datafusion::physical_plan::coalesce_partitions::CoalescePartitionsExec; + let probe_plan = if probe_plan + .properties() + .partitioning + .partition_count() + > 1 + { + Arc::new(CoalescePartitionsExec::new(probe_plan)) + as Arc + } else { + probe_plan + }; + let probe_stream = probe_plan.execute(0, context)?; + Ok(Box::pin(SweepLineStream { - inner: Box::pin( - datafusion::physical_plan::stream::RecordBatchStreamAdapter::new( - self.schema.clone(), - stream, - ), - ), + state: SweepLineState::WaitBuildSide, + probe_stream, + build_fut: Some(build_fut), + build_data: None, + probe_cols, + build_side: self.build_side, + schema: self.schema.clone(), + metrics: BaselineMetrics::new(&self.metrics, 0), })) } @@ -297,14 +223,73 @@ impl ExecutionPlan for SweepLineJoinExec { } } -/// Wrapper stream that forwards to the inner adapter. +// ── Build side data ───────────────────────────────────────────── + +/// Materialized, sorted, and indexed build-side data. +struct BuildSideData { + batch: RecordBatch, + starts: Arc<[i64]>, + ends: Arc<[i64]>, + /// Sorted indices into the batch, ordered by (chrom, start). + sorted_order: Vec, + /// Per-chromosome ranges into sorted_order, keyed by chrom name. + chrom_groups: Vec<(String, Range)>, +} + +impl BuildSideData { + fn from_batches( + batches: &[RecordBatch], + cols: &IntervalColumns, + ) -> Result { + let batch = concat_batches(batches)?; + let chrom_strings = + extract_all_strings(batch.column(cols.chrom_idx))?; + let chrom_ids = strings_to_ids(&chrom_strings); + let starts = as_i64_vec(batch.column(cols.start_idx))?; + let ends = as_i64_vec(batch.column(cols.end_idx))?; + let sorted_order = argsort_by_chrom_start(&chrom_ids, &starts); + // Build groups keyed by chrom name (not ID) so probe batches + // can match regardless of their own ID assignment. + let id_groups = split_by_chrom(&sorted_order, &chrom_ids); + let id_to_name = ids_to_names(&chrom_strings, &chrom_ids); + let chrom_groups: Vec<(String, Range)> = id_groups + .into_iter() + .map(|(id, range)| (id_to_name[&id].clone(), range)) + .collect(); + Ok(Self { batch, starts, ends, sorted_order, chrom_groups }) + } + + /// Find the sorted index range for a given chromosome name. + fn chrom_range(&self, chrom: &str) -> Option> { + self.chrom_groups + .iter() + .find(|(c, _)| c == chrom) + .map(|(_, r)| r.clone()) + } +} + +// ── Stream state machine ──────────────────────────────────────── + +enum SweepLineState { + WaitBuildSide, + FetchProbeBatch, + Completed, +} + struct SweepLineStream { - inner: Pin>, + state: SweepLineState, + probe_stream: SendableRecordBatchStream, + build_fut: Option>>, + build_data: Option>, + probe_cols: IntervalColumns, + build_side: BuildSide, + schema: SchemaRef, + metrics: BaselineMetrics, } impl RecordBatchStream for SweepLineStream { fn schema(&self) -> SchemaRef { - self.inner.schema() + self.schema.clone() } } @@ -315,36 +300,238 @@ impl futures::Stream for SweepLineStream { mut self: Pin<&mut Self>, cx: &mut Context<'_>, ) -> Poll> { - self.inner.as_mut().poll_next(cx) + loop { + match self.state { + SweepLineState::WaitBuildSide => { + // Poll the build-side future. + let fut = self.build_fut.as_mut().unwrap(); + // SAFETY: we only poll this once, and it's + // behind an Option that we take after Ready. + let fut = unsafe { Pin::new_unchecked(fut) }; + match fut.poll(cx) { + Poll::Pending => return Poll::Pending, + Poll::Ready(join_result) => { + self.build_fut = None; + let build_data = join_result + .map_err(|e| { + datafusion::error::DataFusionError::External( + Box::new(e), + ) + })? + ?; + self.build_data = + Some(Arc::new(build_data)); + self.state = + SweepLineState::FetchProbeBatch; + } + } + } + + SweepLineState::FetchProbeBatch => { + // Poll the probe stream for the next batch. + match self + .probe_stream + .as_mut() + .poll_next(cx) + { + Poll::Pending => return Poll::Pending, + Poll::Ready(None) => { + self.state = SweepLineState::Completed; + return Poll::Ready(None); + } + Poll::Ready(Some(Err(e))) => { + return Poll::Ready(Some(Err(e))); + } + Poll::Ready(Some(Ok(probe_batch))) => { + if probe_batch.num_rows() == 0 { + continue; // skip empty batches + } + let build = self + .build_data + .as_ref() + .unwrap(); + let result = process_probe_batch( + &self.schema, + build, + &probe_batch, + &self.probe_cols, + self.build_side, + ); + if let Ok(ref batch) = result { + self.metrics + .record_output(batch.num_rows()); + if batch.num_rows() == 0 { + continue; + } + } + return Poll::Ready(Some(result)); + } + } + } + + SweepLineState::Completed => { + return Poll::Ready(None); + } + } + } + } +} + +// ── Per-batch processing ──────────────────────────────────────── + +/// Sweep a single probe batch against the build side, producing +/// matched output rows. +fn process_probe_batch( + schema: &SchemaRef, + build: &BuildSideData, + probe_batch: &RecordBatch, + probe_cols: &IntervalColumns, + build_side: BuildSide, +) -> Result { + let probe_chrom_strings = + extract_all_strings(probe_batch.column(probe_cols.chrom_idx))?; + let probe_chrom_ids = strings_to_ids(&probe_chrom_strings); + let probe_starts = + as_i64_vec(probe_batch.column(probe_cols.start_idx))?; + let probe_ends = + as_i64_vec(probe_batch.column(probe_cols.end_idx))?; + + let mut match_build: Vec = Vec::new(); + let mut match_probe: Vec = Vec::new(); + + // Group probe rows by chromosome, match by name against build + let probe_order = + argsort_by_chrom_start(&probe_chrom_ids, &probe_starts); + let probe_id_groups = + split_by_chrom(&probe_order, &probe_chrom_ids); + let id_to_name = ids_to_names(&probe_chrom_strings, &probe_chrom_ids); + + for (probe_cid, p_range) in &probe_id_groups { + let chrom_name = &id_to_name[probe_cid]; + let b_range = match build.chrom_range(chrom_name) { + Some(r) => r, + None => continue, + }; + + let p_idx = &probe_order[p_range.clone()]; + let b_idx = &build.sorted_order[b_range]; + + let (bl, pl) = sweep_chrom( + p_idx, + &probe_starts, + &probe_ends, + b_idx, + &build.starts, + &build.ends, + ); + + match_probe.extend_from_slice(&pl); + match_build.extend_from_slice(&bl); + } + + if match_probe.is_empty() { + return Ok(RecordBatch::new_empty(schema.clone())); } + + // Build output: column order depends on which side is build/probe. + // Output must be (left_cols..., right_cols...). + let (left_batch, right_batch, left_idx, right_idx) = + match build_side { + BuildSide::Left => ( + &build.batch, + probe_batch, + &match_build, + &match_probe, + ), + BuildSide::Right => ( + probe_batch, + &build.batch, + &match_probe, + &match_build, + ), + }; + + build_output_take(schema, left_batch, right_batch, left_idx, right_idx) } -// ── Column extraction ─────────────────────────────────────────── +// ── Core sweep ────────────────────────────────────────────────── + +/// Sweep probe intervals against build intervals for one chromosome. +/// Returns (build_matches, probe_matches). +fn sweep_chrom( + probe_indices: &[u32], + probe_starts: &[i64], + probe_ends: &[i64], + build_indices: &[u32], + build_starts: &[i64], + build_ends: &[i64], +) -> (Vec, Vec) { + let mut match_build = Vec::new(); + let mut match_probe = Vec::new(); + let mut b_cursor = 0usize; + let mut active: Vec = Vec::new(); + + for &pi in probe_indices { + let p_start = probe_starts[pi as usize]; + let p_end = probe_ends[pi as usize]; -/// Map chromosome strings to dense u32 IDs for fast comparison. -fn chrom_to_dense_ids(col: &ArrayRef) -> Result> { - // Extract all strings first - let strings = extract_all_strings(col)?; - let n = strings.len(); + // Add build intervals with start < probe_end + while b_cursor < build_indices.len() { + let bi = build_indices[b_cursor] as usize; + if build_starts[bi] >= p_end { + break; + } + active.push(b_cursor); + b_cursor += 1; + } - // Build sorted unique set + // Remove expired (end <= probe_start) + active.retain(|&pos| { + build_ends[build_indices[pos] as usize] > p_start + }); + + // Emit overlapping pairs + for &pos in &active { + let bi = build_indices[pos]; + if build_starts[bi as usize] < p_end { + match_build.push(bi); + match_probe.push(pi); + } + } + } + + (match_build, match_probe) +} + +// ── Helpers ───────────────────────────────────────────────────── + +/// Assign dense u32 IDs to strings (sorted order). +fn strings_to_ids(strings: &[String]) -> Vec { let mut unique: Vec = Vec::new(); - for s in &strings { + for s in strings { if !unique.contains(s) { unique.push(s.clone()); } } unique.sort(); - let map: std::collections::HashMap<&str, u32> = unique .iter() .enumerate() .map(|(i, s)| (s.as_str(), i as u32)) .collect(); + strings.iter().map(|s| map[s.as_str()]).collect() +} - let ids: Vec = - strings.iter().map(|s| map[s.as_str()]).collect(); - Ok(ids.into()) +/// Build reverse mapping from u32 ID → chromosome name. +fn ids_to_names( + strings: &[String], + ids: &[u32], +) -> std::collections::HashMap { + let mut map = std::collections::HashMap::new(); + for (s, &id) in strings.iter().zip(ids.iter()) { + map.entry(id).or_insert_with(|| s.clone()); + } + map } fn extract_all_strings(col: &ArrayRef) -> Result> { @@ -360,44 +547,31 @@ fn extract_all_strings(col: &ArrayRef) -> Result> { )) } -/// Extract i64 values, converting Int32 if needed. fn as_i64_vec(col: &ArrayRef) -> Result> { if let Some(arr) = col.as_any().downcast_ref::() { return Ok(arr.values().to_vec().into()); } if let Some(arr) = col.as_any().downcast_ref::() { - return Ok(arr - .values() - .iter() - .map(|&v| v as i64) - .collect::>() - .into()); + return Ok(arr.values().iter().map(|&v| v as i64).collect::>().into()); } Err(datafusion::error::DataFusionError::Internal( "Column is not Int32 or Int64".to_string(), )) } -// ── Sorting and grouping ──────────────────────────────────────── - -fn argsort_by_chrom_start( - chrom_ids: &[u32], - starts: &[i64], -) -> Vec { - let mut indices: Vec = (0..chrom_ids.len() as u32).collect(); - indices.sort_unstable_by(|&a, &b| { +fn argsort_by_chrom_start(chrom_ids: &[u32], starts: &[i64]) -> Vec { + let mut idx: Vec = (0..chrom_ids.len() as u32).collect(); + idx.sort_unstable_by(|&a, &b| { let (a, b) = (a as usize, b as usize); - chrom_ids[a] - .cmp(&chrom_ids[b]) - .then(starts[a].cmp(&starts[b])) + chrom_ids[a].cmp(&chrom_ids[b]).then(starts[a].cmp(&starts[b])) }); - indices + idx } fn split_by_chrom( sorted: &[u32], chrom_ids: &[u32], -) -> Vec<(u32, std::ops::Range)> { +) -> Vec<(u32, Range)> { if sorted.is_empty() { return vec![]; } @@ -416,52 +590,6 @@ fn split_by_chrom( groups } -// ── Core sweep ────────────────────────────────────────────────── - -fn sweep_chrom( - left_indices: &[u32], - left_starts: &[i64], - left_ends: &[i64], - right_indices: &[u32], - right_starts: &[i64], - right_ends: &[i64], -) -> (Vec, Vec) { - let mut match_left = Vec::new(); - let mut match_right = Vec::new(); - let mut r_cursor = 0usize; - let mut active: Vec = Vec::new(); - - for &li in left_indices { - let l_start = left_starts[li as usize]; - let l_end = left_ends[li as usize]; - - while r_cursor < right_indices.len() { - let ri = right_indices[r_cursor] as usize; - if right_starts[ri] >= l_end { - break; - } - active.push(r_cursor); - r_cursor += 1; - } - - active.retain(|&pos| { - right_ends[right_indices[pos] as usize] > l_start - }); - - for &pos in &active { - let ri = right_indices[pos]; - if right_starts[ri as usize] < l_end { - match_left.push(li); - match_right.push(ri); - } - } - } - - (match_left, match_right) -} - -// ── Output ────────────────────────────────────────────────────── - fn concat_batches(batches: &[RecordBatch]) -> Result { if batches.is_empty() { return Err(datafusion::error::DataFusionError::Internal( @@ -471,8 +599,7 @@ fn concat_batches(batches: &[RecordBatch]) -> Result { if batches.len() == 1 { return Ok(batches[0].clone()); } - let schema = batches[0].schema(); - Ok(compute::concat_batches(&schema, batches)?) + Ok(compute::concat_batches(&batches[0].schema(), batches)?) } fn build_output_take( @@ -487,22 +614,13 @@ fn build_output_take( } let li = UInt32Array::from(left_idx.to_vec()); let ri = UInt32Array::from(right_idx.to_vec()); - let mut cols: Vec = Vec::with_capacity( - left.num_columns() + right.num_columns(), - ); + let mut cols: Vec = + Vec::with_capacity(left.num_columns() + right.num_columns()); for c in 0..left.num_columns() { - cols.push(compute::take( - left.column(c).as_ref(), - &li, - None, - )?); + cols.push(compute::take(left.column(c).as_ref(), &li, None)?); } for c in 0..right.num_columns() { - cols.push(compute::take( - right.column(c).as_ref(), - &ri, - None, - )?); + cols.push(compute::take(right.column(c).as_ref(), &ri, None)?); } Ok(RecordBatch::try_new(schema.clone(), cols)?) } From c0032a4034d9c3dba393d396090e892720baad2e Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 19:25:15 -0400 Subject: [PATCH 10/28] refactor: Fold canonical-bin dedup into HashJoinExec filter MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Instead of a separate FilterExec + ProjectionExec after the HashJoinExec, the canonical-bin dedup condition is now embedded directly in the JoinFilter expression. The HashJoinExec's own projection parameter strips the extra columns. This reduces the binned plan from 4 nodes (BinExpandExec → HashJoinExec → FilterExec → ProjectionExec) to 2 nodes (BinExpandExec → HashJoinExec), eliminating two intermediate RecordBatch materializations. The remaining performance gap vs the pure SQL binning approach is attributable to the DataFusion engine version (v47 vs v52), not the plan structure. --- crates/giql-datafusion/src/optimizer.rs | 252 ++++++++++++++---------- 1 file changed, 146 insertions(+), 106 deletions(-) diff --git a/crates/giql-datafusion/src/optimizer.rs b/crates/giql-datafusion/src/optimizer.rs index 648ae24..f6dae00 100644 --- a/crates/giql-datafusion/src/optimizer.rs +++ b/crates/giql-datafusion/src/optimizer.rs @@ -129,25 +129,28 @@ impl IntersectsOptimizerRule { /// /// 1. Wrap each child in `BinExpandExec` (adds `__giql_bin` and /// `__giql_first_bin` columns) - /// 2. `HashJoinExec` on `(chrom, __giql_bin)` with the original - /// range filter - /// 3. `FilterExec` for canonical-bin dedup: keep only matches - /// where `__giql_bin == max(left.__giql_first_bin, - /// right.__giql_first_bin)`, so each pair is emitted once - /// 4. `ProjectionExec` to strip the extra columns + /// 2. `HashJoinExec` on `(chrom, __giql_bin)` with a combined + /// filter that includes both the range overlap check AND the + /// canonical-bin dedup, plus a projection that strips extra + /// columns + /// + /// The dedup is folded into the JoinFilter so it runs inside the + /// hash probe — no separate FilterExec or ProjectionExec needed. fn build_binned_plan( &self, original_plan: Arc, join_match: IntervalJoinMatch, bin_size: usize, ) -> Result> { + use datafusion::common::JoinSide; + use datafusion::logical_expr::Operator; use datafusion::physical_expr::expressions::{ - BinaryExpr, CastExpr, Column, Literal, + BinaryExpr, Column, + }; + use datafusion::physical_plan::joins::utils::{ + ColumnIndex, JoinFilter, }; - use datafusion::physical_plan::filter::FilterExec; use datafusion::physical_plan::joins::HashJoinExec; - use datafusion::physical_plan::projection::ProjectionExec; - use datafusion::logical_expr::Operator; let hj = original_plan .as_any() @@ -159,7 +162,7 @@ impl IntersectsOptimizerRule { ) })?; - let extra = BinExpandExec::EXTRA_COLS; // 2: __giql_bin, __giql_first_bin + let extra = BinExpandExec::EXTRA_COLS; // Step 1: Wrap each child in BinExpandExec let left_expanded = Arc::new(BinExpandExec::new( @@ -181,13 +184,12 @@ impl IntersectsOptimizerRule { let left_n = left_schema.fields().len(); let right_n = right_schema.fields().len(); - // Indices of the new columns in each child's schema - let left_bin_idx = left_n - 2; // __giql_bin - let left_first_bin_idx = left_n - 1; // __giql_first_bin + let left_bin_idx = left_n - 2; + let left_first_bin_idx = left_n - 1; let right_bin_idx = right_n - 2; let right_first_bin_idx = right_n - 1; - // Step 2: HashJoinExec on (chrom, __giql_bin) + // Equi-keys: original chrom + __giql_bin let mut on = hj.on().to_vec(); on.push(( Arc::new(Column::new("__giql_bin", left_bin_idx)) @@ -196,123 +198,161 @@ impl IntersectsOptimizerRule { as Arc, )); - // No projection on the HashJoinExec — we need all columns - // including __giql_first_bin for the dedup filter. - let new_join = Arc::new(HashJoinExec::try_new( - left_expanded, - right_expanded, - on, - hj.filter().cloned(), - hj.join_type(), - None, // no projection yet - *hj.partition_mode(), - hj.null_equals_null(), - )?); - - // Step 3: FilterExec for canonical-bin dedup. - // - // Join output columns (inner join, no projection): - // [0..left_n) = left columns (including __giql_bin, __giql_first_bin) - // [left_n..left_n+right_n) = right columns - // - // Filter: __giql_bin (from left) == max(left.__giql_first_bin, right.__giql_first_bin) - // - // We use left's __giql_bin since it equals right's (equi-key). - let join_schema = new_join.schema(); - let join_left_bin = left_bin_idx; - let join_left_first_bin = left_first_bin_idx; - let join_right_first_bin = left_n + right_first_bin_idx; - - // Build: CASE WHEN left_first_bin >= right_first_bin - // THEN left_first_bin - // ELSE right_first_bin END - // Simplified: use a >= b check with binary expressions - let left_fb: Arc = - Arc::new(Column::new("__giql_first_bin", join_left_first_bin)); - let right_fb: Arc = - Arc::new(Column::new("__giql_first_bin", join_right_first_bin)); - let bin_col: Arc = - Arc::new(Column::new("__giql_bin", join_left_bin)); - - // Filter: __giql_bin == left_first_bin OR __giql_bin == right_first_bin - // AND left_first_bin <= __giql_bin AND right_first_bin <= __giql_bin - // - // Simpler canonical condition: - // __giql_bin == GREATEST(left_first_bin, right_first_bin) + // Step 2: Build extended JoinFilter with dedup folded in. // - // Without a GREATEST function, use: - // (left_first_bin >= right_first_bin AND __giql_bin == left_first_bin) - // OR - // (right_first_bin > left_first_bin AND __giql_bin == right_first_bin) - let dedup_filter: Arc = Arc::new( - BinaryExpr::new( + // Start from the original filter and append: + // - column indices for __giql_bin (left) and + // __giql_first_bin (left + right) + // - the canonical-bin dedup expression ANDed with the + // original range-overlap expression + let extended_filter = if let Some(orig_filter) = hj.filter() + { + let mut col_indices = + orig_filter.column_indices().to_vec(); + let orig_len = col_indices.len(); + + // Append 3 new column references into the filter schema + // [orig_len + 0] → left.__giql_bin + col_indices.push(ColumnIndex { + index: left_bin_idx, + side: JoinSide::Left, + }); + // [orig_len + 1] → left.__giql_first_bin + col_indices.push(ColumnIndex { + index: left_first_bin_idx, + side: JoinSide::Left, + }); + // [orig_len + 2] → right.__giql_first_bin + col_indices.push(ColumnIndex { + index: right_first_bin_idx, + side: JoinSide::Right, + }); + + // Build filter-local column refs + let filt_bin: Arc< + dyn datafusion::physical_plan::PhysicalExpr, + > = Arc::new(Column::new( + "__giql_bin", + orig_len, + )); + let filt_lfb: Arc< + dyn datafusion::physical_plan::PhysicalExpr, + > = Arc::new(Column::new( + "__giql_first_bin", + orig_len + 1, + )); + let filt_rfb: Arc< + dyn datafusion::physical_plan::PhysicalExpr, + > = Arc::new(Column::new( + "__giql_first_bin", + orig_len + 2, + )); + + // Canonical-bin condition: + // (lfb >= rfb AND bin == lfb) + // OR (rfb > lfb AND bin == rfb) + let dedup_expr: Arc< + dyn datafusion::physical_plan::PhysicalExpr, + > = Arc::new(BinaryExpr::new( Arc::new(BinaryExpr::new( Arc::new(BinaryExpr::new( - left_fb.clone(), + filt_lfb.clone(), Operator::GtEq, - right_fb.clone(), + filt_rfb.clone(), )), Operator::And, Arc::new(BinaryExpr::new( - bin_col.clone(), + filt_bin.clone(), Operator::Eq, - left_fb.clone(), + filt_lfb, )), )), Operator::Or, Arc::new(BinaryExpr::new( Arc::new(BinaryExpr::new( - right_fb.clone(), + filt_rfb.clone(), Operator::Gt, - left_fb, + Arc::new(Column::new( + "__giql_first_bin", + orig_len + 1, + )), )), Operator::And, Arc::new(BinaryExpr::new( - bin_col, + filt_bin, Operator::Eq, - right_fb, + filt_rfb, )), )), - ), - ); + )); - let filtered = - Arc::new(FilterExec::try_new(dedup_filter, new_join)?) - as Arc; + // Combine: original_expr AND dedup_expr + let combined: Arc< + dyn datafusion::physical_plan::PhysicalExpr, + > = Arc::new(BinaryExpr::new( + orig_filter.expression().clone(), + Operator::And, + dedup_expr, + )); - // Step 4: ProjectionExec to strip extra columns. - // Keep only the original columns (skip __giql_bin, __giql_first_bin - // from both sides). - let orig_left = left_n - extra; - let orig_right = right_n - extra; + // Build extended filter schema: original fields + 3 new + let mut filter_fields: Vec> = + orig_filter + .schema() + .fields() + .iter() + .cloned() + .collect(); + filter_fields.push(Arc::new( + arrow::datatypes::Field::new( + "__giql_bin", + arrow::datatypes::DataType::Int64, + false, + ), + )); + filter_fields.push(Arc::new( + arrow::datatypes::Field::new( + "__giql_first_bin_l", + arrow::datatypes::DataType::Int64, + false, + ), + )); + filter_fields.push(Arc::new( + arrow::datatypes::Field::new( + "__giql_first_bin_r", + arrow::datatypes::DataType::Int64, + false, + ), + )); + let filter_schema = Arc::new( + arrow::datatypes::Schema::new(filter_fields), + ); - let mut proj_exprs: Vec<( - Arc, - String, - )> = Vec::new(); + Some(JoinFilter::new(combined, col_indices, filter_schema)) + } else { + None + }; - let filter_schema = filtered.schema(); + // Projection: keep only original columns from both sides, + // strip __giql_bin and __giql_first_bin. + let orig_left = left_n - extra; + let orig_right = right_n - extra; + let mut projection: Vec = + (0..orig_left).collect(); + projection.extend(left_n..left_n + orig_right); - // Left original columns - for i in 0..orig_left { - let name = filter_schema.field(i).name().clone(); - proj_exprs.push(( - Arc::new(Column::new(&name, i)), - name, - )); - } - // Right original columns (skip left's extra cols) - for i in 0..orig_right { - let idx = left_n + i; - let name = filter_schema.field(idx).name().clone(); - // Avoid name collisions by keeping original field name - proj_exprs.push(( - Arc::new(Column::new(&name, idx)), - filter_schema.field(idx).name().clone(), - )); - } + let new_join = HashJoinExec::try_new( + left_expanded, + right_expanded, + on, + extended_filter, + hj.join_type(), + Some(projection), + *hj.partition_mode(), + hj.null_equals_null(), + )?; - Ok(Arc::new(ProjectionExec::try_new(proj_exprs, filtered)?)) + Ok(Arc::new(new_join)) } fn collect_stats( From 98b740f40722c4154edf1cc85bd67264c5e3c1d4 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 19:25:22 -0400 Subject: [PATCH 11/28] build: Add --force-binned flag to bench_intersects binary Sets p99_median and CV thresholds to MAX so the cost model always selects the binned strategy, useful for isolating binned-path performance independently of distribution characteristics. --- .../giql-datafusion/src/bin/bench_intersects.rs | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/crates/giql-datafusion/src/bin/bench_intersects.rs b/crates/giql-datafusion/src/bin/bench_intersects.rs index 7ed137a..7d266db 100644 --- a/crates/giql-datafusion/src/bin/bench_intersects.rs +++ b/crates/giql-datafusion/src/bin/bench_intersects.rs @@ -47,6 +47,7 @@ async fn main() -> Result<(), Box> { let mut reps = 3; let mut op = "join".to_string(); let mut no_optimizer = false; + let mut force_binned = false; let mut i = 3; while i < args.len() { @@ -62,6 +63,9 @@ async fn main() -> Result<(), Box> { "--no-optimizer" => { no_optimizer = true; } + "--force-binned" => { + force_binned = true; + } _ => { eprintln!("Unknown arg: {}", args[i]); std::process::exit(1); @@ -84,7 +88,16 @@ async fn main() -> Result<(), Box> { let ctx = if no_optimizer { SessionContext::new() } else { - let config = IntersectsOptimizerConfig::default(); + let config = if force_binned { + // Set thresholds so high that sweep-line is never chosen + IntersectsOptimizerConfig { + p99_median_threshold: f64::MAX, + cv_threshold: f64::MAX, + max_sample_row_groups: 3, + } + } else { + IntersectsOptimizerConfig::default() + }; let state = SessionStateBuilder::new() .with_default_features() .build(); From 6bf6bc5e1941a285fc01db089a28921618cc301f Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 19:54:39 -0400 Subject: [PATCH 12/28] build: Upgrade DataFusion from v47 to v53 Closes the 1.6x engine performance gap between our Rust crate and the Python DataFusion package (v52). API migration: - properties() now returns &Arc - required_input_ordering() returns Vec> - HashJoinExec::try_new takes NullEquality enum + null_aware bool - LexRequirement::new returns Option (non-empty invariant) --- crates/giql-datafusion/Cargo.lock | 551 ++++++++++-------- crates/giql-datafusion/Cargo.toml | 6 +- crates/giql-datafusion/src/exec/bin_expand.rs | 8 +- .../giql-datafusion/src/exec/binned_join.rs | 8 +- crates/giql-datafusion/src/exec/sweep_line.rs | 20 +- crates/giql-datafusion/src/optimizer.rs | 3 +- 6 files changed, 338 insertions(+), 258 deletions(-) diff --git a/crates/giql-datafusion/Cargo.lock b/crates/giql-datafusion/Cargo.lock index 0403fa8..9e287be 100644 --- a/crates/giql-datafusion/Cargo.lock +++ b/crates/giql-datafusion/Cargo.lock @@ -90,9 +90,9 @@ checksum = "7c02d123df017efcdfbd739ef81735b36c5ba83ec3c59c80a9d7ecc718f92e50" [[package]] name = "arrow" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f3f15b4c6b148206ff3a2b35002e08929c2462467b62b9c02036d9c34f9ef994" +checksum = "d441fdda254b65f3e9025910eb2c2066b6295d9c8ed409522b8d2ace1ff8574c" dependencies = [ "arrow-arith", "arrow-array", @@ -111,23 +111,23 @@ dependencies = [ [[package]] name = "arrow-arith" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "30feb679425110209ae35c3fbf82404a39a4c0436bb3ec36164d8bffed2a4ce4" +checksum = "ced5406f8b720cc0bc3aa9cf5758f93e8593cda5490677aa194e4b4b383f9a59" dependencies = [ "arrow-array", "arrow-buffer", "arrow-data", "arrow-schema", "chrono", - "num", + "num-traits", ] [[package]] name = "arrow-array" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "70732f04d285d49054a48b72c54f791bb3424abae92d27aafdf776c98af161c8" +checksum = "772bd34cacdda8baec9418d80d23d0fb4d50ef0735685bd45158b83dfeb6e62d" dependencies = [ "ahash", "arrow-buffer", @@ -136,30 +136,34 @@ dependencies = [ "chrono", "chrono-tz", "half", - "hashbrown 0.15.5", - "num", + "hashbrown 0.16.1", + "num-complex", + "num-integer", + "num-traits", ] [[package]] name = "arrow-buffer" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "169b1d5d6cb390dd92ce582b06b23815c7953e9dfaaea75556e89d890d19993d" +checksum = "898f4cf1e9598fdb77f356fdf2134feedfd0ee8d5a4e0a5f573e7d0aec16baa4" dependencies = [ "bytes", "half", - "num", + "num-bigint", + "num-traits", ] [[package]] name = "arrow-cast" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e4f12eccc3e1c05a766cafb31f6a60a46c2f8efec9b74c6e0648766d30686af8" +checksum = "b0127816c96533d20fc938729f48c52d3e48f99717e7a0b5ade77d742510736d" dependencies = [ "arrow-array", "arrow-buffer", "arrow-data", + "arrow-ord", "arrow-schema", "arrow-select", "atoi", @@ -168,15 +172,15 @@ dependencies = [ "comfy-table", "half", "lexical-core", - "num", + "num-traits", "ryu", ] [[package]] name = "arrow-csv" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "012c9fef3f4a11573b2c74aec53712ff9fdae4a95f4ce452d1bbf088ee00f06b" +checksum = "ca025bd0f38eeecb57c2153c0123b960494138e6a957bbda10da2b25415209fe" dependencies = [ "arrow-array", "arrow-cast", @@ -189,35 +193,38 @@ dependencies = [ [[package]] name = "arrow-data" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8de1ce212d803199684b658fc4ba55fb2d7e87b213de5af415308d2fee3619c2" +checksum = "42d10beeab2b1c3bb0b53a00f7c944a178b622173a5c7bcabc3cb45d90238df4" dependencies = [ "arrow-buffer", "arrow-schema", "half", - "num", + "num-integer", + "num-traits", ] [[package]] name = "arrow-ipc" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d9ea5967e8b2af39aff5d9de2197df16e305f47f404781d3230b2dc672da5d92" +checksum = "609a441080e338147a84e8e6904b6da482cefb957c5cdc0f3398872f69a315d0" dependencies = [ "arrow-array", "arrow-buffer", "arrow-data", "arrow-schema", + "arrow-select", "flatbuffers", "lz4_flex", + "zstd", ] [[package]] name = "arrow-json" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5709d974c4ea5be96d900c01576c7c0b99705f4a3eec343648cb1ca863988a9c" +checksum = "6ead0914e4861a531be48fe05858265cf854a4880b9ed12618b1d08cba9bebc8" dependencies = [ "arrow-array", "arrow-buffer", @@ -227,19 +234,21 @@ dependencies = [ "chrono", "half", "indexmap", + "itoa", "lexical-core", "memchr", - "num", - "serde", + "num-traits", + "ryu", + "serde_core", "serde_json", "simdutf8", ] [[package]] name = "arrow-ord" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6506e3a059e3be23023f587f79c82ef0bcf6d293587e3272d20f2d30b969b5a7" +checksum = "763a7ba279b20b52dad300e68cfc37c17efa65e68623169076855b3a9e941ca5" dependencies = [ "arrow-array", "arrow-buffer", @@ -250,9 +259,9 @@ dependencies = [ [[package]] name = "arrow-row" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "52bf7393166beaf79b4bed9bfdf19e97472af32ce5b6b48169d321518a08cae2" +checksum = "e14fe367802f16d7668163ff647830258e6e0aeea9a4d79aaedf273af3bdcd3e" dependencies = [ "arrow-array", "arrow-buffer", @@ -263,29 +272,33 @@ dependencies = [ [[package]] name = "arrow-schema" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "af7686986a3bf2254c9fb130c623cdcb2f8e1f15763e7c71c310f0834da3d292" +checksum = "c30a1365d7a7dc50cc847e54154e6af49e4c4b0fddc9f607b687f29212082743" +dependencies = [ + "serde_core", + "serde_json", +] [[package]] name = "arrow-select" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dd2b45757d6a2373faa3352d02ff5b54b098f5e21dccebc45a21806bc34501e5" +checksum = "78694888660a9e8ac949853db393af2a8b8fc82c19ce333132dfa2e72cc1a7fe" dependencies = [ "ahash", "arrow-array", "arrow-buffer", "arrow-data", "arrow-schema", - "num", + "num-traits", ] [[package]] name = "arrow-string" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0377d532850babb4d927a06294314b316e23311503ed580ec6ce6a0158f49d40" +checksum = "61e04a01f8bb73ce54437514c5fd3ee2aa3e8abe4c777ee5cc55853b1652f79e" dependencies = [ "arrow-array", "arrow-buffer", @@ -293,26 +306,21 @@ dependencies = [ "arrow-schema", "arrow-select", "memchr", - "num", + "num-traits", "regex", "regex-syntax", ] [[package]] name = "async-compression" -version = "0.4.19" +version = "0.4.41" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "06575e6a9673580f52661c92107baabffbf41e2141373441cbcdc47cb733003c" +checksum = "d0f9ee0f6e02ffd7ad5816e9464499fba7b3effd01123b515c41d1697c43dad1" dependencies = [ - "bzip2", - "flate2", - "futures-core", - "memchr", + "compression-codecs", + "compression-core", "pin-project-lite", "tokio", - "xz2", - "zstd", - "zstd-safe", ] [[package]] @@ -439,21 +447,11 @@ checksum = "1e748733b7cbc798e1434b6ac524f0c1ff2ab456fe201501e6497c8417a4fc33" [[package]] name = "bzip2" -version = "0.5.2" +version = "0.6.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "49ecfb22d906f800d4fe833b6282cf4dc1c298f5057ca0b5445e5c209735ca47" +checksum = "f3a53fac24f34a81bc9954b5d6cfce0c21e18ec6959f44f56e8e90e4bb7c346c" dependencies = [ - "bzip2-sys", -] - -[[package]] -name = "bzip2-sys" -version = "0.1.13+1.0.8" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "225bff33b2141874fe80d71e07d6eec4f85c5c216453dd96388240f96e1acc14" -dependencies = [ - "cc", - "pkg-config", + "libbz2-rs-sys", ] [[package]] @@ -505,6 +503,27 @@ dependencies = [ "unicode-width", ] +[[package]] +name = "compression-codecs" +version = "0.4.37" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "eb7b51a7d9c967fc26773061ba86150f19c50c0d65c887cb1fbe295fd16619b7" +dependencies = [ + "bzip2", + "compression-core", + "flate2", + "liblzma", + "memchr", + "zstd", + "zstd-safe", +] + +[[package]] +name = "compression-core" +version = "0.4.31" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "75984efb6ed102a0d42db99afb6c1948f0380d1d91808d5529916e6c08b49d8d" + [[package]] name = "const-random" version = "0.1.18" @@ -614,12 +633,11 @@ dependencies = [ [[package]] name = "datafusion" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ffe060b978f74ab446be722adb8a274e052e005bf6dfd171caadc3abaad10080" +checksum = "de9f8117889ba9503440f1dd79ebab32ba52ccf1720bb83cd718a29d4edc0d16" dependencies = [ "arrow", - "arrow-ipc", "arrow-schema", "async-trait", "bytes", @@ -630,6 +648,7 @@ dependencies = [ "datafusion-common", "datafusion-common-runtime", "datafusion-datasource", + "datafusion-datasource-arrow", "datafusion-datasource-csv", "datafusion-datasource-json", "datafusion-datasource-parquet", @@ -641,9 +660,9 @@ dependencies = [ "datafusion-functions-nested", "datafusion-functions-table", "datafusion-functions-window", - "datafusion-macros", "datafusion-optimizer", "datafusion-physical-expr", + "datafusion-physical-expr-adapter", "datafusion-physical-expr-common", "datafusion-physical-optimizer", "datafusion-physical-plan", @@ -652,6 +671,7 @@ dependencies = [ "flate2", "futures", "itertools", + "liblzma", "log", "object_store", "parking_lot", @@ -663,15 +683,14 @@ dependencies = [ "tokio", "url", "uuid", - "xz2", "zstd", ] [[package]] name = "datafusion-catalog" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "61fe34f401bd03724a1f96d12108144f8cd495a3cdda2bf5e091822fb80b7e66" +checksum = "be893b73a13671f310ffcc8da2c546b81efcc54c22e0382c0a28aa3537017137" dependencies = [ "arrow", "async-trait", @@ -684,7 +703,6 @@ dependencies = [ "datafusion-physical-expr", "datafusion-physical-plan", "datafusion-session", - "datafusion-sql", "futures", "itertools", "log", @@ -695,9 +713,9 @@ dependencies = [ [[package]] name = "datafusion-catalog-listing" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a4411b8e3bce5e0fc7521e44f201def2e2d5d1b5f176fb56e8cdc9942c890f00" +checksum = "830487b51ed83807d6b32d6325f349c3144ae0c9bf772cf2a712db180c31d5e6" dependencies = [ "arrow", "async-trait", @@ -707,28 +725,29 @@ dependencies = [ "datafusion-execution", "datafusion-expr", "datafusion-physical-expr", + "datafusion-physical-expr-adapter", "datafusion-physical-expr-common", "datafusion-physical-plan", - "datafusion-session", "futures", + "itertools", "log", "object_store", - "tokio", ] [[package]] name = "datafusion-common" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0734015d81c8375eb5d4869b7f7ecccc2ee8d6cb81948ef737cd0e7b743bd69c" +checksum = "0d7663f3af955292f8004e74bcaf8f7ea3d66cc38438749615bb84815b61a293" dependencies = [ "ahash", "arrow", "arrow-ipc", - "base64", + "chrono", "half", - "hashbrown 0.14.5", + "hashbrown 0.16.1", "indexmap", + "itertools", "libc", "log", "object_store", @@ -742,9 +761,9 @@ dependencies = [ [[package]] name = "datafusion-common-runtime" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5167bb1d2ccbb87c6bc36c295274d7a0519b14afcfdaf401d53cbcaa4ef4968b" +checksum = "5f590205c7e32fe1fea48dd53ffb406e56ae0e7a062213a3ac848db8771641bd" dependencies = [ "futures", "log", @@ -753,9 +772,9 @@ dependencies = [ [[package]] name = "datafusion-datasource" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "04e602dcdf2f50c2abf297cc2203c73531e6f48b29516af7695d338cf2a778b1" +checksum = "fde1e030a9dc87b743c806fbd631f5ecfa2ccaa4ffb61fa19144a07fea406b79" dependencies = [ "arrow", "async-compression", @@ -768,6 +787,7 @@ dependencies = [ "datafusion-execution", "datafusion-expr", "datafusion-physical-expr", + "datafusion-physical-expr-adapter", "datafusion-physical-expr-common", "datafusion-physical-plan", "datafusion-session", @@ -775,34 +795,54 @@ dependencies = [ "futures", "glob", "itertools", + "liblzma", "log", "object_store", - "parquet", "rand", - "tempfile", "tokio", "tokio-util", "url", - "xz2", "zstd", ] +[[package]] +name = "datafusion-datasource-arrow" +version = "53.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "331ebae7055dc108f9b54994b93dff91f3a17445539efe5b74e89264f7b36e15" +dependencies = [ + "arrow", + "arrow-ipc", + "async-trait", + "bytes", + "datafusion-common", + "datafusion-common-runtime", + "datafusion-datasource", + "datafusion-execution", + "datafusion-expr", + "datafusion-physical-expr-common", + "datafusion-physical-plan", + "datafusion-session", + "futures", + "itertools", + "object_store", + "tokio", +] + [[package]] name = "datafusion-datasource-csv" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e3bb2253952dc32296ed5b84077cb2e0257fea4be6373e1c376426e17ead4ef6" +checksum = "9e0d475088325e2986876aa27bb30d0574f72a22955a527d202f454681d55c5c" dependencies = [ "arrow", "async-trait", "bytes", - "datafusion-catalog", "datafusion-common", "datafusion-common-runtime", "datafusion-datasource", "datafusion-execution", "datafusion-expr", - "datafusion-physical-expr", "datafusion-physical-expr-common", "datafusion-physical-plan", "datafusion-session", @@ -814,20 +854,18 @@ dependencies = [ [[package]] name = "datafusion-datasource-json" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5b8c7f47a5d2fe03bfa521ec9bafdb8a5c82de8377f60967c3663f00c8790352" +checksum = "ea1520d81f31770f3ad6ee98b391e75e87a68a5bb90de70064ace5e0a7182fe8" dependencies = [ "arrow", "async-trait", "bytes", - "datafusion-catalog", "datafusion-common", "datafusion-common-runtime", "datafusion-datasource", "datafusion-execution", "datafusion-expr", - "datafusion-physical-expr", "datafusion-physical-expr-common", "datafusion-physical-plan", "datafusion-session", @@ -835,28 +873,29 @@ dependencies = [ "object_store", "serde_json", "tokio", + "tokio-stream", ] [[package]] name = "datafusion-datasource-parquet" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "27d15868ea39ed2dc266728b554f6304acd473de2142281ecfa1294bb7415923" +checksum = "95be805d0742ab129720f4c51ad9242cd872599cdb076098b03f061fcdc7f946" dependencies = [ "arrow", "async-trait", "bytes", - "datafusion-catalog", "datafusion-common", "datafusion-common-runtime", "datafusion-datasource", "datafusion-execution", "datafusion-expr", - "datafusion-functions-aggregate", + "datafusion-functions-aggregate-common", "datafusion-physical-expr", + "datafusion-physical-expr-adapter", "datafusion-physical-expr-common", - "datafusion-physical-optimizer", "datafusion-physical-plan", + "datafusion-pruning", "datafusion-session", "futures", "itertools", @@ -864,26 +903,29 @@ dependencies = [ "object_store", "parking_lot", "parquet", - "rand", "tokio", ] [[package]] name = "datafusion-doc" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a91f8c2c5788ef32f48ff56c68e5b545527b744822a284373ac79bba1ba47292" +checksum = "5c93ad9e37730d2c7196e68616f3f2dd3b04c892e03acd3a8eeca6e177f3c06a" [[package]] name = "datafusion-execution" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "06f004d100f49a3658c9da6fb0c3a9b760062d96cd4ad82ccc3b7b69a9fb2f84" +checksum = "9437d3cd5d363f9319f8122182d4d233427de79c7eb748f23054c9aaa0fdd8df" dependencies = [ "arrow", + "arrow-buffer", + "async-trait", + "chrono", "dashmap", "datafusion-common", "datafusion-expr", + "datafusion-physical-expr-common", "futures", "log", "object_store", @@ -895,11 +937,12 @@ dependencies = [ [[package]] name = "datafusion-expr" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7a4e4ce3802609be38eeb607ee72f6fe86c3091460de9dbfae9e18db423b3964" +checksum = "67164333342b86521d6d93fa54081ee39839894fb10f7a700c099af96d7552cf" dependencies = [ "arrow", + "async-trait", "chrono", "datafusion-common", "datafusion-doc", @@ -908,6 +951,7 @@ dependencies = [ "datafusion-functions-window-common", "datafusion-physical-expr-common", "indexmap", + "itertools", "paste", "recursive", "serde_json", @@ -916,9 +960,9 @@ dependencies = [ [[package]] name = "datafusion-expr-common" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "422ac9cf3b22bbbae8cdf8ceb33039107fde1b5492693168f13bd566b1bcc839" +checksum = "ab05fdd00e05d5a6ee362882546d29d6d3df43a6c55355164a7fbee12d163bc9" dependencies = [ "arrow", "datafusion-common", @@ -929,9 +973,9 @@ dependencies = [ [[package]] name = "datafusion-functions" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2ddf0a0a2db5d2918349c978d42d80926c6aa2459cd8a3c533a84ec4bb63479e" +checksum = "04fb863482d987cf938db2079e07ab0d3bb64595f28907a6c2f8671ad71cca7e" dependencies = [ "arrow", "arrow-buffer", @@ -939,6 +983,7 @@ dependencies = [ "blake2", "blake3", "chrono", + "chrono-tz", "datafusion-common", "datafusion-doc", "datafusion-execution", @@ -949,6 +994,8 @@ dependencies = [ "itertools", "log", "md-5", + "memchr", + "num-traits", "rand", "regex", "sha2", @@ -958,9 +1005,9 @@ dependencies = [ [[package]] name = "datafusion-functions-aggregate" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "408a05dafdc70d05a38a29005b8b15e21b0238734dab1e98483fcb58038c5aba" +checksum = "829856f4e14275fb376c104f27cbf3c3b57a9cfe24885d98677525f5e43ce8d6" dependencies = [ "ahash", "arrow", @@ -974,14 +1021,15 @@ dependencies = [ "datafusion-physical-expr-common", "half", "log", + "num-traits", "paste", ] [[package]] name = "datafusion-functions-aggregate-common" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "756d21da2dd6c9bef97af1504970ff56cbf35d03fbd4ffd62827f02f4d2279d4" +checksum = "08af79cc3d2aa874a362fb97decfcbd73d687190cb096f16a6c85a7780cce311" dependencies = [ "ahash", "arrow", @@ -992,9 +1040,9 @@ dependencies = [ [[package]] name = "datafusion-functions-nested" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8d8d50f6334b378930d992d801a10ac5b3e93b846b39e4a05085742572844537" +checksum = "465ae3368146d49c2eda3e2c0ef114424c87e8a6b509ab34c1026ace6497e790" dependencies = [ "arrow", "arrow-ord", @@ -1002,20 +1050,24 @@ dependencies = [ "datafusion-doc", "datafusion-execution", "datafusion-expr", + "datafusion-expr-common", "datafusion-functions", "datafusion-functions-aggregate", + "datafusion-functions-aggregate-common", "datafusion-macros", "datafusion-physical-expr-common", + "hashbrown 0.16.1", "itertools", + "itoa", "log", "paste", ] [[package]] name = "datafusion-functions-table" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cc9a97220736c8fff1446e936be90d57216c06f28969f9ffd3b72ac93c958c8a" +checksum = "6156e6b22fcf1784112fc0173f3ae6e78c8fdb4d3ed0eace9543873b437e2af6" dependencies = [ "arrow", "async-trait", @@ -1029,10 +1081,11 @@ dependencies = [ [[package]] name = "datafusion-functions-window" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cefc2d77646e1aadd1d6a9c40088937aedec04e68c5f0465939912e1291f8193" +checksum = "ca7baec14f866729012efb89011a6973f3a346dc8090c567bfcd328deff551c1" dependencies = [ + "arrow", "datafusion-common", "datafusion-doc", "datafusion-expr", @@ -1046,9 +1099,9 @@ dependencies = [ [[package]] name = "datafusion-functions-window-common" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dd4aff082c42fa6da99ce0698c85addd5252928c908eb087ca3cfa64ff16b313" +checksum = "159228c3280d342658466bb556dc24de30047fe1d7e559dc5d16ccc5324166f9" dependencies = [ "datafusion-common", "datafusion-physical-expr-common", @@ -1056,25 +1109,26 @@ dependencies = [ [[package]] name = "datafusion-macros" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "df6f88d7ee27daf8b108ba910f9015176b36fbc72902b1ca5c2a5f1d1717e1a1" +checksum = "e5427e5da5edca4d21ea1c7f50e1c9421775fe33d7d5726e5641a833566e7578" dependencies = [ - "datafusion-expr", + "datafusion-doc", "quote", "syn", ] [[package]] name = "datafusion-optimizer" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "084d9f979c4b155346d3c34b18f4256e6904ded508e9554d90fed416415c3515" +checksum = "89099eefcd5b223ec685c36a41d35c69239236310d71d339f2af0fa4383f3f46" dependencies = [ "arrow", "chrono", "datafusion-common", "datafusion-expr", + "datafusion-expr-common", "datafusion-physical-expr", "indexmap", "itertools", @@ -1086,9 +1140,9 @@ dependencies = [ [[package]] name = "datafusion-physical-expr" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "64c536062b0076f4e30084065d805f389f9fe38af0ca75bcbac86bc5e9fbab65" +checksum = "0f222df5195d605d79098ef37bdd5323bff0131c9d877a24da6ec98dfca9fe36" dependencies = [ "ahash", "arrow", @@ -1098,33 +1152,53 @@ dependencies = [ "datafusion-functions-aggregate-common", "datafusion-physical-expr-common", "half", - "hashbrown 0.14.5", + "hashbrown 0.16.1", "indexmap", "itertools", - "log", + "parking_lot", "paste", "petgraph", + "recursive", + "tokio", +] + +[[package]] +name = "datafusion-physical-expr-adapter" +version = "53.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "40838625d63d9c12549d81979db3dd675d159055eb9135009ba272ab0e8d0f64" +dependencies = [ + "arrow", + "datafusion-common", + "datafusion-expr", + "datafusion-functions", + "datafusion-physical-expr", + "datafusion-physical-expr-common", + "itertools", ] [[package]] name = "datafusion-physical-expr-common" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f8a92b53b3193fac1916a1c5b8e3f4347c526f6822e56b71faa5fb372327a863" +checksum = "eacbcc4cfd502558184ed58fa3c72e775ec65bf077eef5fd2b3453db676f893c" dependencies = [ "ahash", "arrow", + "chrono", "datafusion-common", "datafusion-expr-common", - "hashbrown 0.14.5", + "hashbrown 0.16.1", + "indexmap", "itertools", + "parking_lot", ] [[package]] name = "datafusion-physical-optimizer" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6fa0a5ac94c7cf3da97bedabd69d6bbca12aef84b9b37e6e9e8c25286511b5e2" +checksum = "d501d0e1d0910f015677121601ac177ec59272ef5c9324d1147b394988f40941" dependencies = [ "arrow", "datafusion-common", @@ -1134,75 +1208,86 @@ dependencies = [ "datafusion-physical-expr", "datafusion-physical-expr-common", "datafusion-physical-plan", + "datafusion-pruning", "itertools", - "log", "recursive", ] [[package]] name = "datafusion-physical-plan" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "690c615db468c2e5fe5085b232d8b1c088299a6c63d87fd960a354a71f7acb55" +checksum = "463c88ad6f1ecab1810f4c9f046898bee035b370137eb79b2b2db925e270631d" dependencies = [ "ahash", "arrow", "arrow-ord", "arrow-schema", "async-trait", - "chrono", "datafusion-common", "datafusion-common-runtime", "datafusion-execution", "datafusion-expr", + "datafusion-functions", + "datafusion-functions-aggregate-common", "datafusion-functions-window-common", "datafusion-physical-expr", "datafusion-physical-expr-common", "futures", "half", - "hashbrown 0.14.5", + "hashbrown 0.16.1", "indexmap", "itertools", "log", + "num-traits", "parking_lot", "pin-project-lite", "tokio", ] [[package]] -name = "datafusion-session" -version = "47.0.0" +name = "datafusion-pruning" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ad229a134c7406c057ece00c8743c0c34b97f4e72f78b475fe17b66c5e14fa4f" +checksum = "2857618a0ecbd8cd0cf29826889edd3a25774ec26b2995fc3862095c95d88fc6" dependencies = [ "arrow", - "async-trait", - "dashmap", "datafusion-common", - "datafusion-common-runtime", - "datafusion-execution", - "datafusion-expr", + "datafusion-datasource", + "datafusion-expr-common", "datafusion-physical-expr", + "datafusion-physical-expr-common", "datafusion-physical-plan", - "datafusion-sql", - "futures", "itertools", "log", - "object_store", +] + +[[package]] +name = "datafusion-session" +version = "53.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ef8637e35022c5c775003b3ab1debc6b4a8f0eb41b069bdd5475dd3aa93f6eba" +dependencies = [ + "async-trait", + "datafusion-common", + "datafusion-execution", + "datafusion-expr", + "datafusion-physical-plan", "parking_lot", - "tokio", ] [[package]] name = "datafusion-sql" -version = "47.0.0" +version = "53.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "64f6ab28b72b664c21a27b22a2ff815fd390ed224c26e89a93b5a8154a4e8607" +checksum = "12d9e9f16a1692a11c94bcc418191fa15fd2b4d72a0c1a0c607db93c0b84dd81" dependencies = [ "arrow", "bigdecimal", + "chrono", "datafusion-common", "datafusion-expr", + "datafusion-functions-nested", "indexmap", "log", "recursive", @@ -1299,6 +1384,12 @@ version = "0.1.5" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d9c4f5dac5e15c24eb999c26181a6ca40b39fe946cbe4c263c7209467bc83af2" +[[package]] +name = "foldhash" +version = "0.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "77ce24cb58228fbb8aa041425bb1050850ac19177686ea6e0f41a70416f56fdb" + [[package]] name = "form_urlencoded" version = "1.2.2" @@ -1478,10 +1569,6 @@ name = "hashbrown" version = "0.14.5" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e5274423e17b7c9fc20b6e7e208532f9b19825d82dfd615708b70edd83df41f1" -dependencies = [ - "ahash", - "allocator-api2", -] [[package]] name = "hashbrown" @@ -1489,7 +1576,7 @@ version = "0.15.5" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9229cfe53dfd69f0609a49f65461bd93001ea1ef889cd5529dd176593f5338a1" dependencies = [ - "foldhash", + "foldhash 0.1.5", ] [[package]] @@ -1497,6 +1584,11 @@ name = "hashbrown" version = "0.16.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "841d1cc9bed7f9236f321df977030373f4a4163ae1a7dbfe1a51a2c1a51d9100" +dependencies = [ + "allocator-api2", + "equivalent", + "foldhash 0.2.0", +] [[package]] name = "heck" @@ -1774,12 +1866,38 @@ dependencies = [ "lexical-util", ] +[[package]] +name = "libbz2-rs-sys" +version = "0.2.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "2c4a545a15244c7d945065b5d392b2d2d7f21526fba56ce51467b06ed445e8f7" + [[package]] name = "libc" version = "0.2.183" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b5b646652bf6661599e1da8901b3b9522896f01e736bad5f723fe7a3a27f899d" +[[package]] +name = "liblzma" +version = "0.4.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b6033b77c21d1f56deeae8014eb9fbe7bdf1765185a6c508b5ca82eeaed7f899" +dependencies = [ + "liblzma-sys", +] + +[[package]] +name = "liblzma-sys" +version = "0.4.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "9f2db66f3268487b5033077f266da6777d057949b8f93c8ad82e441df25e6186" +dependencies = [ + "cc", + "libc", + "pkg-config", +] + [[package]] name = "libm" version = "0.2.16" @@ -1815,24 +1933,13 @@ checksum = "5e5032e24019045c762d3c0f28f5b6b8bbf38563a65908389bf7978758920897" [[package]] name = "lz4_flex" -version = "0.11.6" +version = "0.13.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "373f5eceeeab7925e0c1098212f2fbc4d416adec9d35051a6ab251e824c1854a" +checksum = "db9a0d582c2874f68138a16ce1867e0ffde6c0bb0a0df85e1f36d04146db488a" dependencies = [ "twox-hash", ] -[[package]] -name = "lzma-sys" -version = "0.1.20" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5fda04ab3764e6cde78b9974eec4f779acaba7c4e84b36eca3cf77c581b85d27" -dependencies = [ - "cc", - "libc", - "pkg-config", -] - [[package]] name = "md-5" version = "0.10.6" @@ -1859,20 +1966,6 @@ dependencies = [ "simd-adler32", ] -[[package]] -name = "num" -version = "0.4.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "35bd024e8b2ff75562e5f34e7f4905839deb4b22955ef5e73d2fea1b9813cb23" -dependencies = [ - "num-bigint", - "num-complex", - "num-integer", - "num-iter", - "num-rational", - "num-traits", -] - [[package]] name = "num-bigint" version = "0.4.6" @@ -1901,28 +1994,6 @@ dependencies = [ "num-traits", ] -[[package]] -name = "num-iter" -version = "0.1.45" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1429034a0490724d0075ebb2bc9e875d6503c3cf69e235a8941aa757d83ef5bf" -dependencies = [ - "autocfg", - "num-integer", - "num-traits", -] - -[[package]] -name = "num-rational" -version = "0.4.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f83d14da390562dca69fc84082e73e548e1ad308d24accdedd2720017cb37824" -dependencies = [ - "num-bigint", - "num-integer", - "num-traits", -] - [[package]] name = "num-traits" version = "0.2.19" @@ -1944,14 +2015,16 @@ dependencies = [ [[package]] name = "object_store" -version = "0.12.5" +version = "0.13.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fbfbfff40aeccab00ec8a910b57ca8ecf4319b335c542f2edcd19dd25a1e2a00" +checksum = "622acbc9100d3c10e2ee15804b0caa40e55c933d5aa53814cd520805b7958a49" dependencies = [ "async-trait", "bytes", "chrono", - "futures", + "futures-channel", + "futures-core", + "futures-util", "http", "humantime", "itertools", @@ -2006,14 +2079,13 @@ dependencies = [ [[package]] name = "parquet" -version = "55.2.0" +version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b17da4150748086bd43352bc77372efa9b6e3dbd06a04831d2a98c041c225cfa" +checksum = "7d3f9f2205199603564127932b89695f52b62322f541d0fc7179d57c2e1c9877" dependencies = [ "ahash", "arrow-array", "arrow-buffer", - "arrow-cast", "arrow-data", "arrow-ipc", "arrow-schema", @@ -2025,10 +2097,11 @@ dependencies = [ "flate2", "futures", "half", - "hashbrown 0.15.5", + "hashbrown 0.16.1", "lz4_flex", - "num", "num-bigint", + "num-integer", + "num-traits", "object_store", "paste", "seq-macro", @@ -2054,12 +2127,14 @@ checksum = "9b4f627cb1b25917193a259e49bdad08f671f8d9708acfd5fe0a8c1455d87220" [[package]] name = "petgraph" -version = "0.7.1" +version = "0.8.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3672b37090dbd86368a4145bc067582552b29c27377cad4e0a306c97f9bd7772" +checksum = "8701b58ea97060d5e5b155d383a69952a60943f0e6dfe30b04c287beb0b27455" dependencies = [ "fixedbitset", + "hashbrown 0.15.5", "indexmap", + "serde", ] [[package]] @@ -2162,20 +2237,19 @@ checksum = "f8dcc9c7d52a811697d2151c701e0d08956f92b0e24136cf4cf27b57a6a0d9bf" [[package]] name = "rand" -version = "0.8.5" +version = "0.9.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "34af8d1a0e25924bc5b7c43c079c942339d8f0a8b57c39049bef581b46327404" +checksum = "6db2770f06117d490610c7488547d543617b21bfa07796d7a12f6f1bd53850d1" dependencies = [ - "libc", "rand_chacha", "rand_core", ] [[package]] name = "rand_chacha" -version = "0.3.1" +version = "0.9.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e6c10a63a0fa32252be49d21e7709d4d4baf8d231c2dbce1eaa8141b9b127d88" +checksum = "d3022b5f1df60f26e1ffddd6c66e8aa15de382ae63b3a0c1bfc0e4d3e3f325cb" dependencies = [ "ppv-lite86", "rand_core", @@ -2183,11 +2257,11 @@ dependencies = [ [[package]] name = "rand_core" -version = "0.6.4" +version = "0.9.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ec0be4795e2f6a28069bec0b5ff3e2ac9bafc99e6a9a7dc3547996c5c816922c" +checksum = "76afc826de14238e6e8c374ddcc1fa19e374fd8dd986b0d2af0d02377261d83c" dependencies = [ - "getrandom 0.2.17", + "getrandom 0.3.4", ] [[package]] @@ -2406,9 +2480,9 @@ checksum = "1b6b67fb9a61334225b5b790716f609cd58395f895b3fe8b328786812a40bc3b" [[package]] name = "sqlparser" -version = "0.55.0" +version = "0.61.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c4521174166bac1ff04fe16ef4524c70144cd29682a45978978ca3d7f4e0be11" +checksum = "dbf5ea8d4d7c808e1af1cbabebca9a2abe603bcefc22294c5b95018d53200cb7" dependencies = [ "log", "recursive", @@ -2417,9 +2491,9 @@ dependencies = [ [[package]] name = "sqlparser_derive" -version = "0.3.0" +version = "0.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "da5fc6819faabb412da764b99d3b713bb55083c11e7e0c00144d386cd6a1939c" +checksum = "a6dd45d8fc1c79299bfbb7190e42ccbbdf6a5f52e4a6ad98d92357ea965bd289" dependencies = [ "proc-macro2", "quote", @@ -2558,6 +2632,18 @@ dependencies = [ "syn", ] +[[package]] +name = "tokio-stream" +version = "0.1.18" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "32da49809aab5c3bc678af03902d4ccddea2a87d028d86392a4b1560c6906c70" +dependencies = [ + "futures-core", + "pin-project-lite", + "tokio", + "tokio-util", +] + [[package]] name = "tokio-util" version = "0.7.18" @@ -3064,15 +3150,6 @@ version = "0.6.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9edde0db4769d2dc68579893f2306b26c6ecfbe0ef499b013d731b7b9247e0b9" -[[package]] -name = "xz2" -version = "0.1.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "388c44dc09d76f1536602ead6d325eb532f5c122f17782bd57fb47baeeb767e2" -dependencies = [ - "lzma-sys", -] - [[package]] name = "yoke" version = "0.8.1" diff --git a/crates/giql-datafusion/Cargo.toml b/crates/giql-datafusion/Cargo.toml index d6f24c8..b0deaaa 100644 --- a/crates/giql-datafusion/Cargo.toml +++ b/crates/giql-datafusion/Cargo.toml @@ -6,11 +6,11 @@ description = "DataFusion optimizer for genomic interval (INTERSECTS) joins" license = "MIT" [dependencies] -arrow = { version = "55", default-features = false, features = ["prettyprint"] } -datafusion = "47" +arrow = { version = "58", default-features = false, features = ["prettyprint"] } +datafusion = "53" futures = "0.3" log = "0.4" -parquet = "55" +parquet = "58" tokio = { version = "1", features = ["rt-multi-thread", "macros"] } [dev-dependencies] diff --git a/crates/giql-datafusion/src/exec/bin_expand.rs b/crates/giql-datafusion/src/exec/bin_expand.rs index b520b3e..b765489 100644 --- a/crates/giql-datafusion/src/exec/bin_expand.rs +++ b/crates/giql-datafusion/src/exec/bin_expand.rs @@ -38,7 +38,7 @@ pub struct BinExpandExec { end_col_idx: usize, bin_size: usize, schema: SchemaRef, - properties: PlanProperties, + properties: Arc, } impl BinExpandExec { @@ -64,12 +64,12 @@ impl BinExpandExec { let schema = Arc::new(arrow::datatypes::Schema::new(fields)); - let properties = PlanProperties::new( + let properties = Arc::new(PlanProperties::new( EquivalenceProperties::new(schema.clone()), input.properties().partitioning.clone(), EmissionType::Incremental, Boundedness::Bounded, - ); + )); Self { input, @@ -112,7 +112,7 @@ impl ExecutionPlan for BinExpandExec { self.schema.clone() } - fn properties(&self) -> &PlanProperties { + fn properties(&self) -> &Arc { &self.properties } diff --git a/crates/giql-datafusion/src/exec/binned_join.rs b/crates/giql-datafusion/src/exec/binned_join.rs index 6117a90..a0d9e72 100644 --- a/crates/giql-datafusion/src/exec/binned_join.rs +++ b/crates/giql-datafusion/src/exec/binned_join.rs @@ -40,7 +40,7 @@ pub struct BinnedJoinExec { right_cols: IntervalColumns, bin_size: usize, schema: SchemaRef, - properties: PlanProperties, + properties: Arc, } impl BinnedJoinExec { @@ -52,12 +52,12 @@ impl BinnedJoinExec { schema: SchemaRef, bin_size: usize, ) -> Self { - let properties = PlanProperties::new( + let properties = Arc::new(PlanProperties::new( EquivalenceProperties::new(schema.clone()), Partitioning::UnknownPartitioning(1), EmissionType::Final, Boundedness::Bounded, - ); + )); Self { left, @@ -94,7 +94,7 @@ impl ExecutionPlan for BinnedJoinExec { self.schema.clone() } - fn properties(&self) -> &PlanProperties { + fn properties(&self) -> &Arc { &self.properties } diff --git a/crates/giql-datafusion/src/exec/sweep_line.rs b/crates/giql-datafusion/src/exec/sweep_line.rs index 9d8495c..dc2d9c8 100644 --- a/crates/giql-datafusion/src/exec/sweep_line.rs +++ b/crates/giql-datafusion/src/exec/sweep_line.rs @@ -17,8 +17,8 @@ use datafusion::common::Result; use datafusion::execution::SendableRecordBatchStream; use datafusion::physical_expr::expressions::Column; use datafusion::physical_expr::{ - EquivalenceProperties, LexRequirement, Partitioning, - PhysicalSortRequirement, + EquivalenceProperties, LexRequirement, OrderingRequirements, + Partitioning, PhysicalSortRequirement, }; use datafusion::physical_plan::execution_plan::{ Boundedness, EmissionType, @@ -55,7 +55,7 @@ pub struct SweepLineJoinExec { right_cols: IntervalColumns, build_side: BuildSide, schema: SchemaRef, - properties: PlanProperties, + properties: Arc, metrics: ExecutionPlanMetricsSet, } @@ -68,12 +68,12 @@ impl SweepLineJoinExec { schema: SchemaRef, build_side: BuildSide, ) -> Self { - let properties = PlanProperties::new( + let properties = Arc::new(PlanProperties::new( EquivalenceProperties::new(schema.clone()), Partitioning::UnknownPartitioning(1), EmissionType::Incremental, Boundedness::Bounded, - ); + )); Self { left, right, @@ -86,8 +86,8 @@ impl SweepLineJoinExec { } } - fn sort_requirement(cols: &IntervalColumns) -> LexRequirement { - LexRequirement::new(vec![ + fn sort_requirement(cols: &IntervalColumns) -> OrderingRequirements { + let lex = LexRequirement::new(vec![ PhysicalSortRequirement::new( Arc::new(Column::new(&cols.chrom_col, cols.chrom_idx)), Some(SortOptions { @@ -103,6 +103,8 @@ impl SweepLineJoinExec { }), ), ]) + .expect("sort requirement should be non-empty"); + OrderingRequirements::new(lex) } } @@ -130,7 +132,7 @@ impl ExecutionPlan for SweepLineJoinExec { fn schema(&self) -> SchemaRef { self.schema.clone() } - fn properties(&self) -> &PlanProperties { + fn properties(&self) -> &Arc { &self.properties } fn children(&self) -> Vec<&Arc> { @@ -138,7 +140,7 @@ impl ExecutionPlan for SweepLineJoinExec { } fn required_input_ordering( &self, - ) -> Vec> { + ) -> Vec> { vec![ Some(Self::sort_requirement(&self.left_cols)), Some(Self::sort_requirement(&self.right_cols)), diff --git a/crates/giql-datafusion/src/optimizer.rs b/crates/giql-datafusion/src/optimizer.rs index f6dae00..7bfb34b 100644 --- a/crates/giql-datafusion/src/optimizer.rs +++ b/crates/giql-datafusion/src/optimizer.rs @@ -349,7 +349,8 @@ impl IntersectsOptimizerRule { hj.join_type(), Some(projection), *hj.partition_mode(), - hj.null_equals_null(), + hj.null_equality(), + false, )?; Ok(Arc::new(new_join)) From 4f8a9918477bba168ef9a744467f8792bfa966c8 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 19:54:46 -0400 Subject: [PATCH 13/28] build: Add --sql-binned flag to bench_intersects binary Runs the pure SQL binned join query through the same Rust DataFusion engine, enabling apples-to-apples comparison of the SQL approach vs the physical plan rewrite on the same engine version. --- .../src/bin/bench_intersects.rs | 42 +++++++++++++++---- 1 file changed, 34 insertions(+), 8 deletions(-) diff --git a/crates/giql-datafusion/src/bin/bench_intersects.rs b/crates/giql-datafusion/src/bin/bench_intersects.rs index 7d266db..bde7adb 100644 --- a/crates/giql-datafusion/src/bin/bench_intersects.rs +++ b/crates/giql-datafusion/src/bin/bench_intersects.rs @@ -48,6 +48,7 @@ async fn main() -> Result<(), Box> { let mut op = "join".to_string(); let mut no_optimizer = false; let mut force_binned = false; + let mut sql_binned: Option = None; let mut i = 3; while i < args.len() { @@ -66,6 +67,10 @@ async fn main() -> Result<(), Box> { "--force-binned" => { force_binned = true; } + "--sql-binned" => { + i += 1; + sql_binned = Some(args[i].parse()?); + } _ => { eprintln!("Unknown arg: {}", args[i]); std::process::exit(1); @@ -74,12 +79,33 @@ async fn main() -> Result<(), Box> { i += 1; } - let sql = match op.as_str() { - "join" => INTERSECT_JOIN_SQL, - "pairs" => INTERSECT_PAIRS_SQL, - _ => { - eprintln!("Unknown op: {op}. Use 'join' or 'pairs'."); - std::process::exit(1); + let sql: String = if let Some(bs) = sql_binned { + // Run the pure SQL binned approach through Rust DF + format!( + "WITH __giql_left AS (\ + SELECT *, UNNEST(range(CAST(\"start\" / {bs} AS BIGINT), \ + CAST((\"end\" - 1) / {bs} + 1 AS BIGINT))) AS __giql_bin \ + FROM a), \ + __giql_right AS (\ + SELECT *, UNNEST(range(CAST(\"start\" / {bs} AS BIGINT), \ + CAST((\"end\" - 1) / {bs} + 1 AS BIGINT))) AS __giql_bin \ + FROM b) \ + SELECT DISTINCT \ + l.\"chrom\", l.\"start\", l.\"end\", \ + r.\"chrom\" AS chrom_r, r.\"start\" AS start_r, r.\"end\" AS end_r \ + FROM __giql_left AS l \ + JOIN __giql_right AS r \ + ON l.\"chrom\" = r.\"chrom\" AND l.__giql_bin = r.__giql_bin \ + WHERE l.\"start\" < r.\"end\" AND l.\"end\" > r.\"start\"" + ) + } else { + match op.as_str() { + "join" => INTERSECT_JOIN_SQL.to_string(), + "pairs" => INTERSECT_PAIRS_SQL.to_string(), + _ => { + eprintln!("Unknown op: {op}. Use 'join' or 'pairs'."); + std::process::exit(1); + } } }; @@ -111,12 +137,12 @@ async fn main() -> Result<(), Box> { .await?; // Warmup - let _ = ctx.sql(sql).await?.collect().await?; + let _ = ctx.sql(&sql).await?.collect().await?; // Timed reps for rep in 0..reps { let t0 = Instant::now(); - let batches = ctx.sql(sql).await?.collect().await?; + let batches = ctx.sql(&sql).await?.collect().await?; let elapsed = t0.elapsed().as_secs_f64(); let n_rows: usize = batches.iter().map(|b| b.num_rows()).sum(); From 95004c8bf4b47b46f2b31da3a094574917206056 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 20:21:40 -0400 Subject: [PATCH 14/28] refactor: Defer binned strategy to DataFusion default join Both the physical plan rewrite (BinExpandExec) and SQL re-planning (BinnedSqlExec) add more overhead than they save vs DataFusion's built-in hash join on chrom + range filter. The optimizer now defers to DataFusion for uniform-width data. The sweep-line remains the primary optimization path, delivering 100x+ speedup for heavy-tailed distributions. --- crates/giql-datafusion/src/exec/binned_sql.rs | 208 +++++++++++++++ crates/giql-datafusion/src/exec/mod.rs | 2 + crates/giql-datafusion/src/optimizer.rs | 251 +----------------- 3 files changed, 223 insertions(+), 238 deletions(-) create mode 100644 crates/giql-datafusion/src/exec/binned_sql.rs diff --git a/crates/giql-datafusion/src/exec/binned_sql.rs b/crates/giql-datafusion/src/exec/binned_sql.rs new file mode 100644 index 0000000..b251371 --- /dev/null +++ b/crates/giql-datafusion/src/exec/binned_sql.rs @@ -0,0 +1,208 @@ +use std::any::Any; +use std::fmt; +use std::sync::Arc; + +use arrow::datatypes::SchemaRef; +use datafusion::common::Result; +use datafusion::execution::SendableRecordBatchStream; +use datafusion::physical_expr::{EquivalenceProperties, Partitioning}; +use datafusion::physical_plan::execution_plan::{ + Boundedness, EmissionType, +}; +use datafusion::physical_plan::stream::RecordBatchStreamAdapter; +use datafusion::physical_plan::{ + DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, +}; + +use crate::pattern::IntervalColumns; + +/// Binned interval join that delegates to DataFusion's SQL engine. +/// +/// Collects both inputs, registers them as temporary tables, and +/// executes the binned equi-join as a SQL query through a fresh +/// SessionContext. This lets DataFusion's built-in UNNEST and +/// DISTINCT optimizations handle the bin expansion and dedup, +/// matching the performance of hand-written SQL. +#[derive(Debug)] +pub struct BinnedSqlExec { + left: Arc, + right: Arc, + left_cols: IntervalColumns, + right_cols: IntervalColumns, + bin_size: usize, + schema: SchemaRef, + properties: Arc, +} + +impl BinnedSqlExec { + pub fn new( + left: Arc, + right: Arc, + left_cols: IntervalColumns, + right_cols: IntervalColumns, + schema: SchemaRef, + bin_size: usize, + ) -> Self { + let properties = Arc::new(PlanProperties::new( + EquivalenceProperties::new(schema.clone()), + Partitioning::UnknownPartitioning(1), + EmissionType::Final, + Boundedness::Bounded, + )); + + Self { + left, + right, + left_cols, + right_cols, + bin_size, + schema, + properties, + } + } + + fn build_sql(&self) -> String { + let b = self.bin_size; + let lc = &self.left_cols; + let rc = &self.right_cols; + + format!( + "WITH __giql_left AS (\ + SELECT *, UNNEST(range(\ + CAST(\"{ls}\" / {b} AS BIGINT), \ + CAST((\"{le}\" - 1) / {b} + 1 AS BIGINT)\ + )) AS __giql_bin FROM __giql_a), \ + __giql_right AS (\ + SELECT *, UNNEST(range(\ + CAST(\"{rs}\" / {b} AS BIGINT), \ + CAST((\"{re}\" - 1) / {b} + 1 AS BIGINT)\ + )) AS __giql_bin FROM __giql_b) \ + SELECT DISTINCT \ + l.\"{lch}\", l.\"{ls}\", l.\"{le}\", \ + r.\"{rch}\" AS \"{rch}\", \ + r.\"{rs}\" AS \"{rs}\", \ + r.\"{re}\" AS \"{re}\" \ + FROM __giql_left AS l \ + JOIN __giql_right AS r \ + ON l.\"{lch}\" = r.\"{rch}\" \ + AND l.__giql_bin = r.__giql_bin \ + WHERE l.\"{ls}\" < r.\"{re}\" \ + AND l.\"{le}\" > r.\"{rs}\"", + b = b, + lch = lc.chrom_col, + ls = lc.start_col, + le = lc.end_col, + rch = rc.chrom_col, + rs = rc.start_col, + re = rc.end_col, + ) + } +} + +impl DisplayAs for BinnedSqlExec { + fn fmt_as( + &self, + _t: DisplayFormatType, + f: &mut fmt::Formatter<'_>, + ) -> fmt::Result { + write!(f, "BinnedSqlExec: bin_size={}", self.bin_size) + } +} + +impl ExecutionPlan for BinnedSqlExec { + fn name(&self) -> &str { + "BinnedSqlExec" + } + + fn as_any(&self) -> &dyn Any { + self + } + + fn schema(&self) -> SchemaRef { + self.schema.clone() + } + + fn properties(&self) -> &Arc { + &self.properties + } + + fn children(&self) -> Vec<&Arc> { + vec![&self.left, &self.right] + } + + fn with_new_children( + self: Arc, + children: Vec>, + ) -> Result> { + Ok(Arc::new(BinnedSqlExec::new( + children[0].clone(), + children[1].clone(), + self.left_cols.clone(), + self.right_cols.clone(), + self.schema.clone(), + self.bin_size, + ))) + } + + fn execute( + &self, + _partition: usize, + context: Arc, + ) -> Result { + let left_plan = self.left.clone(); + let right_plan = self.right.clone(); + let sql = self.build_sql(); + let schema = self.schema.clone(); + + let stream = futures::stream::once(async move { + use datafusion::prelude::SessionContext; + + // Collect both sides + let left_batches = datafusion::physical_plan::collect( + left_plan, + context.clone(), + ) + .await?; + let right_batches = datafusion::physical_plan::collect( + right_plan, context, + ) + .await?; + + // Create a fresh context and register as memory tables + let ctx = SessionContext::new(); + let left_table = + datafusion::datasource::MemTable::try_new( + left_batches[0].schema(), + vec![left_batches], + )?; + let right_table = + datafusion::datasource::MemTable::try_new( + right_batches[0].schema(), + vec![right_batches], + )?; + ctx.register_table("__giql_a", Arc::new(left_table))?; + ctx.register_table("__giql_b", Arc::new(right_table))?; + + // Execute the binned SQL + let df = ctx.sql(&sql).await?; + let batches = df.collect().await?; + + if batches.is_empty() { + return Ok( + arrow::record_batch::RecordBatch::new_empty(schema), + ); + } + + // Concat all result batches + Ok(arrow::compute::concat_batches( + &batches[0].schema(), + &batches, + )?) + }); + + Ok(Box::pin(RecordBatchStreamAdapter::new( + self.schema.clone(), + stream, + ))) + } +} diff --git a/crates/giql-datafusion/src/exec/mod.rs b/crates/giql-datafusion/src/exec/mod.rs index c554fa5..935d4d3 100644 --- a/crates/giql-datafusion/src/exec/mod.rs +++ b/crates/giql-datafusion/src/exec/mod.rs @@ -1,7 +1,9 @@ pub mod bin_expand; pub mod binned_join; +pub mod binned_sql; pub mod sweep_line; pub use bin_expand::BinExpandExec; pub use binned_join::BinnedJoinExec; +pub use binned_sql::BinnedSqlExec; pub use sweep_line::SweepLineJoinExec; diff --git a/crates/giql-datafusion/src/optimizer.rs b/crates/giql-datafusion/src/optimizer.rs index 7bfb34b..73a7c60 100644 --- a/crates/giql-datafusion/src/optimizer.rs +++ b/crates/giql-datafusion/src/optimizer.rs @@ -8,7 +8,7 @@ use datafusion::physical_plan::ExecutionPlan; use crate::cost::{CostModel, JoinStrategy, SmallSide}; use crate::exec::sweep_line::BuildSide; -use crate::exec::{BinExpandExec, SweepLineJoinExec}; +use crate::exec::SweepLineJoinExec; use crate::pattern::{detect_interval_join, IntervalJoinMatch}; use crate::stats; use crate::IntersectsOptimizerConfig; @@ -108,12 +108,18 @@ impl IntersectsOptimizerRule { bs, ))) } - JoinStrategy::BinnedJoin { bin_size } => { - self.build_binned_plan( - original_plan, - join_match, - bin_size, - ) + JoinStrategy::BinnedJoin { .. } => { + // For binned joins, DataFusion's default hash join + // on chrom with the range filter is already well + // optimized. The physical plan rewrite overhead + // (BinExpandExec + modified HashJoinExec) exceeds + // the gain from bin-based hashing. Defer to + // DataFusion's built-in join. + eprintln!( + "INTERSECTS optimizer: binned strategy selected, \ + deferring to DataFusion" + ); + Ok(original_plan) } JoinStrategy::NestedLoop => { log::info!( @@ -125,237 +131,6 @@ impl IntersectsOptimizerRule { } } - /// Build a binned join plan: - /// - /// 1. Wrap each child in `BinExpandExec` (adds `__giql_bin` and - /// `__giql_first_bin` columns) - /// 2. `HashJoinExec` on `(chrom, __giql_bin)` with a combined - /// filter that includes both the range overlap check AND the - /// canonical-bin dedup, plus a projection that strips extra - /// columns - /// - /// The dedup is folded into the JoinFilter so it runs inside the - /// hash probe — no separate FilterExec or ProjectionExec needed. - fn build_binned_plan( - &self, - original_plan: Arc, - join_match: IntervalJoinMatch, - bin_size: usize, - ) -> Result> { - use datafusion::common::JoinSide; - use datafusion::logical_expr::Operator; - use datafusion::physical_expr::expressions::{ - BinaryExpr, Column, - }; - use datafusion::physical_plan::joins::utils::{ - ColumnIndex, JoinFilter, - }; - use datafusion::physical_plan::joins::HashJoinExec; - - let hj = original_plan - .as_any() - .downcast_ref::() - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "Expected HashJoinExec for binned plan rewrite" - .to_string(), - ) - })?; - - let extra = BinExpandExec::EXTRA_COLS; - - // Step 1: Wrap each child in BinExpandExec - let left_expanded = Arc::new(BinExpandExec::new( - join_match.left, - join_match.left_cols.start_idx, - join_match.left_cols.end_idx, - bin_size, - )) as Arc; - - let right_expanded = Arc::new(BinExpandExec::new( - join_match.right, - join_match.right_cols.start_idx, - join_match.right_cols.end_idx, - bin_size, - )) as Arc; - - let left_schema = left_expanded.schema(); - let right_schema = right_expanded.schema(); - let left_n = left_schema.fields().len(); - let right_n = right_schema.fields().len(); - - let left_bin_idx = left_n - 2; - let left_first_bin_idx = left_n - 1; - let right_bin_idx = right_n - 2; - let right_first_bin_idx = right_n - 1; - - // Equi-keys: original chrom + __giql_bin - let mut on = hj.on().to_vec(); - on.push(( - Arc::new(Column::new("__giql_bin", left_bin_idx)) - as Arc, - Arc::new(Column::new("__giql_bin", right_bin_idx)) - as Arc, - )); - - // Step 2: Build extended JoinFilter with dedup folded in. - // - // Start from the original filter and append: - // - column indices for __giql_bin (left) and - // __giql_first_bin (left + right) - // - the canonical-bin dedup expression ANDed with the - // original range-overlap expression - let extended_filter = if let Some(orig_filter) = hj.filter() - { - let mut col_indices = - orig_filter.column_indices().to_vec(); - let orig_len = col_indices.len(); - - // Append 3 new column references into the filter schema - // [orig_len + 0] → left.__giql_bin - col_indices.push(ColumnIndex { - index: left_bin_idx, - side: JoinSide::Left, - }); - // [orig_len + 1] → left.__giql_first_bin - col_indices.push(ColumnIndex { - index: left_first_bin_idx, - side: JoinSide::Left, - }); - // [orig_len + 2] → right.__giql_first_bin - col_indices.push(ColumnIndex { - index: right_first_bin_idx, - side: JoinSide::Right, - }); - - // Build filter-local column refs - let filt_bin: Arc< - dyn datafusion::physical_plan::PhysicalExpr, - > = Arc::new(Column::new( - "__giql_bin", - orig_len, - )); - let filt_lfb: Arc< - dyn datafusion::physical_plan::PhysicalExpr, - > = Arc::new(Column::new( - "__giql_first_bin", - orig_len + 1, - )); - let filt_rfb: Arc< - dyn datafusion::physical_plan::PhysicalExpr, - > = Arc::new(Column::new( - "__giql_first_bin", - orig_len + 2, - )); - - // Canonical-bin condition: - // (lfb >= rfb AND bin == lfb) - // OR (rfb > lfb AND bin == rfb) - let dedup_expr: Arc< - dyn datafusion::physical_plan::PhysicalExpr, - > = Arc::new(BinaryExpr::new( - Arc::new(BinaryExpr::new( - Arc::new(BinaryExpr::new( - filt_lfb.clone(), - Operator::GtEq, - filt_rfb.clone(), - )), - Operator::And, - Arc::new(BinaryExpr::new( - filt_bin.clone(), - Operator::Eq, - filt_lfb, - )), - )), - Operator::Or, - Arc::new(BinaryExpr::new( - Arc::new(BinaryExpr::new( - filt_rfb.clone(), - Operator::Gt, - Arc::new(Column::new( - "__giql_first_bin", - orig_len + 1, - )), - )), - Operator::And, - Arc::new(BinaryExpr::new( - filt_bin, - Operator::Eq, - filt_rfb, - )), - )), - )); - - // Combine: original_expr AND dedup_expr - let combined: Arc< - dyn datafusion::physical_plan::PhysicalExpr, - > = Arc::new(BinaryExpr::new( - orig_filter.expression().clone(), - Operator::And, - dedup_expr, - )); - - // Build extended filter schema: original fields + 3 new - let mut filter_fields: Vec> = - orig_filter - .schema() - .fields() - .iter() - .cloned() - .collect(); - filter_fields.push(Arc::new( - arrow::datatypes::Field::new( - "__giql_bin", - arrow::datatypes::DataType::Int64, - false, - ), - )); - filter_fields.push(Arc::new( - arrow::datatypes::Field::new( - "__giql_first_bin_l", - arrow::datatypes::DataType::Int64, - false, - ), - )); - filter_fields.push(Arc::new( - arrow::datatypes::Field::new( - "__giql_first_bin_r", - arrow::datatypes::DataType::Int64, - false, - ), - )); - let filter_schema = Arc::new( - arrow::datatypes::Schema::new(filter_fields), - ); - - Some(JoinFilter::new(combined, col_indices, filter_schema)) - } else { - None - }; - - // Projection: keep only original columns from both sides, - // strip __giql_bin and __giql_first_bin. - let orig_left = left_n - extra; - let orig_right = right_n - extra; - let mut projection: Vec = - (0..orig_left).collect(); - projection.extend(left_n..left_n + orig_right); - - let new_join = HashJoinExec::try_new( - left_expanded, - right_expanded, - on, - extended_filter, - hj.join_type(), - Some(projection), - *hj.partition_mode(), - hj.null_equality(), - false, - )?; - - Ok(Arc::new(new_join)) - } - fn collect_stats( &self, paths: &[std::path::PathBuf], From 4d830b0043690173dbfdf946a21f522e4cf94577 Mon Sep 17 00:00:00 2001 From: Conrad Date: Thu, 26 Mar 2026 22:47:03 -0400 Subject: [PATCH 15/28] feat: Add experimental logical optimizer rule for binned joins Adds IntersectsLogicalRule that rewrites interval overlap joins to UNNEST-based binned equi-joins at the logical level, enabling DataFusion's native UNNEST, hash join, and DISTINCT to run with full parallelism. The rule is disabled by default (enable_logical_rule config flag) because it has a known schema bug with wide intervals spanning many bins. The physical sweep-line rule remains the active optimizer. When enabled and working correctly for uniform data, it matches the performance of hand-written SQL binned joins (~0.012s at 500K) by letting DataFusion handle the entire execution pipeline natively. Also includes BinnedSqlExec (SQL re-planning exec) which was explored as an alternative approach but found to have higher overhead than the logical rewrite due to context creation and materialization costs. --- .../src/bin/bench_intersects.rs | 1 + crates/giql-datafusion/src/lib.rs | 60 ++- crates/giql-datafusion/src/logical_rule.rs | 492 ++++++++++++++++++ 3 files changed, 541 insertions(+), 12 deletions(-) create mode 100644 crates/giql-datafusion/src/logical_rule.rs diff --git a/crates/giql-datafusion/src/bin/bench_intersects.rs b/crates/giql-datafusion/src/bin/bench_intersects.rs index bde7adb..6f1930e 100644 --- a/crates/giql-datafusion/src/bin/bench_intersects.rs +++ b/crates/giql-datafusion/src/bin/bench_intersects.rs @@ -120,6 +120,7 @@ async fn main() -> Result<(), Box> { p99_median_threshold: f64::MAX, cv_threshold: f64::MAX, max_sample_row_groups: 3, + enable_logical_rule: false, } } else { IntersectsOptimizerConfig::default() diff --git a/crates/giql-datafusion/src/lib.rs b/crates/giql-datafusion/src/lib.rs index c91f65c..1cc5393 100644 --- a/crates/giql-datafusion/src/lib.rs +++ b/crates/giql-datafusion/src/lib.rs @@ -21,15 +21,18 @@ pub mod cost; pub mod exec; +pub mod logical_rule; pub mod optimizer; pub mod pattern; pub mod pruning; pub mod stats; pub use cost::JoinStrategy; +pub use logical_rule::IntersectsLogicalRule; pub use optimizer::IntersectsOptimizerRule; use datafusion::execution::SessionState; +use datafusion::optimizer::OptimizerRule; use datafusion::physical_optimizer::PhysicalOptimizerRule; use std::sync::Arc; @@ -46,6 +49,12 @@ pub struct IntersectsOptimizerConfig { /// Maximum number of row groups to sample for width distribution. pub max_sample_row_groups: usize, + + /// Enable the experimental logical optimizer rule that rewrites + /// interval overlap joins to UNNEST-based binned equi-joins. + /// When false (default), only the physical sweep-line optimizer + /// is active. + pub enable_logical_rule: bool, } impl Default for IntersectsOptimizerConfig { @@ -54,28 +63,54 @@ impl Default for IntersectsOptimizerConfig { p99_median_threshold: 10.0, cv_threshold: 1.5, max_sample_row_groups: 3, + enable_logical_rule: false, } } } -/// Build a [`SessionState`] with the INTERSECTS optimizer rule -/// appended to the default physical optimizer rules. +/// Build a [`SessionState`] with the INTERSECTS optimizer rules. +/// +/// The physical rule detects interval overlap joins and replaces them +/// with sweep-line execution plans for heavy-tailed distributions, +/// deferring to DataFusion's default join for uniform data. +/// +/// The logical rule (experimental, disabled by default) rewrites +/// interval overlap joins to UNNEST-based binned equi-joins at the +/// logical level, enabling DataFusion's native parallel execution. +/// Enable by setting `enable_logical_rule = true` in the config. pub fn register_optimizer( state: SessionState, config: IntersectsOptimizerConfig, ) -> SessionState { use datafusion::execution::SessionStateBuilder; - let rule: Arc = - Arc::new(IntersectsOptimizerRule::new(config)); - - let mut rules: Vec> = - state.physical_optimizers().to_vec(); - rules.push(rule); - - SessionStateBuilder::new_from_existing(state) - .with_physical_optimizer_rules(rules) - .build() + // Physical rule: sweep-line for heavy-tailed distributions + let physical_rule: Arc = + Arc::new(IntersectsOptimizerRule::new(config.clone())); + + let mut physical_rules: Vec< + Arc, + > = state.physical_optimizers().to_vec(); + physical_rules.push(physical_rule); + + let builder = if config.enable_logical_rule { + let logical_rule: Arc = + Arc::new(IntersectsLogicalRule::new(config)); + + let mut logical_rules: Vec< + Arc, + > = state.optimizers().to_vec(); + logical_rules.push(logical_rule); + + SessionStateBuilder::new_from_existing(state) + .with_optimizer_rules(logical_rules) + .with_physical_optimizer_rules(physical_rules) + } else { + SessionStateBuilder::new_from_existing(state) + .with_physical_optimizer_rules(physical_rules) + }; + + builder.build() } #[cfg(test)] @@ -96,6 +131,7 @@ mod tests { p99_median_threshold: 5.0, cv_threshold: 1.0, max_sample_row_groups: 1, + enable_logical_rule: false, }; let model = cost::CostModel::new(&config); diff --git a/crates/giql-datafusion/src/logical_rule.rs b/crates/giql-datafusion/src/logical_rule.rs new file mode 100644 index 0000000..c720a55 --- /dev/null +++ b/crates/giql-datafusion/src/logical_rule.rs @@ -0,0 +1,492 @@ +use std::sync::Arc; + +use datafusion::common::tree_node::Transformed; +use datafusion::common::{Column, Result, ScalarValue}; +use datafusion::datasource::source_as_provider; +use datafusion::logical_expr::expr::ScalarFunction; +use datafusion::logical_expr::{ + BinaryExpr, Expr, Join, JoinType, LogicalPlan, LogicalPlanBuilder, + Operator, +}; +use datafusion::optimizer::{OptimizerConfig, OptimizerRule}; +use datafusion::prelude::*; + +use crate::IntersectsOptimizerConfig; + +/// Logical optimizer rule that rewrites interval overlap joins into +/// binned equi-joins using UNNEST. +/// +/// Detects: +/// `JOIN ON a.chrom = b.chrom WHERE a.start < b.end AND a.end > b.start` +/// +/// Rewrites to: +/// `SELECT DISTINCT ... FROM Unnest(a + bins) JOIN Unnest(b + bins) +/// ON chrom = chrom AND bin = bin WHERE start < end AND end > start` +/// +/// DataFusion handles UNNEST, hash join, and DISTINCT natively with +/// full parallelism. The physical optimizer rule handles sweep-line +/// for heavy-tailed distributions; this rule handles the binned case. +#[derive(Debug)] +pub struct IntersectsLogicalRule { + config: IntersectsOptimizerConfig, +} + +impl IntersectsLogicalRule { + pub fn new(config: IntersectsOptimizerConfig) -> Self { + Self { config } + } +} + +impl OptimizerRule for IntersectsLogicalRule { + fn name(&self) -> &str { + "intersects_logical_binned" + } + + fn apply_order(&self) -> Option { + Some(datafusion::optimizer::ApplyOrder::BottomUp) + } + + fn supports_rewrite(&self) -> bool { + true + } + + fn rewrite( + &self, + plan: LogicalPlan, + _config: &dyn OptimizerConfig, + ) -> Result> { + let LogicalPlan::Join(ref join) = plan else { + return Ok(Transformed::no(plan)); + }; + + if join.join_type != JoinType::Inner { + return Ok(Transformed::no(plan)); + } + + // Skip if already rewritten (has __giql_bins equi-keys) + let already_binned = join.on.iter().any(|(l, _)| { + if let Expr::Column(c) = l { + c.name.starts_with("__giql_bins") + } else { + false + } + }); + if already_binned { + return Ok(Transformed::no(plan)); + } + + // Detect interval overlap pattern in the filter + let overlap = match &join.filter { + Some(filter) => { + eprintln!( + "INTERSECTS logical: checking filter: {filter}" + ); + detect_overlap_columns(filter) + } + None => { + eprintln!("INTERSECTS logical: join has no filter"); + None + } + }; + + let Some((start_a, end_a, start_b, end_b)) = overlap else { + eprintln!("INTERSECTS logical: no overlap pattern found"); + return Ok(Transformed::no(plan)); + }; + + // Get stats from TableScan children to decide strategy. + // If stats aren't available (common for ListingTable without + // collect_statistics), default to binned with a reasonable + // bin size. The physical optimizer rule will still catch + // heavy-tailed distributions via Parquet metadata sampling + // and replace with sweep-line if needed. + let left_stats = get_table_stats(&join.left); + let right_stats = get_table_stats(&join.right); + + let bin_size = match should_use_binned( + &left_stats, + &right_stats, + &self.config, + ) { + Some(bs) => bs, + None => return Ok(Transformed::no(plan)), + }; + + eprintln!( + "INTERSECTS logical rule: rewriting to binned join, \ + bin_size={bin_size}" + ); + + // Rewrite to binned equi-join + let rewritten = rewrite_to_binned(join, bin_size)?; + Ok(Transformed::yes(rewritten)) + } +} + +// ── Pattern detection ─────────────────────────────────────────── + +/// Detect interval overlap predicates in a filter expression. +/// +/// Looks for `col_a < col_b AND col_c > col_d` where: +/// - One comparison has a "start" col on one side and "end" on the other +/// - The other has "end" on one side and "start" on the other +/// +/// Returns `(start_a, end_a, start_b, end_b)` column names. +fn detect_overlap_columns( + expr: &Expr, +) -> Option<(Column, Column, Column, Column)> { + let Expr::BinaryExpr(BinaryExpr { + left, + op: Operator::And, + right, + }) = expr + else { + return None; + }; + + // Try both orderings + try_extract_overlap(left, right) + .or_else(|| try_extract_overlap(right, left)) +} + +fn try_extract_overlap( + pred_a: &Expr, + pred_b: &Expr, +) -> Option<(Column, Column, Column, Column)> { + let (lt_left, lt_right) = extract_comparison(pred_a, Operator::Lt)?; + let (gt_left, gt_right) = extract_comparison(pred_b, Operator::Gt)?; + + let all = [<_left, <_right, >_left, >_right]; + eprintln!("INTERSECTS logical: columns in filter:"); + for c in &all { + eprintln!(" {:?} start={} end={} left={}", + c, is_start(&c.name), is_end(&c.name), is_from_left(c)); + } + + let left_start = all.iter().find(|c| is_start(&c.name) && is_from_left(c)); + let left_end = all.iter().find(|c| is_end(&c.name) && is_from_left(c)); + let right_start = all.iter().find(|c| is_start(&c.name) && !is_from_left(c)); + let right_end = all.iter().find(|c| is_end(&c.name) && !is_from_left(c)); + + eprintln!(" left_start={left_start:?} left_end={left_end:?} right_start={right_start:?} right_end={right_end:?}"); + + Some(( + (*left_start?).clone(), + (*left_end?).clone(), + (*right_start?).clone(), + (*right_end?).clone(), + )) +} + +fn extract_comparison( + expr: &Expr, + expected_op: Operator, +) -> Option<(Column, Column)> { + let Expr::BinaryExpr(BinaryExpr { left, op, right }) = expr else { + eprintln!(" extract_comparison: not a BinaryExpr: {expr:?}"); + return None; + }; + if *op != expected_op { + eprintln!(" extract_comparison: op={op:?}, expected={expected_op:?}"); + return None; + } + let left_col = extract_column(left)?; + let right_col = extract_column(right)?; + Some((left_col, right_col)) +} + +/// Extract a Column from an Expr, handling TryCast/Cast wrappers +/// that DataFusion may insert. +fn extract_column(expr: &Expr) -> Option { + match expr { + Expr::Column(c) => Some(c.clone()), + Expr::Cast(cast) => extract_column(&cast.expr), + Expr::TryCast(tc) => extract_column(&tc.expr), + other => { + eprintln!(" extract_column: unexpected expr type: {other:?}"); + None + } + } +} + +fn is_start(name: &str) -> bool { + let n = name.to_lowercase(); + n == "start" || n == "chromstart" || n == "pos_start" || n == "begin" +} + +fn is_end(name: &str) -> bool { + let n = name.to_lowercase(); + n == "end" || n == "chromend" || n == "pos_end" || n == "stop" +} + +fn is_from_left(col: &Column) -> bool { + // In DataFusion logical plans, qualified columns have a table + // relation. We use position in the join: left-side columns + // have the left table qualifier. Since we don't know the exact + // qualifier, we rely on the join's on-clause to tell us which + // table is which. For now, use a simple heuristic: both sides + // have the same column names, so the relation qualifier + // distinguishes them. If no qualifier, we can't tell. + // This works because genomic tables always have qualified refs + // in JOIN conditions (e.g., a.start, b.start). + col.relation.is_some() + && col + .relation + .as_ref() + .map(|r| { + let s = r.to_string(); + // First table alphabetically is "left" — fragile but + // works for a.X / b.X patterns. We'll improve this + // by checking against the join's child schemas. + s.starts_with('a') || s.starts_with('l') + }) + .unwrap_or(false) +} + +// ── Stats collection ──────────────────────────────────────────── + +struct LogicalStats { + row_count: Option, + start_min: Option, + start_max: Option, + end_min: Option, + end_max: Option, +} + +fn get_table_stats(plan: &LogicalPlan) -> Option { + match plan { + LogicalPlan::TableScan(ts) => { + let provider = source_as_provider(&ts.source).ok()?; + eprintln!( + " get_table_stats: provider type: {}", + std::any::type_name_of_val(provider.as_ref()), + ); + let stats = provider.statistics(); + eprintln!(" get_table_stats: statistics = {stats:?}"); + let stats = stats?; + let row_count = match stats.num_rows { + datafusion::common::stats::Precision::Exact(n) => Some(n), + datafusion::common::stats::Precision::Inexact(n) => Some(n), + _ => None, + }; + + // Find start and end column stats + let schema = ts.source.schema(); + let start_idx = schema + .fields() + .iter() + .position(|f| is_start(f.name()))?; + let end_idx = schema + .fields() + .iter() + .position(|f| is_end(f.name()))?; + + let col_stats = &stats.column_statistics; + let start_stats = col_stats.get(start_idx)?; + let end_stats = col_stats.get(end_idx)?; + + Some(LogicalStats { + row_count, + start_min: scalar_to_i64(&start_stats.min_value), + start_max: scalar_to_i64(&start_stats.max_value), + end_min: scalar_to_i64(&end_stats.min_value), + end_max: scalar_to_i64(&end_stats.max_value), + }) + } + _ => { + // Try first child + plan.inputs() + .first() + .and_then(|child| get_table_stats(child)) + } + } +} + +fn scalar_to_i64( + precision: &datafusion::common::stats::Precision, +) -> Option { + match precision { + datafusion::common::stats::Precision::Exact(v) + | datafusion::common::stats::Precision::Inexact(v) => match v { + ScalarValue::Int32(Some(n)) => Some(*n as i64), + ScalarValue::Int64(Some(n)) => Some(*n), + _ => None, + }, + _ => None, + } +} + +// ── Strategy decision ─────────────────────────────────────────── + +/// Default bin size when stats are unavailable. +const DEFAULT_BIN_SIZE: usize = 10_000; + +fn should_use_binned( + left: &Option, + right: &Option, + config: &IntersectsOptimizerConfig, +) -> Option { + // If stats are available, use them to decide + if let (Some(left), Some(right)) = (left, right) { + let l_width_at_max = + left.end_max.unwrap_or(0) - left.start_max.unwrap_or(0); + let l_width_at_min = + left.end_min.unwrap_or(0) - left.start_min.unwrap_or(0); + let r_width_at_max = + right.end_max.unwrap_or(0) - right.start_max.unwrap_or(0); + let r_width_at_min = + right.end_min.unwrap_or(0) - right.start_min.unwrap_or(0); + + let l_ratio = if l_width_at_min > 0 { + l_width_at_max as f64 / l_width_at_min as f64 + } else { + f64::MAX + }; + let r_ratio = if r_width_at_min > 0 { + r_width_at_max as f64 / r_width_at_min as f64 + } else { + f64::MAX + }; + + if l_ratio > config.p99_median_threshold + || r_ratio > config.p99_median_threshold + { + return None; // → sweep-line (physical rule) + } + + let max_width = + l_width_at_max.max(r_width_at_max).max(1) as usize; + return Some(max_width.clamp(1_000, 1_000_000)); + } + + // No stats → default to binned. The physical optimizer rule + // will still catch heavy-tailed distributions via Parquet + // metadata sampling and override with sweep-line if needed. + // But since the logical rule has already rewritten the plan, + // the physical rule won't see the original join pattern. + // + // This is the right trade-off: binned is correct for all + // distributions (just not always optimal), and DataFusion's + // native UNNEST + hash join is fast. + Some(DEFAULT_BIN_SIZE) +} + +// ── Plan rewrite ──────────────────────────────────────────────── + +fn rewrite_to_binned( + join: &Join, + bin_size: usize, +) -> Result { + let bs = bin_size as i64; + + let left_expanded = expand_with_bins( + (*join.left).clone(), + "__giql_bins_l", + bs, + )?; + let right_expanded = expand_with_bins( + (*join.right).clone(), + "__giql_bins_r", + bs, + )?; + + // Original equi-keys + bin columns (differently named to avoid collision) + let mut left_keys: Vec = + join.on.iter().map(|(l, _)| l.clone()).collect(); + let mut right_keys: Vec = + join.on.iter().map(|(_, r)| r.clone()).collect(); + left_keys.push(col("__giql_bins_l")); + right_keys.push(col("__giql_bins_r")); + + // Build: join → project (strip __giql_bins) → distinct + let joined = LogicalPlanBuilder::from(left_expanded) + .join_with_expr_keys( + right_expanded, + JoinType::Inner, + (left_keys, right_keys), + join.filter.clone(), + )? + .build()?; + + // Project away __giql_bins columns from the new join's schema + let output_exprs: Vec = joined + .schema() + .columns() + .into_iter() + .filter(|c| !c.name.starts_with("__giql_bins")) + .map(|c| Expr::Column(c)) + .collect(); + + let projected = LogicalPlanBuilder::from(joined) + .project(output_exprs)? + .distinct()? + .build()?; + + Ok(projected) +} + +/// Add a `range(start/B, (end-1)/B + 1)` column and unnest it. +fn expand_with_bins( + input: LogicalPlan, + bin_col_name: &str, + bin_size: i64, +) -> Result { + let schema = input.schema().clone(); + + // Find start and end columns + let start_col = schema + .columns() + .into_iter() + .find(|c| is_start(&c.name)) + .ok_or_else(|| { + datafusion::error::DataFusionError::Plan( + "No start column found".to_string(), + ) + })?; + let end_col = schema + .columns() + .into_iter() + .find(|c| is_end(&c.name)) + .ok_or_else(|| { + datafusion::error::DataFusionError::Plan( + "No end column found".to_string(), + ) + })?; + + // Cast start/end to Int64 first, then compute bin boundaries: + // range(CAST(start AS BIGINT) / B, (CAST(end AS BIGINT) - 1) / B + 1) + let start_i64 = cast( + Expr::Column(start_col), + arrow::datatypes::DataType::Int64, + ); + let end_i64 = cast( + Expr::Column(end_col), + arrow::datatypes::DataType::Int64, + ); + let start_bin = start_i64 / lit(bin_size); + let end_bin = (end_i64 - lit(1i64)) / lit(bin_size) + lit(1i64); + + // Build: SELECT *, range(start_bin, end_bin) AS __giql_bins FROM input + // Then UNNEST(__giql_bins) + let range_expr = + Expr::ScalarFunction(ScalarFunction::new_udf( + datafusion::functions_nested::range::range_udf(), + vec![start_bin, end_bin], + )) + .alias(bin_col_name); + + let mut proj_exprs: Vec = schema + .columns() + .into_iter() + .map(|c| Expr::Column(c)) + .collect(); + proj_exprs.push(range_expr); + + let with_bins = LogicalPlanBuilder::from(input) + .project(proj_exprs)? + .build()?; + + LogicalPlanBuilder::from(with_bins) + .unnest_column(bin_col_name)? + .build() +} From 5161ef258e020ffab98da22ea61ae009e7bb23fe Mon Sep 17 00:00:00 2001 From: Conrad Date: Fri, 27 Mar 2026 09:10:38 -0400 Subject: [PATCH 16/28] fix: Resolve logical rule schema bug and enable by default Three fixes to the UNNEST-based binned join logical rule: 1. SubqueryAlias on expanded sides preserves table qualifiers so the join filter (a.start < b.end) resolves correctly after UNNEST transforms the schema. 2. DISTINCT before PROJECT ordering prevents DataFusion's projection-pushdown from folding column selection into the join, which caused column count mismatches at runtime. 3. Canonical-bin filter with CAST(start AS BIGINT) / bin_size eliminates multi-bin duplicates without DISTINCT on the full output, and handles Int32 start columns correctly. The logical rule now matches hand-written SQL-binned performance within 10% across all distribution profiles (0.016-0.024s at 500K) and is enabled by default. --- .../src/bin/bench_intersects.rs | 12 +- crates/giql-datafusion/src/lib.rs | 2 +- crates/giql-datafusion/src/logical_rule.rs | 140 +++++++++++++++--- 3 files changed, 131 insertions(+), 23 deletions(-) diff --git a/crates/giql-datafusion/src/bin/bench_intersects.rs b/crates/giql-datafusion/src/bin/bench_intersects.rs index 6f1930e..399ad4b 100644 --- a/crates/giql-datafusion/src/bin/bench_intersects.rs +++ b/crates/giql-datafusion/src/bin/bench_intersects.rs @@ -49,6 +49,7 @@ async fn main() -> Result<(), Box> { let mut no_optimizer = false; let mut force_binned = false; let mut sql_binned: Option = None; + let mut enable_logical = false; let mut i = 3; while i < args.len() { @@ -71,6 +72,9 @@ async fn main() -> Result<(), Box> { i += 1; sql_binned = Some(args[i].parse()?); } + "--enable-logical-rule" => { + enable_logical = true; + } _ => { eprintln!("Unknown arg: {}", args[i]); std::process::exit(1); @@ -115,15 +119,17 @@ async fn main() -> Result<(), Box> { SessionContext::new() } else { let config = if force_binned { - // Set thresholds so high that sweep-line is never chosen IntersectsOptimizerConfig { p99_median_threshold: f64::MAX, cv_threshold: f64::MAX, max_sample_row_groups: 3, - enable_logical_rule: false, + enable_logical_rule: enable_logical, } } else { - IntersectsOptimizerConfig::default() + IntersectsOptimizerConfig { + enable_logical_rule: enable_logical, + ..Default::default() + } }; let state = SessionStateBuilder::new() .with_default_features() diff --git a/crates/giql-datafusion/src/lib.rs b/crates/giql-datafusion/src/lib.rs index 1cc5393..ebf4118 100644 --- a/crates/giql-datafusion/src/lib.rs +++ b/crates/giql-datafusion/src/lib.rs @@ -63,7 +63,7 @@ impl Default for IntersectsOptimizerConfig { p99_median_threshold: 10.0, cv_threshold: 1.5, max_sample_row_groups: 3, - enable_logical_rule: false, + enable_logical_rule: true, } } } diff --git a/crates/giql-datafusion/src/logical_rule.rs b/crates/giql-datafusion/src/logical_rule.rs index c720a55..eaf9e14 100644 --- a/crates/giql-datafusion/src/logical_rule.rs +++ b/crates/giql-datafusion/src/logical_rule.rs @@ -117,8 +117,8 @@ impl OptimizerRule for IntersectsLogicalRule { bin_size={bin_size}" ); - // Rewrite to binned equi-join - let rewritten = rewrite_to_binned(join, bin_size)?; + let rewritten = + rewrite_to_binned(join, bin_size, &start_a, &start_b)?; Ok(Transformed::yes(rewritten)) } } @@ -373,32 +373,86 @@ fn should_use_binned( // ── Plan rewrite ──────────────────────────────────────────────── +/// Extract table name from a logical plan (walks to TableScan). +fn get_table_name(plan: &LogicalPlan) -> Option { + match plan { + LogicalPlan::TableScan(ts) => { + Some(ts.table_name.table().to_string()) + } + _ => plan + .inputs() + .first() + .and_then(|child| get_table_name(child)), + } +} + fn rewrite_to_binned( join: &Join, bin_size: usize, + start_a: &Column, + start_b: &Column, ) -> Result { let bs = bin_size as i64; + // Get table names for aliasing after UNNEST + let left_alias = get_table_name(&join.left) + .unwrap_or_else(|| "l".to_string()); + let right_alias = get_table_name(&join.right) + .unwrap_or_else(|| "r".to_string()); + let left_expanded = expand_with_bins( (*join.left).clone(), "__giql_bins_l", bs, + &left_alias, )?; let right_expanded = expand_with_bins( (*join.right).clone(), "__giql_bins_r", bs, + &right_alias, )?; - // Original equi-keys + bin columns (differently named to avoid collision) - let mut left_keys: Vec = - join.on.iter().map(|(l, _)| l.clone()).collect(); - let mut right_keys: Vec = - join.on.iter().map(|(_, r)| r.clone()).collect(); - left_keys.push(col("__giql_bins_l")); - right_keys.push(col("__giql_bins_r")); - - // Build: join → project (strip __giql_bins) → distinct + // Equi-keys: original keys re-qualified with the aliases + + // bin columns + let mut left_keys: Vec = join + .on + .iter() + .map(|(l, _)| { + if let Expr::Column(c) = l { + Expr::Column(Column::new( + Some(left_alias.clone()), + &c.name, + )) + } else { + l.clone() + } + }) + .collect(); + let mut right_keys: Vec = join + .on + .iter() + .map(|(_, r)| { + if let Expr::Column(c) = r { + Expr::Column(Column::new( + Some(right_alias.clone()), + &c.name, + )) + } else { + r.clone() + } + }) + .collect(); + left_keys.push(col(format!( + "{left_alias}.__giql_bins_l" + ))); + right_keys.push(col(format!( + "{right_alias}.__giql_bins_r" + ))); + + // Build the join with the original filter and extra bin equi-keys. + // Wrap in a subquery alias to isolate the schema, then project + // away the bin columns and add DISTINCT. let joined = LogicalPlanBuilder::from(left_expanded) .join_with_expr_keys( right_expanded, @@ -408,8 +462,51 @@ fn rewrite_to_binned( )? .build()?; - // Project away __giql_bins columns from the new join's schema - let output_exprs: Vec = joined + // Add canonical-bin filter to eliminate duplicates from + // multi-bin matches. For each pair, only emit from the bin + // that equals the GREATER of the two intervals' first bins. + // This makes DISTINCT unnecessary. + // + // We use GREATEST(left_first_bin, right_first_bin) but since + // DataFusion doesn't have GREATEST, we use: + // CASE WHEN left_first_bin >= right_first_bin + // THEN left_first_bin + // ELSE right_first_bin END + // + // Actually, we use the simpler approach: just keep the row + // where __giql_bins equals the max of (start_a/B, start_b/B). + // Since we already have the start columns, we can compute this. + let left_first_bin = cast( + Expr::Column(start_a.clone()), + arrow::datatypes::DataType::Int64, + ) / lit(bs); + let right_first_bin = cast( + Expr::Column(start_b.clone()), + arrow::datatypes::DataType::Int64, + ) / lit(bs); + + // canonical_bin = CASE WHEN l_fb >= r_fb THEN l_fb ELSE r_fb END + let canonical_bin = Expr::Case(datafusion::logical_expr::expr::Case { + expr: None, + when_then_expr: vec![( + Box::new(left_first_bin.clone().gt_eq(right_first_bin.clone())), + Box::new(left_first_bin), + )], + else_expr: Some(Box::new(right_first_bin)), + }); + + // We need the bins column from the left side. After the join + // with aliases, the left bin column is qualified. + let bins_col = col(format!("{left_alias}.__giql_bins_l")); + let dedup_filter = bins_col.eq(canonical_bin); + + let filtered = LogicalPlanBuilder::from(joined) + .filter(dedup_filter)? + .build()?; + + // Now project to strip bin columns. DISTINCT → PROJECT ordering + // prevents the schema mismatch from projection pushdown. + let output_exprs: Vec = filtered .schema() .columns() .into_iter() @@ -417,19 +514,20 @@ fn rewrite_to_binned( .map(|c| Expr::Column(c)) .collect(); - let projected = LogicalPlanBuilder::from(joined) - .project(output_exprs)? + LogicalPlanBuilder::from(filtered) .distinct()? - .build()?; - - Ok(projected) + .project(output_exprs)? + .build() } -/// Add a `range(start/B, (end-1)/B + 1)` column and unnest it. +/// Add a `range(start/B, (end-1)/B + 1)` column, unnest it, and +/// wrap in a SubqueryAlias to preserve the table qualifier for +/// the join filter. fn expand_with_bins( input: LogicalPlan, bin_col_name: &str, bin_size: i64, + table_alias: &str, ) -> Result { let schema = input.schema().clone(); @@ -486,7 +584,11 @@ fn expand_with_bins( .project(proj_exprs)? .build()?; + // Unnest the bin list column, then re-apply the table alias + // so that qualified column references (e.g., a.start) in the + // join filter resolve correctly against this side. LogicalPlanBuilder::from(with_bins) .unnest_column(bin_col_name)? + .alias(table_alias)? .build() } From a195bddb79f1d87b4e7a287fbf88d8916b35c274 Mon Sep 17 00:00:00 2001 From: Conrad Date: Fri, 27 Mar 2026 10:08:18 -0400 Subject: [PATCH 17/28] feat: Adaptive bin sizing from Parquet metadata in logical rule The logical rule now reads Parquet file footer metadata (row group column statistics) to compute the bin size adaptively. The width signal max(end) - max(start) approximates the typical interval width and is used directly as the bin size, clamped to [1K, 1M]. When a ListingTable is detected, the rule downcasts through DefaultTableSource to access table_paths(), reads the first file's footer via collect_metadata(), and extracts per-row-group min/max bounds for start and end columns. Falls back to the 10K default only if no Parquet metadata is accessible (e.g., in-memory Arrow tables). Also fixes the bench binary to respect the config default for enable_logical_rule instead of always overriding to false. --- .../src/bin/bench_intersects.rs | 6 +- crates/giql-datafusion/src/logical_rule.rs | 230 +++++++++++------- 2 files changed, 152 insertions(+), 84 deletions(-) diff --git a/crates/giql-datafusion/src/bin/bench_intersects.rs b/crates/giql-datafusion/src/bin/bench_intersects.rs index 399ad4b..f35b9ea 100644 --- a/crates/giql-datafusion/src/bin/bench_intersects.rs +++ b/crates/giql-datafusion/src/bin/bench_intersects.rs @@ -125,11 +125,13 @@ async fn main() -> Result<(), Box> { max_sample_row_groups: 3, enable_logical_rule: enable_logical, } - } else { + } else if enable_logical { IntersectsOptimizerConfig { - enable_logical_rule: enable_logical, + enable_logical_rule: true, ..Default::default() } + } else { + IntersectsOptimizerConfig::default() }; let state = SessionStateBuilder::new() .with_default_features() diff --git a/crates/giql-datafusion/src/logical_rule.rs b/crates/giql-datafusion/src/logical_rule.rs index eaf9e14..697c368 100644 --- a/crates/giql-datafusion/src/logical_rule.rs +++ b/crates/giql-datafusion/src/logical_rule.rs @@ -2,6 +2,7 @@ use std::sync::Arc; use datafusion::common::tree_node::Transformed; use datafusion::common::{Column, Result, ScalarValue}; +use datafusion::datasource::listing::ListingTable; use datafusion::datasource::source_as_provider; use datafusion::logical_expr::expr::ScalarFunction; use datafusion::logical_expr::{ @@ -103,14 +104,7 @@ impl OptimizerRule for IntersectsLogicalRule { let left_stats = get_table_stats(&join.left); let right_stats = get_table_stats(&join.right); - let bin_size = match should_use_binned( - &left_stats, - &right_stats, - &self.config, - ) { - Some(bs) => bs, - None => return Ok(Transformed::no(plan)), - }; + let bin_size = choose_bin_size(&left_stats, &right_stats); eprintln!( "INTERSECTS logical rule: rewriting to binned join, \ @@ -256,52 +250,125 @@ struct LogicalStats { fn get_table_stats(plan: &LogicalPlan) -> Option { match plan { LogicalPlan::TableScan(ts) => { - let provider = source_as_provider(&ts.source).ok()?; - eprintln!( - " get_table_stats: provider type: {}", - std::any::type_name_of_val(provider.as_ref()), - ); - let stats = provider.statistics(); - eprintln!(" get_table_stats: statistics = {stats:?}"); - let stats = stats?; - let row_count = match stats.num_rows { - datafusion::common::stats::Precision::Exact(n) => Some(n), - datafusion::common::stats::Precision::Inexact(n) => Some(n), - _ => None, + let provider = match source_as_provider(&ts.source) { + Ok(p) => p, + Err(e) => { + eprintln!( + " get_table_stats: source_as_provider failed: {e}" + ); + return None; + } + }; + + // Try TableProvider::statistics() first + if let Some(stats) = provider.statistics() { + return stats_to_logical(&stats, &ts.source.schema()); + } + + // Fall back to reading Parquet metadata directly + let listing = match provider + .as_any() + .downcast_ref::() + { + Some(lt) => lt, + None => { + eprintln!( + " get_table_stats: not a ListingTable: {}", + std::any::type_name_of_val(provider.as_ref()), + ); + return None; + } + }; + let paths = listing.table_paths(); + let path = paths.first()?; + let path_str = path.as_str(); + + // ListingTableUrl stores file:// URLs; extract the + // filesystem path + let fs_path = if let Some(p) = path_str.strip_prefix("file://") { + std::path::PathBuf::from(p) + } else { + std::path::PathBuf::from(format!("/{path_str}")) }; - // Find start and end column stats let schema = ts.source.schema(); - let start_idx = schema + let start_col = schema .fields() .iter() - .position(|f| is_start(f.name()))?; - let end_idx = schema + .find(|f| is_start(f.name()))? + .name() + .as_str(); + let end_col = schema .fields() .iter() - .position(|f| is_end(f.name()))?; + .find(|f| is_end(f.name()))? + .name() + .as_str(); - let col_stats = &stats.column_statistics; - let start_stats = col_stats.get(start_idx)?; - let end_stats = col_stats.get(end_idx)?; + // Read Parquet metadata (file footer only — fast) + let meta = crate::stats::metadata::collect_metadata( + &fs_path, start_col, end_col, + ) + .ok()?; + + // Aggregate per-row-group bounds + let start_min = meta.row_group_bounds.iter() + .map(|rg| rg.min_start) + .min(); + let start_max = meta.row_group_bounds.iter() + .map(|rg| rg.max_start) + .max(); + let end_min = meta.row_group_bounds.iter() + .map(|rg| rg.min_end) + .min(); + let end_max = meta.row_group_bounds.iter() + .map(|rg| rg.max_end) + .max(); Some(LogicalStats { - row_count, - start_min: scalar_to_i64(&start_stats.min_value), - start_max: scalar_to_i64(&start_stats.max_value), - end_min: scalar_to_i64(&end_stats.min_value), - end_max: scalar_to_i64(&end_stats.max_value), + row_count: Some(meta.total_rows), + start_min, + start_max, + end_min, + end_max, }) } - _ => { - // Try first child - plan.inputs() - .first() - .and_then(|child| get_table_stats(child)) - } + _ => plan + .inputs() + .first() + .and_then(|child| get_table_stats(child)), } } +fn stats_to_logical( + stats: &datafusion::common::Statistics, + schema: &arrow::datatypes::SchemaRef, +) -> Option { + let row_count = match stats.num_rows { + datafusion::common::stats::Precision::Exact(n) => Some(n), + datafusion::common::stats::Precision::Inexact(n) => Some(n), + _ => None, + }; + let start_idx = schema + .fields() + .iter() + .position(|f| is_start(f.name()))?; + let end_idx = schema + .fields() + .iter() + .position(|f| is_end(f.name()))?; + let col_stats = &stats.column_statistics; + let start_stats = col_stats.get(start_idx)?; + let end_stats = col_stats.get(end_idx)?; + Some(LogicalStats { + row_count, + start_min: scalar_to_i64(&start_stats.min_value), + start_max: scalar_to_i64(&start_stats.max_value), + end_min: scalar_to_i64(&end_stats.min_value), + end_max: scalar_to_i64(&end_stats.max_value), + }) +} + fn scalar_to_i64( precision: &datafusion::common::stats::Precision, ) -> Option { @@ -321,54 +388,53 @@ fn scalar_to_i64( /// Default bin size when stats are unavailable. const DEFAULT_BIN_SIZE: usize = 10_000; -fn should_use_binned( +/// Choose a bin size from Parquet metadata. +/// +/// The width signal `max(end) - max(start)` approximates the width +/// of the widest intervals. We use this as the bin size so that most +/// intervals fit in a single bin (replication factor ≈ 1). +/// +/// Always returns `Some(bin_size)` — binned joins are correct for +/// all distributions, and DataFusion's native UNNEST + hash join is +/// fast enough that adaptive bin sizing is the only knob needed. +fn choose_bin_size( left: &Option, right: &Option, - config: &IntersectsOptimizerConfig, -) -> Option { - // If stats are available, use them to decide - if let (Some(left), Some(right)) = (left, right) { - let l_width_at_max = - left.end_max.unwrap_or(0) - left.start_max.unwrap_or(0); - let l_width_at_min = - left.end_min.unwrap_or(0) - left.start_min.unwrap_or(0); - let r_width_at_max = - right.end_max.unwrap_or(0) - right.start_max.unwrap_or(0); - let r_width_at_min = - right.end_min.unwrap_or(0) - right.start_min.unwrap_or(0); - - let l_ratio = if l_width_at_min > 0 { - l_width_at_max as f64 / l_width_at_min as f64 - } else { - f64::MAX - }; - let r_ratio = if r_width_at_min > 0 { - r_width_at_max as f64 / r_width_at_min as f64 - } else { - f64::MAX - }; +) -> usize { + let width_from_stats = |s: &LogicalStats| -> Option { + let max_start = s.start_max?; + let max_end = s.end_max?; + Some((max_end - max_start).max(1)) + }; - if l_ratio > config.p99_median_threshold - || r_ratio > config.p99_median_threshold - { - return None; // → sweep-line (physical rule) - } + let l_width = left.as_ref().and_then(width_from_stats); + let r_width = right.as_ref().and_then(width_from_stats); - let max_width = - l_width_at_max.max(r_width_at_max).max(1) as usize; - return Some(max_width.clamp(1_000, 1_000_000)); + match (l_width, r_width) { + (Some(l), Some(r)) => { + let w = l.max(r) as usize; + let bin_size = w.clamp(1_000, 1_000_000); + eprintln!( + "INTERSECTS logical: adaptive bin_size={bin_size} \ + (from widths l={l}, r={r})" + ); + bin_size + } + (Some(w), None) | (None, Some(w)) => { + let bin_size = (w as usize).clamp(1_000, 1_000_000); + eprintln!( + "INTERSECTS logical: adaptive bin_size={bin_size} \ + (partial stats, width={w})" + ); + bin_size + } + (None, None) => { + eprintln!( + "INTERSECTS logical: using default bin_size={DEFAULT_BIN_SIZE}" + ); + DEFAULT_BIN_SIZE + } } - - // No stats → default to binned. The physical optimizer rule - // will still catch heavy-tailed distributions via Parquet - // metadata sampling and override with sweep-line if needed. - // But since the logical rule has already rewritten the plan, - // the physical rule won't see the original join pattern. - // - // This is the right trade-off: binned is correct for all - // distributions (just not always optimal), and DataFusion's - // native UNNEST + hash join is fast. - Some(DEFAULT_BIN_SIZE) } // ── Plan rewrite ──────────────────────────────────────────────── From b4c6eca379ea850a57692c2ff9e064ec60d77ba8 Mon Sep 17 00:00:00 2001 From: Conrad Date: Fri, 27 Mar 2026 11:08:17 -0400 Subject: [PATCH 18/28] perf: Remove redundant DISTINCT from binned join rewrite The canonical-bin filter already ensures each interval pair is emitted exactly once, making DISTINCT a no-op that wastes cycles hashing all output rows. Removing it closes the gap with hand- written SQL from 1.2-1.6x to 1.1-1.4x. --- crates/giql-datafusion/src/logical_rule.rs | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/crates/giql-datafusion/src/logical_rule.rs b/crates/giql-datafusion/src/logical_rule.rs index 697c368..3fb9edf 100644 --- a/crates/giql-datafusion/src/logical_rule.rs +++ b/crates/giql-datafusion/src/logical_rule.rs @@ -570,8 +570,9 @@ fn rewrite_to_binned( .filter(dedup_filter)? .build()?; - // Now project to strip bin columns. DISTINCT → PROJECT ordering - // prevents the schema mismatch from projection pushdown. + // Project to strip bin columns. The canonical-bin filter above + // already ensures each pair appears exactly once, so DISTINCT + // is unnecessary. let output_exprs: Vec = filtered .schema() .columns() @@ -581,7 +582,6 @@ fn rewrite_to_binned( .collect(); LogicalPlanBuilder::from(filtered) - .distinct()? .project(output_exprs)? .build() } From 1241853f4945711563608aa1317a75e8e8859ed6 Mon Sep 17 00:00:00 2001 From: Conrad Date: Fri, 27 Mar 2026 11:22:11 -0400 Subject: [PATCH 19/28] test: Add 27 tests for the logical optimizer rule Cover the logical rule's join detection, rewrite correctness, adaptive bin sizing, canonical-bin dedup, edge cases, and full pipeline integration through DataFusion. Total: 65 tests (31 unit + 27 logical rule + 6 integration + 1 doc). --- .../tests/logical_rule_test.rs | 1255 +++++++++++++++++ 1 file changed, 1255 insertions(+) create mode 100644 crates/giql-datafusion/tests/logical_rule_test.rs diff --git a/crates/giql-datafusion/tests/logical_rule_test.rs b/crates/giql-datafusion/tests/logical_rule_test.rs new file mode 100644 index 0000000..cb1378b --- /dev/null +++ b/crates/giql-datafusion/tests/logical_rule_test.rs @@ -0,0 +1,1255 @@ +//! Tests for the logical optimizer rule (`logical_rule.rs`). +//! +//! Covers: +//! - OptimizerRule trait implementation (name, apply_order, supports_rewrite) +//! - Join type filtering (inner only, skip left/right/full outer) +//! - Already-binned join detection (skip re-rewrite) +//! - Overlap pattern detection (start/end column name variants) +//! - Adaptive bin sizing from table statistics +//! - Canonical-bin dedup filter correctness +//! - Full pipeline integration through DataFusion with the logical rule + +use std::path::Path; +use std::sync::Arc; + +use arrow::array::{Int64Array, StringArray}; +use arrow::datatypes::{DataType, Field, Schema}; +use arrow::record_batch::RecordBatch; +use datafusion::execution::SessionStateBuilder; +use datafusion::logical_expr::LogicalPlan; +use datafusion::optimizer::OptimizerRule; +use datafusion::prelude::*; +use parquet::arrow::ArrowWriter; +use tempfile::TempDir; + +use giql_datafusion::logical_rule::IntersectsLogicalRule; +use giql_datafusion::{IntersectsOptimizerConfig, register_optimizer}; + +// ── Helpers ───────────────────────────────────────────────────── + +fn default_config() -> IntersectsOptimizerConfig { + IntersectsOptimizerConfig::default() +} + +fn make_rule() -> IntersectsLogicalRule { + IntersectsLogicalRule::new(default_config()) +} + +fn write_intervals_parquet( + dir: &Path, + filename: &str, + chroms: &[&str], + starts: &[i64], + ends: &[i64], +) -> std::path::PathBuf { + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(StringArray::from(chroms.to_vec())), + Arc::new(Int64Array::from(starts.to_vec())), + Arc::new(Int64Array::from(ends.to_vec())), + ], + ) + .unwrap(); + + let path = dir.join(filename); + let file = std::fs::File::create(&path).unwrap(); + let mut writer = + ArrowWriter::try_new(file, schema, None).unwrap(); + writer.write(&batch).unwrap(); + writer.close().unwrap(); + path +} + +fn write_intervals_parquet_custom_schema( + dir: &Path, + filename: &str, + schema: Arc, + chroms: &[&str], + starts: &[i64], + ends: &[i64], +) -> std::path::PathBuf { + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(StringArray::from(chroms.to_vec())), + Arc::new(Int64Array::from(starts.to_vec())), + Arc::new(Int64Array::from(ends.to_vec())), + ], + ) + .unwrap(); + + let path = dir.join(filename); + let file = std::fs::File::create(&path).unwrap(); + let mut writer = + ArrowWriter::try_new(file, schema, None).unwrap(); + writer.write(&batch).unwrap(); + writer.close().unwrap(); + path +} + +/// Create a SessionContext with the logical rule enabled. +fn make_ctx_with_logical_rule() -> SessionContext { + let config = IntersectsOptimizerConfig { + enable_logical_rule: true, + ..default_config() + }; + let state = SessionStateBuilder::new() + .with_default_features() + .build(); + let state = register_optimizer(state, config); + SessionContext::from(state) +} + +/// Create a SessionContext with the logical rule disabled. +fn make_ctx_without_logical_rule() -> SessionContext { + let config = IntersectsOptimizerConfig { + enable_logical_rule: false, + ..default_config() + }; + let state = SessionStateBuilder::new() + .with_default_features() + .build(); + let state = register_optimizer(state, config); + SessionContext::from(state) +} + +const INTERSECTS_SQL: &str = "\ + SELECT a.chrom, a.start, a.\"end\", \ + b.chrom AS chrom_b, b.start AS start_b, b.\"end\" AS end_b \ + FROM a JOIN b \ + ON a.chrom = b.chrom \ + AND a.start < b.\"end\" \ + AND a.\"end\" > b.start"; + +// ── OptimizerRule trait tests ─────────────────────────────────── + +#[test] +fn test_rule_name() { + let rule = make_rule(); + assert_eq!(rule.name(), "intersects_logical_binned"); +} + +#[test] +fn test_rule_apply_order_is_bottom_up() { + let rule = make_rule(); + let order = rule.apply_order(); + assert!(order.is_some()); + assert!(matches!( + order.unwrap(), + datafusion::optimizer::ApplyOrder::BottomUp + )); +} + +#[test] +fn test_rule_supports_rewrite() { + let rule = make_rule(); + #[allow(deprecated)] + let supports = rule.supports_rewrite(); + assert!(supports); +} + +// ── Rewrite skipping tests ────────────────────────────────────── + +#[test] +fn test_rewrite_skips_non_join_plan() { + // Given a non-join logical plan (TableScan), + // When the rule is applied, + // Then the plan is returned unchanged. + let rule = make_rule(); + let config = datafusion::optimizer::OptimizerContext::new(); + + // Create a simple empty relation plan (not a join) + let plan = LogicalPlan::EmptyRelation( + datafusion::logical_expr::EmptyRelation { + produce_one_row: false, + schema: Arc::new( + datafusion::common::DFSchema::empty(), + ), + }, + ); + + let result = rule.rewrite(plan.clone(), &config).unwrap(); + assert!(!result.transformed); +} + +#[tokio::test] +async fn test_rewrite_skips_left_join() { + // Given a LEFT JOIN with interval overlap predicates, + // When the logical rule is applied, + // Then the plan is not rewritten (only INNER joins are supported). + let ctx = SessionContext::new(); + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(StringArray::from(vec!["chr1"])), + Arc::new(Int64Array::from(vec![100])), + Arc::new(Int64Array::from(vec![200])), + ], + ) + .unwrap(); + + let table = datafusion::datasource::MemTable::try_new( + schema.clone(), + vec![vec![batch.clone()]], + ) + .unwrap(); + let table2 = datafusion::datasource::MemTable::try_new( + schema, + vec![vec![batch]], + ) + .unwrap(); + ctx.register_table("a", Arc::new(table)).unwrap(); + ctx.register_table("b", Arc::new(table2)).unwrap(); + + let left_join_sql = "\ + SELECT a.chrom, a.start, a.\"end\", \ + b.chrom, b.start, b.\"end\" \ + FROM a LEFT JOIN b \ + ON a.chrom = b.chrom \ + AND a.start < b.\"end\" \ + AND a.\"end\" > b.start"; + + let df = ctx.sql(left_join_sql).await.unwrap(); + let plan = df.logical_plan().clone(); + + let rule = make_rule(); + let config = datafusion::optimizer::OptimizerContext::new(); + + // Walk the plan tree looking for join nodes + let result = rule.rewrite(plan, &config).unwrap(); + // Either the plan is not transformed (because it's not a Join + // at top level), or if DataFusion restructured it, the rule + // should still not rewrite non-inner joins. + // The important thing is that the rule doesn't panic. + let _ = result; +} + +#[tokio::test] +async fn test_rewrite_skips_right_join() { + // Given a RIGHT JOIN with interval overlap predicates, + // When the logical rule is applied, + // Then the plan is not rewritten (only INNER joins are supported). + let ctx = SessionContext::new(); + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(StringArray::from(vec!["chr1"])), + Arc::new(Int64Array::from(vec![100])), + Arc::new(Int64Array::from(vec![200])), + ], + ) + .unwrap(); + + let table = datafusion::datasource::MemTable::try_new( + schema.clone(), + vec![vec![batch.clone()]], + ) + .unwrap(); + let table2 = datafusion::datasource::MemTable::try_new( + schema, + vec![vec![batch]], + ) + .unwrap(); + ctx.register_table("a", Arc::new(table)).unwrap(); + ctx.register_table("b", Arc::new(table2)).unwrap(); + + let right_join_sql = "\ + SELECT a.chrom, a.start, a.\"end\", \ + b.chrom, b.start, b.\"end\" \ + FROM a RIGHT JOIN b \ + ON a.chrom = b.chrom \ + AND a.start < b.\"end\" \ + AND a.\"end\" > b.start"; + + let df = ctx.sql(right_join_sql).await.unwrap(); + let plan = df.logical_plan().clone(); + + let rule = make_rule(); + let config = datafusion::optimizer::OptimizerContext::new(); + + let result = rule.rewrite(plan, &config).unwrap(); + let _ = result; +} + +#[tokio::test] +async fn test_rewrite_skips_full_outer_join() { + // Given a FULL OUTER JOIN with interval overlap predicates, + // When the logical rule is applied, + // Then the plan is not rewritten. + let ctx = SessionContext::new(); + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(StringArray::from(vec!["chr1"])), + Arc::new(Int64Array::from(vec![100])), + Arc::new(Int64Array::from(vec![200])), + ], + ) + .unwrap(); + + let table = datafusion::datasource::MemTable::try_new( + schema.clone(), + vec![vec![batch.clone()]], + ) + .unwrap(); + let table2 = datafusion::datasource::MemTable::try_new( + schema, + vec![vec![batch]], + ) + .unwrap(); + ctx.register_table("a", Arc::new(table)).unwrap(); + ctx.register_table("b", Arc::new(table2)).unwrap(); + + let full_join_sql = "\ + SELECT a.chrom, a.start, a.\"end\", \ + b.chrom, b.start, b.\"end\" \ + FROM a FULL OUTER JOIN b \ + ON a.chrom = b.chrom \ + AND a.start < b.\"end\" \ + AND a.\"end\" > b.start"; + + let df = ctx.sql(full_join_sql).await.unwrap(); + let plan = df.logical_plan().clone(); + + let rule = make_rule(); + let config = datafusion::optimizer::OptimizerContext::new(); + + let result = rule.rewrite(plan, &config).unwrap(); + let _ = result; +} + +// ── Register optimizer with logical rule enabled/disabled ─────── + +#[test] +fn test_register_optimizer_with_logical_rule_enabled() { + // Given a default config with enable_logical_rule = true, + // When register_optimizer is called, + // Then both logical and physical rules are added. + let state = SessionStateBuilder::new() + .with_default_features() + .build(); + let n_logical_before = state.optimizers().len(); + let n_physical_before = state.physical_optimizers().len(); + + let config = IntersectsOptimizerConfig { + enable_logical_rule: true, + ..default_config() + }; + let state = register_optimizer(state, config); + + assert_eq!( + state.optimizers().len(), + n_logical_before + 1, + "Should add one logical rule" + ); + assert_eq!( + state.physical_optimizers().len(), + n_physical_before + 1, + "Should add one physical rule" + ); + + let last_logical = state.optimizers().last().unwrap(); + assert_eq!(last_logical.name(), "intersects_logical_binned"); +} + +#[test] +fn test_register_optimizer_with_logical_rule_disabled() { + // Given a config with enable_logical_rule = false, + // When register_optimizer is called, + // Then only the physical rule is added, not the logical rule. + let state = SessionStateBuilder::new() + .with_default_features() + .build(); + let n_logical_before = state.optimizers().len(); + let n_physical_before = state.physical_optimizers().len(); + + let config = IntersectsOptimizerConfig { + enable_logical_rule: false, + ..default_config() + }; + let state = register_optimizer(state, config); + + assert_eq!( + state.optimizers().len(), + n_logical_before, + "Should NOT add a logical rule" + ); + assert_eq!( + state.physical_optimizers().len(), + n_physical_before + 1, + "Should still add the physical rule" + ); +} + +// ── Adaptive bin sizing integration tests ─────────────────────── + +#[tokio::test] +async fn test_logical_rule_produces_correct_results_simple() { + // Given two tables with known overlapping intervals and the + // logical rule enabled, + // When an INTERSECTS join is executed, + // Then the correct number of overlap pairs is returned. + let dir = TempDir::new().unwrap(); + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1", "chr1"], + &[100, 300, 600], + &[250, 500, 800], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1"], + &[200, 700], + &[400, 900], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // Expected overlaps: + // a[100,250) x b[200,400) -> yes + // a[300,500) x b[200,400) -> yes + // a[600,800) x b[700,900) -> yes + assert_eq!(total_rows, 3); +} + +#[tokio::test] +async fn test_logical_rule_no_false_positives_adjacent() { + // Given adjacent intervals [100,200) and [200,300) with the + // logical rule enabled, + // When an INTERSECTS join is executed, + // Then no overlap pairs are returned (half-open semantics). + let dir = TempDir::new().unwrap(); + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1"], + &[100], + &[200], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1"], + &[200], + &[300], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 0); +} + +#[tokio::test] +async fn test_logical_rule_containment() { + // Given an interval [100,500) that fully contains [200,300) + // with the logical rule enabled, + // When an INTERSECTS join is executed, + // Then exactly one overlap pair is returned. + let dir = TempDir::new().unwrap(); + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1"], + &[100], + &[500], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1"], + &[200], + &[300], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 1); +} + +#[tokio::test] +async fn test_logical_rule_different_chroms_no_overlap() { + // Given intervals on different chromosomes with the logical + // rule enabled, + // When an INTERSECTS join is executed, + // Then no pairs are returned even though the coordinates overlap. + let dir = TempDir::new().unwrap(); + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1"], + &[100, 300], + &[500, 600], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr2", "chr2"], + &[100, 300], + &[500, 600], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 0); +} + +// ── Canonical-bin dedup correctness ───────────────────────────── + +#[tokio::test] +async fn test_no_duplicate_pairs_wide_intervals() { + // Given wide intervals that span multiple bins, + // When the logical rule rewrites to a binned join, + // Then each overlapping pair appears exactly once (dedup filter + // eliminates multi-bin duplicates). + let dir = TempDir::new().unwrap(); + + // Wide intervals spanning many bins (default bin ~10k) + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1"], + &[0, 50000], + &[40000, 90000], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1"], + &[10000, 60000], + &[30000, 80000], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // a[0,40000) overlaps b[10000,30000) -> yes + // a[0,40000) overlaps b[60000,80000) -> no (40000 < 60000) + // a[50000,90000) overlaps b[10000,30000) -> no (50000 >= 30000) + // a[50000,90000) overlaps b[60000,80000) -> yes + assert_eq!(total_rows, 2); +} + +#[tokio::test] +async fn test_no_duplicate_pairs_many_bins() { + // Given an interval that spans many bins and overlaps with + // multiple other intervals, + // When the logical rule rewrites to a binned join, + // Then each pair appears exactly once regardless of how many + // bins they share. + let dir = TempDir::new().unwrap(); + + // One very wide interval on each side, plus a narrow one + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1"], + &[0], + &[100000], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1", "chr1"], + &[5000, 50000, 200000], + &[15000, 70000, 300000], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // a[0,100000) overlaps b[5000,15000) -> yes + // a[0,100000) overlaps b[50000,70000) -> yes + // a[0,100000) overlaps b[200000,300000) -> no (100000 <= 200000) + assert_eq!(total_rows, 2); +} + +// ── Adaptive bin sizing ───────────────────────────────────────── + +#[tokio::test] +async fn test_narrow_intervals_produce_small_bin_size() { + // Given tables with narrow intervals (width ~100bp), + // When the logical rule processes them, + // Then the bin size should be small (clamped to minimum 1000) + // and the result should still be correct. + let dir = TempDir::new().unwrap(); + + // 100 narrow intervals of width 100 + let chroms: Vec<&str> = vec!["chr1"; 100]; + let starts: Vec = (0..100).map(|i| i * 200).collect(); + let ends: Vec = starts.iter().map(|s| s + 100).collect(); + + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &chroms, + &starts, + &ends, + ); + // Overlapping intervals offset by 50 + let starts_b: Vec = (0..100).map(|i| i * 200 + 50).collect(); + let ends_b: Vec = starts_b.iter().map(|s| s + 100).collect(); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &chroms, + &starts_b, + &ends_b, + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // Each a interval [i*200, i*200+100) overlaps b[i*200+50, i*200+150) + // Plus a[i*200, i*200+100) may also overlap b[(i-1)*200+50, (i-1)*200+150) + // when i*200 < (i-1)*200+150, i.e., 200 < 150 -> never. + // So exactly 100 pairs. + assert_eq!(total_rows, 100); +} + +#[tokio::test] +async fn test_wide_intervals_produce_large_bin_size() { + // Given tables with wide intervals (width ~50000bp), + // When the logical rule processes them, + // Then the result should still be correct with the adaptively + // chosen larger bin size. + let dir = TempDir::new().unwrap(); + + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1"], + &[0, 100000], + &[50000, 150000], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1"], + &[25000, 125000], + &[75000, 175000], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // a[0,50000) overlaps b[25000,75000) -> yes + // a[0,50000) overlaps b[125000,175000) -> no + // a[100000,150000) overlaps b[25000,75000) -> no + // a[100000,150000) overlaps b[125000,175000) -> yes + assert_eq!(total_rows, 2); +} + +// ── Multi-chromosome tests ────────────────────────────────────── + +#[tokio::test] +async fn test_multi_chromosome_intersects() { + // Given intervals on multiple chromosomes, + // When the logical rule processes the join, + // Then overlaps are correctly identified per-chromosome only. + let dir = TempDir::new().unwrap(); + + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr2", "chr3"], + &[100, 100, 100], + &[500, 500, 500], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr2", "chr4"], + &[200, 200, 200], + &[400, 400, 400], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // chr1: a[100,500) x b[200,400) -> yes + // chr2: a[100,500) x b[200,400) -> yes + // chr3 vs chr4: no match + assert_eq!(total_rows, 2); +} + +// ── Many-to-many overlap (correctness stress test) ────────────── + +#[tokio::test] +async fn test_many_to_many_overlap() { + // Given overlapping intervals where each interval overlaps + // multiple intervals on the other side, + // When the logical rule processes the join, + // Then all valid pairs are returned exactly once. + let dir = TempDir::new().unwrap(); + + // Three intervals each spanning [0,300), [100,400), [200,500) + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1", "chr1"], + &[0, 100, 200], + &[300, 400, 500], + ); + // Three intervals each spanning [150,350), [250,450), [350,550) + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1", "chr1"], + &[150, 250, 350], + &[350, 450, 550], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // a[0,300) vs b[150,350) -> yes (0<350, 300>150) + // a[0,300) vs b[250,450) -> yes (0<450, 300>250) + // a[0,300) vs b[350,550) -> no (300 <= 350) + // a[100,400) vs b[150,350) -> yes + // a[100,400) vs b[250,450) -> yes + // a[100,400) vs b[350,550) -> yes (100<550, 400>350) + // a[200,500) vs b[150,350) -> yes (200<350, 500>150) + // a[200,500) vs b[250,450) -> yes + // a[200,500) vs b[350,550) -> yes (200<550, 500>350) + assert_eq!(total_rows, 8); +} + +// ── Logical rule vs no logical rule consistency ───────────────── + +#[tokio::test] +async fn test_logical_rule_matches_baseline_results() { + // Given the same data, + // When an INTERSECTS join is executed with and without the + // logical rule, + // Then both produce the same number of results. + let dir = TempDir::new().unwrap(); + + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1", "chr2", "chr2"], + &[100, 500, 200, 800], + &[400, 900, 600, 1200], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1", "chr2"], + &[300, 700, 400], + &[600, 1000, 700], + ); + + // With logical rule + let ctx_with = make_ctx_with_logical_rule(); + ctx_with + .register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx_with + .register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result_with = ctx_with.sql(INTERSECTS_SQL).await.unwrap(); + let batches_with = result_with.collect().await.unwrap(); + let rows_with: usize = + batches_with.iter().map(|b| b.num_rows()).sum(); + + // Without logical rule + let ctx_without = make_ctx_without_logical_rule(); + ctx_without + .register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx_without + .register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result_without = ctx_without.sql(INTERSECTS_SQL).await.unwrap(); + let batches_without = result_without.collect().await.unwrap(); + let rows_without: usize = + batches_without.iter().map(|b| b.num_rows()).sum(); + + assert_eq!( + rows_with, rows_without, + "Logical rule should produce same count as baseline" + ); + + // Also verify the expected count: + // chr1: a[100,400) x b[300,600) -> yes + // a[100,400) x b[700,1000) -> no + // a[500,900) x b[300,600) -> yes + // a[500,900) x b[700,1000) -> yes + // chr2: a[200,600) x b[400,700) -> yes + // a[800,1200) x b[400,700) -> no + assert_eq!(rows_with, 4); +} + +// ── Empty tables ──────────────────────────────────────────────── + +#[tokio::test] +async fn test_logical_rule_empty_right_table() { + // Given a non-empty left table and an empty right table, + // When the logical rule processes an INTERSECTS join, + // Then zero rows are returned. + let dir = TempDir::new().unwrap(); + + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1"], + &[100, 300], + &[200, 400], + ); + + // Empty table - at least one row needed for Parquet writing, + // so we'll use the memory table approach + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let empty_batch = RecordBatch::new_empty(schema.clone()); + let empty_table = datafusion::datasource::MemTable::try_new( + schema, + vec![vec![empty_batch]], + ) + .unwrap(); + ctx.register_table("b", Arc::new(empty_table)).unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 0); +} + +// ── Single-row tables ─────────────────────────────────────────── + +#[tokio::test] +async fn test_logical_rule_single_row_overlap() { + // Given two single-row tables with overlapping intervals, + // When the logical rule processes the join, + // Then exactly one pair is returned. + let dir = TempDir::new().unwrap(); + + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1"], + &[100], + &[300], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1"], + &[200], + &[400], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 1); +} + +#[tokio::test] +async fn test_logical_rule_single_row_no_overlap() { + // Given two single-row tables with non-overlapping intervals, + // When the logical rule processes the join, + // Then zero pairs are returned. + let dir = TempDir::new().unwrap(); + + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1"], + &[100], + &[200], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1"], + &[300], + &[400], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 0); +} + +// ── Identical intervals ───────────────────────────────────────── + +#[tokio::test] +async fn test_logical_rule_identical_intervals() { + // Given two tables with identical intervals, + // When the logical rule processes the join, + // Then all N*M pairs are returned where both are on the same + // chrom. + let dir = TempDir::new().unwrap(); + + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1"], + &[100, 100], + &[200, 200], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1"], + &[100, 100], + &[200, 200], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // 2 left x 2 right = 4 pairs, all overlap + assert_eq!(total_rows, 4); +} + +// ── Boundary conditions ───────────────────────────────────────── + +#[tokio::test] +async fn test_logical_rule_one_bp_overlap() { + // Given intervals that overlap by exactly 1bp, + // When the logical rule processes the join, + // Then the pair is returned. + let dir = TempDir::new().unwrap(); + + // a[100,201) and b[200,300) overlap at position 200 + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1"], + &[100], + &[201], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1"], + &[200], + &[300], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + assert_eq!(total_rows, 1); +} + +// ── Column name variants (chromStart/chromEnd) ────────────────── + +#[tokio::test] +async fn test_logical_rule_chromstart_chromend_columns() { + // Given tables with BED-style column names (chromStart, chromEnd), + // When an INTERSECTS join is executed with the logical rule, + // Then the column names are recognized and overlaps are found. + let dir = TempDir::new().unwrap(); + + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("chromStart", DataType::Int64, false), + Field::new("chromEnd", DataType::Int64, false), + ])); + + let path_a = write_intervals_parquet_custom_schema( + dir.path(), + "a.parquet", + schema.clone(), + &["chr1", "chr1"], + &[100, 500], + &[300, 700], + ); + let path_b = write_intervals_parquet_custom_schema( + dir.path(), + "b.parquet", + schema, + &["chr1"], + &[200], + &[600], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let sql = "\ + SELECT a.chrom, a.\"chromStart\", a.\"chromEnd\", \ + b.chrom AS chrom_b, b.\"chromStart\" AS start_b, \ + b.\"chromEnd\" AS end_b \ + FROM a JOIN b \ + ON a.chrom = b.chrom \ + AND a.\"chromStart\" < b.\"chromEnd\" \ + AND a.\"chromEnd\" > b.\"chromStart\""; + + let result = ctx.sql(sql).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // a[100,300) x b[200,600) -> yes + // a[500,700) x b[200,600) -> yes (500 < 600, 700 > 200) + assert_eq!(total_rows, 2); +} + +// ── Verify values in output ───────────────────────────────────── + +#[tokio::test] +async fn test_logical_rule_output_values_correct() { + // Given known overlapping intervals, + // When the logical rule processes the join, + // Then the output columns contain the correct start/end values. + let dir = TempDir::new().unwrap(); + + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1"], + &[100], + &[300], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1"], + &[200], + &[400], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) + .await + .unwrap(); + ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + assert_eq!(batches.len(), 1); + + let batch = &batches[0]; + assert_eq!(batch.num_rows(), 1); + + // Check the values: should have a.start=100, a.end=300, + // b.start=200, b.end=400 + let a_start = batch + .column_by_name("start") + .unwrap() + .as_any() + .downcast_ref::() + .unwrap(); + assert_eq!(a_start.value(0), 100); + + let a_end = batch + .column_by_name("end") + .unwrap() + .as_any() + .downcast_ref::() + .unwrap(); + assert_eq!(a_end.value(0), 300); + + let b_start = batch + .column_by_name("start_b") + .unwrap() + .as_any() + .downcast_ref::() + .unwrap(); + assert_eq!(b_start.value(0), 200); + + let b_end = batch + .column_by_name("end_b") + .unwrap() + .as_any() + .downcast_ref::() + .unwrap(); + assert_eq!(b_end.value(0), 400); +} From e6702a5600c5b86da62123ae3d3d1082993cf71d Mon Sep 17 00:00:00 2001 From: Conrad Date: Fri, 27 Mar 2026 11:32:41 -0400 Subject: [PATCH 20/28] =?UTF-8?q?fix:=20Address=20PR=20review=20=E2=80=94?= =?UTF-8?q?=20schema-based=20join=20side=20detection,=20remove=20debug=20o?= =?UTF-8?q?utput?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Blocking fixes from code review: 1. Replace the fragile is_from_left alphabetical heuristic with schema-based column resolution. The function now checks the column's qualified name against the join's left child schema via DFSchema::has_column(), correctly handling any table alias. 2. Replace all eprintln! debug statements with log::debug! across logical_rule.rs, optimizer.rs, and pattern.rs (17 instances). Also fixes: - Doc comment for enable_logical_rule now matches the true default - Remove unnecessary unsafe Pin::new_unchecked in sweep_line.rs - Add test for non-a/l table aliases (peaks/genes) to verify fix --- crates/giql-datafusion/src/exec/sweep_line.rs | 2 +- crates/giql-datafusion/src/lib.rs | 8 +- crates/giql-datafusion/src/logical_rule.rs | 86 +++++++------------ crates/giql-datafusion/src/optimizer.rs | 8 +- crates/giql-datafusion/src/pattern.rs | 2 +- .../tests/logical_rule_test.rs | 39 +++++++++ 6 files changed, 79 insertions(+), 66 deletions(-) diff --git a/crates/giql-datafusion/src/exec/sweep_line.rs b/crates/giql-datafusion/src/exec/sweep_line.rs index dc2d9c8..18b6d06 100644 --- a/crates/giql-datafusion/src/exec/sweep_line.rs +++ b/crates/giql-datafusion/src/exec/sweep_line.rs @@ -309,7 +309,7 @@ impl futures::Stream for SweepLineStream { let fut = self.build_fut.as_mut().unwrap(); // SAFETY: we only poll this once, and it's // behind an Option that we take after Ready. - let fut = unsafe { Pin::new_unchecked(fut) }; + let fut = Pin::new(fut); match fut.poll(cx) { Poll::Pending => return Poll::Pending, Poll::Ready(join_result) => { diff --git a/crates/giql-datafusion/src/lib.rs b/crates/giql-datafusion/src/lib.rs index ebf4118..054ffc4 100644 --- a/crates/giql-datafusion/src/lib.rs +++ b/crates/giql-datafusion/src/lib.rs @@ -50,10 +50,10 @@ pub struct IntersectsOptimizerConfig { /// Maximum number of row groups to sample for width distribution. pub max_sample_row_groups: usize, - /// Enable the experimental logical optimizer rule that rewrites - /// interval overlap joins to UNNEST-based binned equi-joins. - /// When false (default), only the physical sweep-line optimizer - /// is active. + /// Enable the logical optimizer rule that rewrites interval + /// overlap joins to UNNEST-based binned equi-joins. When false, + /// only the physical sweep-line optimizer is active. Enabled by + /// default. pub enable_logical_rule: bool, } diff --git a/crates/giql-datafusion/src/logical_rule.rs b/crates/giql-datafusion/src/logical_rule.rs index 3fb9edf..19062d5 100644 --- a/crates/giql-datafusion/src/logical_rule.rs +++ b/crates/giql-datafusion/src/logical_rule.rs @@ -77,21 +77,15 @@ impl OptimizerRule for IntersectsLogicalRule { } // Detect interval overlap pattern in the filter + let left_schema = join.left.schema(); let overlap = match &join.filter { Some(filter) => { - eprintln!( - "INTERSECTS logical: checking filter: {filter}" - ); - detect_overlap_columns(filter) - } - None => { - eprintln!("INTERSECTS logical: join has no filter"); - None + detect_overlap_columns(filter, &left_schema) } + None => None, }; let Some((start_a, end_a, start_b, end_b)) = overlap else { - eprintln!("INTERSECTS logical: no overlap pattern found"); return Ok(Transformed::no(plan)); }; @@ -106,7 +100,7 @@ impl OptimizerRule for IntersectsLogicalRule { let bin_size = choose_bin_size(&left_stats, &right_stats); - eprintln!( + log::debug!( "INTERSECTS logical rule: rewriting to binned join, \ bin_size={bin_size}" ); @@ -126,8 +120,13 @@ impl OptimizerRule for IntersectsLogicalRule { /// - The other has "end" on one side and "start" on the other /// /// Returns `(start_a, end_a, start_b, end_b)` column names. +/// Detect interval overlap predicates in a filter expression. +/// +/// Checks the join's left child schema to determine which columns +/// belong to which side — no heuristics based on table name. fn detect_overlap_columns( expr: &Expr, + left_schema: &datafusion::common::DFSchemaRef, ) -> Option<(Column, Column, Column, Column)> { let Expr::BinaryExpr(BinaryExpr { left, @@ -138,31 +137,26 @@ fn detect_overlap_columns( return None; }; - // Try both orderings - try_extract_overlap(left, right) - .or_else(|| try_extract_overlap(right, left)) + try_extract_overlap(left, right, left_schema) + .or_else(|| try_extract_overlap(right, left, left_schema)) } fn try_extract_overlap( pred_a: &Expr, pred_b: &Expr, + left_schema: &datafusion::common::DFSchemaRef, ) -> Option<(Column, Column, Column, Column)> { let (lt_left, lt_right) = extract_comparison(pred_a, Operator::Lt)?; let (gt_left, gt_right) = extract_comparison(pred_b, Operator::Gt)?; let all = [<_left, <_right, >_left, >_right]; - eprintln!("INTERSECTS logical: columns in filter:"); - for c in &all { - eprintln!(" {:?} start={} end={} left={}", - c, is_start(&c.name), is_end(&c.name), is_from_left(c)); - } - let left_start = all.iter().find(|c| is_start(&c.name) && is_from_left(c)); - let left_end = all.iter().find(|c| is_end(&c.name) && is_from_left(c)); - let right_start = all.iter().find(|c| is_start(&c.name) && !is_from_left(c)); - let right_end = all.iter().find(|c| is_end(&c.name) && !is_from_left(c)); + let is_left = |c: &Column| column_in_schema(c, left_schema); - eprintln!(" left_start={left_start:?} left_end={left_end:?} right_start={right_start:?} right_end={right_end:?}"); + let left_start = all.iter().find(|c| is_start(&c.name) && is_left(c)); + let left_end = all.iter().find(|c| is_end(&c.name) && is_left(c)); + let right_start = all.iter().find(|c| is_start(&c.name) && !is_left(c)); + let right_end = all.iter().find(|c| is_end(&c.name) && !is_left(c)); Some(( (*left_start?).clone(), @@ -177,11 +171,9 @@ fn extract_comparison( expected_op: Operator, ) -> Option<(Column, Column)> { let Expr::BinaryExpr(BinaryExpr { left, op, right }) = expr else { - eprintln!(" extract_comparison: not a BinaryExpr: {expr:?}"); return None; }; if *op != expected_op { - eprintln!(" extract_comparison: op={op:?}, expected={expected_op:?}"); return None; } let left_col = extract_column(left)?; @@ -196,10 +188,7 @@ fn extract_column(expr: &Expr) -> Option { Expr::Column(c) => Some(c.clone()), Expr::Cast(cast) => extract_column(&cast.expr), Expr::TryCast(tc) => extract_column(&tc.expr), - other => { - eprintln!(" extract_column: unexpected expr type: {other:?}"); - None - } + _ => None, } } @@ -213,28 +202,13 @@ fn is_end(name: &str) -> bool { n == "end" || n == "chromend" || n == "pos_end" || n == "stop" } -fn is_from_left(col: &Column) -> bool { - // In DataFusion logical plans, qualified columns have a table - // relation. We use position in the join: left-side columns - // have the left table qualifier. Since we don't know the exact - // qualifier, we rely on the join's on-clause to tell us which - // table is which. For now, use a simple heuristic: both sides - // have the same column names, so the relation qualifier - // distinguishes them. If no qualifier, we can't tell. - // This works because genomic tables always have qualified refs - // in JOIN conditions (e.g., a.start, b.start). - col.relation.is_some() - && col - .relation - .as_ref() - .map(|r| { - let s = r.to_string(); - // First table alphabetically is "left" — fragile but - // works for a.X / b.X patterns. We'll improve this - // by checking against the join's child schemas. - s.starts_with('a') || s.starts_with('l') - }) - .unwrap_or(false) +/// Check whether a column belongs to a schema by matching its +/// qualified name against the schema's columns. +fn column_in_schema( + col: &Column, + schema: &datafusion::common::DFSchemaRef, +) -> bool { + schema.has_column(col) } // ── Stats collection ──────────────────────────────────────────── @@ -253,7 +227,7 @@ fn get_table_stats(plan: &LogicalPlan) -> Option { let provider = match source_as_provider(&ts.source) { Ok(p) => p, Err(e) => { - eprintln!( + log::debug!( " get_table_stats: source_as_provider failed: {e}" ); return None; @@ -272,7 +246,7 @@ fn get_table_stats(plan: &LogicalPlan) -> Option { { Some(lt) => lt, None => { - eprintln!( + log::debug!( " get_table_stats: not a ListingTable: {}", std::any::type_name_of_val(provider.as_ref()), ); @@ -414,7 +388,7 @@ fn choose_bin_size( (Some(l), Some(r)) => { let w = l.max(r) as usize; let bin_size = w.clamp(1_000, 1_000_000); - eprintln!( + log::debug!( "INTERSECTS logical: adaptive bin_size={bin_size} \ (from widths l={l}, r={r})" ); @@ -422,14 +396,14 @@ fn choose_bin_size( } (Some(w), None) | (None, Some(w)) => { let bin_size = (w as usize).clamp(1_000, 1_000_000); - eprintln!( + log::debug!( "INTERSECTS logical: adaptive bin_size={bin_size} \ (partial stats, width={w})" ); bin_size } (None, None) => { - eprintln!( + log::debug!( "INTERSECTS logical: using default bin_size={DEFAULT_BIN_SIZE}" ); DEFAULT_BIN_SIZE diff --git a/crates/giql-datafusion/src/optimizer.rs b/crates/giql-datafusion/src/optimizer.rs index 73a7c60..58a589e 100644 --- a/crates/giql-datafusion/src/optimizer.rs +++ b/crates/giql-datafusion/src/optimizer.rs @@ -41,7 +41,7 @@ impl IntersectsOptimizerRule { &self, plan: Arc, ) -> Result> { - eprintln!( + log::debug!( "INTERSECTS optimizer: visiting node: {}", plan.name() ); @@ -83,7 +83,7 @@ impl IntersectsOptimizerRule { let strategy = match (&left_stats, &right_stats) { (Some(left), Some(right)) => cost_model.decide(left, right), _ => { - eprintln!( + log::debug!( "INTERSECTS optimizer: no Parquet stats available, \ deferring to DataFusion" ); @@ -91,7 +91,7 @@ impl IntersectsOptimizerRule { } }; - eprintln!("INTERSECTS optimizer: selected {strategy:?}"); + log::debug!("INTERSECTS optimizer: selected {strategy:?}"); match strategy { JoinStrategy::SweepLine { build_side } => { @@ -115,7 +115,7 @@ impl IntersectsOptimizerRule { // (BinExpandExec + modified HashJoinExec) exceeds // the gain from bin-based hashing. Defer to // DataFusion's built-in join. - eprintln!( + log::debug!( "INTERSECTS optimizer: binned strategy selected, \ deferring to DataFusion" ); diff --git a/crates/giql-datafusion/src/pattern.rs b/crates/giql-datafusion/src/pattern.rs index 8d1e065..a239b39 100644 --- a/crates/giql-datafusion/src/pattern.rs +++ b/crates/giql-datafusion/src/pattern.rs @@ -149,7 +149,7 @@ fn detect_from_hash_join( ) { Some(cols) => cols, None => { - eprintln!( + log::debug!( "INTERSECTS optimizer: HashJoinExec filter didn't \ match. filter={:?}, indices={:?}", filter_expr, column_indices, diff --git a/crates/giql-datafusion/tests/logical_rule_test.rs b/crates/giql-datafusion/tests/logical_rule_test.rs index cb1378b..bf73902 100644 --- a/crates/giql-datafusion/tests/logical_rule_test.rs +++ b/crates/giql-datafusion/tests/logical_rule_test.rs @@ -1253,3 +1253,42 @@ async fn test_logical_rule_output_values_correct() { .unwrap(); assert_eq!(b_end.value(0), 400); } + +/// Tables aliased as "peaks" and "genes" — not starting with 'a' or 'l'. +/// This previously broke with the alphabetical is_from_left heuristic. +#[tokio::test] +async fn test_logical_rule_non_al_table_aliases() { + let dir = TempDir::new().unwrap(); + let left_path = write_intervals_parquet( + dir.path(), "peaks.parquet", + &["chr1", "chr1"], &[100, 300], &[250, 500], + ); + let right_path = write_intervals_parquet( + dir.path(), "genes.parquet", + &["chr1", "chr1"], &[200, 400], &[350, 600], + ); + + let ctx = make_ctx_with_logical_rule(); + ctx.register_parquet("peaks", left_path.to_str().unwrap(), Default::default()) + .await.unwrap(); + ctx.register_parquet("genes", right_path.to_str().unwrap(), Default::default()) + .await.unwrap(); + + let sql = r#" + SELECT peaks.chrom, peaks.start, peaks."end", + genes.chrom AS chrom_b, genes.start AS start_b, genes."end" AS end_b + FROM peaks JOIN genes + ON peaks.chrom = genes.chrom + AND peaks.start < genes."end" + AND peaks."end" > genes.start + "#; + + let result = ctx.sql(sql).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = batches.iter().map(|b| b.num_rows()).sum(); + + // [100,250) overlaps [200,350): yes + // [300,500) overlaps [200,350): yes + // [300,500) overlaps [400,600): yes + assert_eq!(total_rows, 3); +} From 4e9965c3cc130d54970c465c0e45bb2a64b0199d Mon Sep 17 00:00:00 2001 From: Conrad Date: Fri, 27 Mar 2026 15:54:37 -0400 Subject: [PATCH 21/28] feat: Add datafusion dialect to GIQL transpiler The transpile() function now accepts dialect="datafusion" which emits giql_intersects(start, end, start, end) function calls for column-to-column INTERSECTS joins instead of expanding to raw overlap predicates. This preserves INTERSECTS semantics through the SQL layer so a DataFusion logical optimizer rule can match on the function call directly, without heuristic column-name pattern detection. Literal range queries and CONTAINS/WITHIN fall through to the base generator unchanged. --- src/giql/generators/__init__.py | 3 ++- src/giql/generators/datafusion.py | 35 ++++++++++++++++++++++++++ src/giql/transpile.py | 42 ++++++++++++++++++++----------- 3 files changed, 65 insertions(+), 15 deletions(-) create mode 100644 src/giql/generators/datafusion.py diff --git a/src/giql/generators/__init__.py b/src/giql/generators/__init__.py index ca8cb16..226851c 100644 --- a/src/giql/generators/__init__.py +++ b/src/giql/generators/__init__.py @@ -1,5 +1,6 @@ """SQL generators for GIQL transpilation.""" from giql.generators.base import BaseGIQLGenerator +from giql.generators.datafusion import DataFusionGIQLGenerator -__all__ = ["BaseGIQLGenerator"] +__all__ = ["BaseGIQLGenerator", "DataFusionGIQLGenerator"] diff --git a/src/giql/generators/datafusion.py b/src/giql/generators/datafusion.py new file mode 100644 index 0000000..3af6a1f --- /dev/null +++ b/src/giql/generators/datafusion.py @@ -0,0 +1,35 @@ +"""DataFusion SQL generator for GIQL transpilation. + +Emits ``giql_intersects()`` function calls for column-to-column +INTERSECTS joins instead of expanding to raw overlap predicates. +A DataFusion logical optimizer rule matches on that function call +and rewrites it to a binned equi-join with adaptive bin sizing. +""" + +from __future__ import annotations + +from giql.generators.base import BaseGIQLGenerator + + +class DataFusionGIQLGenerator(BaseGIQLGenerator): + """Generator that preserves INTERSECTS semantics for DataFusion. + + For column-to-column INTERSECTS joins, emits:: + + (l.chrom = r.chrom AND giql_intersects(l.start, l.end, r.start, r.end)) + + instead of the standard overlap predicates. The chrom equi-key is + preserved as plain SQL so DataFusion can use it for hash + partitioning. All other operations (literal range queries, + CONTAINS, WITHIN) fall through to the base generator. + """ + + def _generate_column_join(self, left_col: str, right_col: str, op_type: str) -> str: + if op_type == "intersects": + l_chrom, l_start, l_end = self._get_column_refs(left_col, None) + r_chrom, r_start, r_end = self._get_column_refs(right_col, None) + return ( + f"({l_chrom} = {r_chrom} " + f"AND giql_intersects({l_start}, {l_end}, {r_start}, {r_end}))" + ) + return super()._generate_column_join(left_col, right_col, op_type) diff --git a/src/giql/transpile.py b/src/giql/transpile.py index 2b29c3d..d140aa1 100644 --- a/src/giql/transpile.py +++ b/src/giql/transpile.py @@ -4,6 +4,10 @@ to standard SQL. """ +from __future__ import annotations + +from typing import Literal + from sqlglot import parse_one from giql.dialect import GIQLDialect @@ -45,6 +49,7 @@ def _build_tables(tables: list[str | Table] | None) -> Tables: def transpile( giql: str, tables: list[str | Table] | None = None, + dialect: Literal["default", "datafusion"] = "default", ) -> str: """Transpile a GIQL query to SQL. @@ -60,6 +65,12 @@ def transpile( Table configurations. Strings use default column mappings (chrom, start, end, strand). Table objects provide custom column name mappings. + dialect : {"default", "datafusion"} + Target SQL dialect. ``"datafusion"`` emits + ``giql_intersects()`` function calls for column-to-column + INTERSECTS joins, allowing a DataFusion logical optimizer + rule to rewrite them into binned equi-joins with adaptive + bin sizing. Default emits standard SQL-92 overlap predicates. Returns ------- @@ -69,7 +80,8 @@ def transpile( Raises ------ ValueError - If the query cannot be parsed or transpiled. + If the query cannot be parsed or transpiled, or if an + unsupported dialect is specified. Examples -------- @@ -80,19 +92,12 @@ def transpile( tables=["peaks"], ) - Custom table configuration:: + DataFusion dialect for optimized interval joins:: sql = transpile( - "SELECT * FROM peaks WHERE interval INTERSECTS 'chr1:1000-2000'", - tables=[ - Table( - "peaks", - genomic_col="interval", - chrom_col="chrom", - start_col="start", - end_col="end", - ) - ], + "SELECT * FROM a JOIN b ON a.interval INTERSECTS b.interval", + tables=["a", "b"], + dialect="datafusion", ) """ # Build tables container @@ -102,8 +107,17 @@ def transpile( merge_transformer = MergeTransformer(tables_container) cluster_transformer = ClusterTransformer(tables_container) - # Initialize generator with table configurations - generator = BaseGIQLGenerator(tables=tables_container) + # Initialize generator for the target dialect + if dialect == "datafusion": + from giql.generators.datafusion import DataFusionGIQLGenerator + + generator = DataFusionGIQLGenerator(tables=tables_container) + elif dialect == "default": + generator = BaseGIQLGenerator(tables=tables_container) + else: + raise ValueError( + f"Unknown dialect: {dialect!r}. Supported: 'default', 'datafusion'" + ) # Parse GIQL query try: From 3418e0f846a2c655bb97436adb2b5f2debfbe0d9 Mon Sep 17 00:00:00 2001 From: Conrad Date: Fri, 27 Mar 2026 15:54:58 -0400 Subject: [PATCH 22/28] refactor!: Replace heuristic overlap detection with giql_intersects UDF The logical optimizer rule now matches on giql_intersects() function calls emitted by the GIQL transpiler's datafusion dialect, instead of reverse-engineering overlap predicates from column name heuristics. A placeholder giql_intersects ScalarUDF is registered so DataFusion's SQL parser accepts the function call. The logical rule rewrites it to a binned equi-join with adaptive bin sizing, replacing the function call with real overlap predicates before execution. This eliminates the physical optimizer, cost model, sweep line executor, sampling infrastructure, and heuristic pattern matching (15 files, ~4,000 lines removed). The binned join approach with adaptive bin sizing from table statistics is sufficient for all interval width distributions. BREAKING CHANGE: register_optimizer() no longer accepts IntersectsOptimizerConfig. The IntersectsOptimizerConfig struct, JoinStrategy enum, and physical optimizer rule are removed. --- crates/giql-datafusion/Cargo.lock | 1 - crates/giql-datafusion/Cargo.toml | 5 +- .../src/bin/bench_intersects.rs | 165 ----- crates/giql-datafusion/src/cost.rs | 309 --------- crates/giql-datafusion/src/exec/bin_expand.rs | 249 ------- .../giql-datafusion/src/exec/binned_join.rs | 377 ----------- crates/giql-datafusion/src/exec/binned_sql.rs | 208 ------ crates/giql-datafusion/src/exec/mod.rs | 9 - crates/giql-datafusion/src/exec/sweep_line.rs | 628 ------------------ crates/giql-datafusion/src/lib.rs | 241 +++---- crates/giql-datafusion/src/logical_rule.rs | 446 +++++-------- crates/giql-datafusion/src/optimizer.rs | 179 ----- crates/giql-datafusion/src/pattern.rs | 410 ------------ crates/giql-datafusion/src/pruning.rs | 165 ----- crates/giql-datafusion/src/stats/metadata.rs | 273 -------- crates/giql-datafusion/src/stats/mod.rs | 150 ----- crates/giql-datafusion/src/stats/sampler.rs | 304 --------- crates/giql-datafusion/src/stats/types.rs | 143 ---- .../giql-datafusion/tests/integration_test.rs | 269 -------- .../tests/logical_rule_test.rs | 398 ++++------- 20 files changed, 430 insertions(+), 4499 deletions(-) delete mode 100644 crates/giql-datafusion/src/bin/bench_intersects.rs delete mode 100644 crates/giql-datafusion/src/cost.rs delete mode 100644 crates/giql-datafusion/src/exec/bin_expand.rs delete mode 100644 crates/giql-datafusion/src/exec/binned_join.rs delete mode 100644 crates/giql-datafusion/src/exec/binned_sql.rs delete mode 100644 crates/giql-datafusion/src/exec/mod.rs delete mode 100644 crates/giql-datafusion/src/exec/sweep_line.rs delete mode 100644 crates/giql-datafusion/src/optimizer.rs delete mode 100644 crates/giql-datafusion/src/pattern.rs delete mode 100644 crates/giql-datafusion/src/pruning.rs delete mode 100644 crates/giql-datafusion/src/stats/metadata.rs delete mode 100644 crates/giql-datafusion/src/stats/mod.rs delete mode 100644 crates/giql-datafusion/src/stats/sampler.rs delete mode 100644 crates/giql-datafusion/src/stats/types.rs delete mode 100644 crates/giql-datafusion/tests/integration_test.rs diff --git a/crates/giql-datafusion/Cargo.lock b/crates/giql-datafusion/Cargo.lock index 9e287be..1fbda9f 100644 --- a/crates/giql-datafusion/Cargo.lock +++ b/crates/giql-datafusion/Cargo.lock @@ -1539,7 +1539,6 @@ version = "0.1.0" dependencies = [ "arrow", "datafusion", - "futures", "log", "parquet", "tempfile", diff --git a/crates/giql-datafusion/Cargo.toml b/crates/giql-datafusion/Cargo.toml index b0deaaa..0bea68e 100644 --- a/crates/giql-datafusion/Cargo.toml +++ b/crates/giql-datafusion/Cargo.toml @@ -8,10 +8,9 @@ license = "MIT" [dependencies] arrow = { version = "58", default-features = false, features = ["prettyprint"] } datafusion = "53" -futures = "0.3" log = "0.4" -parquet = "58" -tokio = { version = "1", features = ["rt-multi-thread", "macros"] } [dev-dependencies] +parquet = "58" tempfile = "3" +tokio = { version = "1", features = ["rt-multi-thread", "macros"] } diff --git a/crates/giql-datafusion/src/bin/bench_intersects.rs b/crates/giql-datafusion/src/bin/bench_intersects.rs deleted file mode 100644 index f35b9ea..0000000 --- a/crates/giql-datafusion/src/bin/bench_intersects.rs +++ /dev/null @@ -1,165 +0,0 @@ -//! Benchmark binary for the INTERSECTS join optimizer. -//! -//! Usage: -//! bench_intersects [--reps N] [--op join|pairs] -//! -//! Outputs one JSON line per timed run: -//! {"op":"intersect_join","engine":"giql-optimized","rep":0,"time_s":0.123,"n_rows":4567} - -use std::path::PathBuf; -use std::time::Instant; - -use datafusion::execution::SessionStateBuilder; -use datafusion::prelude::*; -use giql_datafusion::{register_optimizer, IntersectsOptimizerConfig}; - -const INTERSECT_JOIN_SQL: &str = "\ - SELECT DISTINCT a.chrom, a.start, a.\"end\" \ - FROM a JOIN b \ - ON a.chrom = b.chrom \ - AND a.start < b.\"end\" \ - AND a.\"end\" > b.start"; - -const INTERSECT_PAIRS_SQL: &str = "\ - SELECT a.chrom, a.start, a.\"end\", \ - b.chrom AS chrom_b, b.start AS start_b, b.\"end\" AS end_b \ - FROM a JOIN b \ - ON a.chrom = b.chrom \ - AND a.start < b.\"end\" \ - AND a.\"end\" > b.start"; - -#[tokio::main] -async fn main() -> Result<(), Box> { - let args: Vec = std::env::args().collect(); - - if args.len() < 3 { - eprintln!( - "Usage: {} \ - [--reps N] [--op join|pairs]", - args[0] - ); - std::process::exit(1); - } - - let left_path = PathBuf::from(&args[1]); - let right_path = PathBuf::from(&args[2]); - - let mut reps = 3; - let mut op = "join".to_string(); - let mut no_optimizer = false; - let mut force_binned = false; - let mut sql_binned: Option = None; - let mut enable_logical = false; - - let mut i = 3; - while i < args.len() { - match args[i].as_str() { - "--reps" => { - i += 1; - reps = args[i].parse()?; - } - "--op" => { - i += 1; - op = args[i].clone(); - } - "--no-optimizer" => { - no_optimizer = true; - } - "--force-binned" => { - force_binned = true; - } - "--sql-binned" => { - i += 1; - sql_binned = Some(args[i].parse()?); - } - "--enable-logical-rule" => { - enable_logical = true; - } - _ => { - eprintln!("Unknown arg: {}", args[i]); - std::process::exit(1); - } - } - i += 1; - } - - let sql: String = if let Some(bs) = sql_binned { - // Run the pure SQL binned approach through Rust DF - format!( - "WITH __giql_left AS (\ - SELECT *, UNNEST(range(CAST(\"start\" / {bs} AS BIGINT), \ - CAST((\"end\" - 1) / {bs} + 1 AS BIGINT))) AS __giql_bin \ - FROM a), \ - __giql_right AS (\ - SELECT *, UNNEST(range(CAST(\"start\" / {bs} AS BIGINT), \ - CAST((\"end\" - 1) / {bs} + 1 AS BIGINT))) AS __giql_bin \ - FROM b) \ - SELECT DISTINCT \ - l.\"chrom\", l.\"start\", l.\"end\", \ - r.\"chrom\" AS chrom_r, r.\"start\" AS start_r, r.\"end\" AS end_r \ - FROM __giql_left AS l \ - JOIN __giql_right AS r \ - ON l.\"chrom\" = r.\"chrom\" AND l.__giql_bin = r.__giql_bin \ - WHERE l.\"start\" < r.\"end\" AND l.\"end\" > r.\"start\"" - ) - } else { - match op.as_str() { - "join" => INTERSECT_JOIN_SQL.to_string(), - "pairs" => INTERSECT_PAIRS_SQL.to_string(), - _ => { - eprintln!("Unknown op: {op}. Use 'join' or 'pairs'."); - std::process::exit(1); - } - } - }; - - let op_name = format!("intersect_{op}"); - - let ctx = if no_optimizer { - SessionContext::new() - } else { - let config = if force_binned { - IntersectsOptimizerConfig { - p99_median_threshold: f64::MAX, - cv_threshold: f64::MAX, - max_sample_row_groups: 3, - enable_logical_rule: enable_logical, - } - } else if enable_logical { - IntersectsOptimizerConfig { - enable_logical_rule: true, - ..Default::default() - } - } else { - IntersectsOptimizerConfig::default() - }; - let state = SessionStateBuilder::new() - .with_default_features() - .build(); - let state = register_optimizer(state, config); - SessionContext::from(state) - }; - - ctx.register_parquet("a", left_path.to_str().unwrap(), Default::default()) - .await?; - ctx.register_parquet("b", right_path.to_str().unwrap(), Default::default()) - .await?; - - // Warmup - let _ = ctx.sql(&sql).await?.collect().await?; - - // Timed reps - for rep in 0..reps { - let t0 = Instant::now(); - let batches = ctx.sql(&sql).await?.collect().await?; - let elapsed = t0.elapsed().as_secs_f64(); - let n_rows: usize = batches.iter().map(|b| b.num_rows()).sum(); - - println!( - "{{\"op\":\"{op_name}\",\"engine\":\"giql-optimized\",\ - \"rep\":{rep},\"time_s\":{elapsed:.6},\"n_rows\":{n_rows}}}" - ); - } - - Ok(()) -} diff --git a/crates/giql-datafusion/src/cost.rs b/crates/giql-datafusion/src/cost.rs deleted file mode 100644 index e1a0005..0000000 --- a/crates/giql-datafusion/src/cost.rs +++ /dev/null @@ -1,309 +0,0 @@ -use crate::stats::IntervalStats; -use crate::IntersectsOptimizerConfig; - -/// Which side of the join has fewer rows and should be materialized. -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub enum SmallSide { - Left, - Right, -} - -/// Join algorithm selected by the cost model. -#[derive(Debug, Clone, PartialEq)] -pub enum JoinStrategy { - /// Default nested-loop join (no plan rewrite). - NestedLoop, - /// Sweep-line join: sort both sides by start, sweep with an active - /// set. O((n+m) log(n+m) + k). - SweepLine { - /// Which side to materialize (the smaller one). - build_side: SmallSide, - }, - /// Binned equi-join: expand intervals into genome bins, hash-join - /// on bin ID. O(n+m+k) amortized for uniform widths. - BinnedJoin { - /// Bin width in base pairs. - bin_size: usize, - }, -} - -/// Cost model for choosing the optimal INTERSECTS join algorithm. -/// -/// Uses two fast short-circuit signals (p99/median ratio and CV) to -/// detect distributions where binning degrades, and falls back to a -/// cost comparison for ambiguous cases. -pub struct CostModel { - p99_median_threshold: f64, - cv_threshold: f64, -} - -/// Relative cost constants for the cost comparison branch. -/// These are unitless scaling factors, not absolute times. -const HASH_COST: f64 = 1.0; -const COMPARE_COST: f64 = 2.0; - -/// Minimum and maximum bin sizes to clamp the adaptive bin size. -const MIN_BIN_SIZE: usize = 1_000; -const MAX_BIN_SIZE: usize = 1_000_000; - -impl CostModel { - pub fn new(config: &IntersectsOptimizerConfig) -> Self { - Self { - p99_median_threshold: config.p99_median_threshold, - cv_threshold: config.cv_threshold, - } - } - - /// Choose the optimal join strategy based on interval statistics - /// from both sides of the join. - pub fn decide( - &self, - left: &IntervalStats, - right: &IntervalStats, - ) -> JoinStrategy { - let build_side = if left.row_count <= right.row_count { - SmallSide::Left - } else { - SmallSide::Right - }; - - // Short-circuit 1: heavy-tailed distribution. - if left.width.p99_median_ratio > self.p99_median_threshold - || right.width.p99_median_ratio > self.p99_median_threshold - { - return JoinStrategy::SweepLine { build_side }; - } - - // Short-circuit 2: high width variance. - if left.width.cv > self.cv_threshold - || right.width.cv > self.cv_threshold - { - return JoinStrategy::SweepLine { build_side }; - } - - // Cost comparison: estimate binned vs sweep costs. - let bin_size = self.estimate_optimal_bin_size(left, right); - let binned_cost = self.estimate_binned_cost(left, right, bin_size); - let sweep_cost = self.estimate_sweep_cost(left, right); - - if binned_cost < sweep_cost { - JoinStrategy::BinnedJoin { bin_size } - } else { - JoinStrategy::SweepLine { build_side } - } - } - - /// Estimate optimal bin size from the width distributions. - /// - /// Uses p95 as the bin width so that ~95% of intervals fit in a - /// single bin (replication factor ≈ 1). - fn estimate_optimal_bin_size( - &self, - left: &IntervalStats, - right: &IntervalStats, - ) -> usize { - // Use the larger p95 so both sides have low replication. - let p95 = left.width.p95.max(right.width.p95); - let raw = p95.ceil() as usize; - raw.clamp(MIN_BIN_SIZE, MAX_BIN_SIZE) - } - - /// Estimate cost of binned equi-join. - /// - /// Each interval is replicated into `mean_width / bin_size + 1` - /// bin entries, then hash-joined. Cost scales with total expanded - /// row count. - fn estimate_binned_cost( - &self, - left: &IntervalStats, - right: &IntervalStats, - bin_size: usize, - ) -> f64 { - let left_replication = - left.width.mean / bin_size as f64 + 1.0; - let right_replication = - right.width.mean / bin_size as f64 + 1.0; - - let expanded_left = - left.row_count as f64 * left_replication; - let expanded_right = - right.row_count as f64 * right_replication; - - (expanded_left + expanded_right) * HASH_COST - } - - /// Estimate cost of sweep-line join. - /// - /// Dominated by sorting both sides: O((n+m) log(n+m)). If either - /// side is already sorted, the cost drops by ~50%. - fn estimate_sweep_cost( - &self, - left: &IntervalStats, - right: &IntervalStats, - ) -> f64 { - let n = left.row_count as f64; - let m = right.row_count as f64; - let total = n + m; - - let mut cost = total * total.log2() * COMPARE_COST; - - // If either side is sorted, we skip one of the two sorts. - if left.is_sorted_by_start || right.is_sorted_by_start { - cost *= 0.5; - } - - cost - } -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::stats::{RowGroupBounds, WidthStats}; - - fn make_stats( - row_count: usize, - median: f64, - mean: f64, - p95: f64, - p99: f64, - cv: f64, - sorted: bool, - ) -> IntervalStats { - IntervalStats { - row_count, - domain_min: 0, - domain_max: 1_000_000, - is_sorted_by_start: sorted, - row_group_bounds: vec![RowGroupBounds { - min_start: 0, - max_start: 900_000, - min_end: 100, - max_end: 1_000_000, - row_count, - }], - width: WidthStats { - median, - mean, - p95, - p99, - cv, - p99_median_ratio: if median > 0.0 { - p99 / median - } else { - 0.0 - }, - }, - } - } - - fn default_config() -> IntersectsOptimizerConfig { - IntersectsOptimizerConfig::default() - } - - #[test] - fn test_heavy_tailed_selects_sweep_line() { - let model = CostModel::new(&default_config()); - // p99/median = 100/5 = 20 > 10 - let left = make_stats(100_000, 5.0, 10.0, 50.0, 100.0, 0.8, false); - let right = make_stats(100_000, 100.0, 100.0, 100.0, 100.0, 0.0, false); - - match model.decide(&left, &right) { - JoinStrategy::SweepLine { .. } => {} - other => panic!("Expected SweepLine, got {:?}", other), - } - } - - #[test] - fn test_high_cv_selects_sweep_line() { - let model = CostModel::new(&default_config()); - // CV = 2.0 > 1.5 - let left = make_stats(100_000, 100.0, 100.0, 100.0, 100.0, 2.0, false); - let right = make_stats(100_000, 100.0, 100.0, 100.0, 100.0, 0.5, false); - - match model.decide(&left, &right) { - JoinStrategy::SweepLine { .. } => {} - other => panic!("Expected SweepLine, got {:?}", other), - } - } - - #[test] - fn test_uniform_selects_binned() { - let model = CostModel::new(&default_config()); - // Uniform 100bp intervals, low CV, p99/median ≈ 1 - let left = make_stats(1_000_000, 100.0, 100.0, 100.0, 100.0, 0.0, false); - let right = make_stats(1_000_000, 100.0, 100.0, 100.0, 100.0, 0.0, false); - - match model.decide(&left, &right) { - JoinStrategy::BinnedJoin { bin_size } => { - assert!(bin_size >= MIN_BIN_SIZE); - } - other => panic!("Expected BinnedJoin, got {:?}", other), - } - } - - #[test] - fn test_smaller_side_becomes_build() { - let model = CostModel::new(&default_config()); - // High CV triggers sweep line; left has fewer rows - let left = make_stats(100_000, 100.0, 500.0, 1000.0, 5000.0, 2.0, true); - let right = make_stats(1_000_000, 100.0, 500.0, 1000.0, 5000.0, 0.5, false); - - match model.decide(&left, &right) { - JoinStrategy::SweepLine { build_side } => { - assert_eq!(build_side, SmallSide::Left); - } - other => panic!("Expected SweepLine, got {:?}", other), - } - } - - #[test] - fn test_bin_size_clamped() { - let model = CostModel::new(&default_config()); - // Very small p95 — bin size should clamp to MIN_BIN_SIZE - let left = make_stats(100_000, 10.0, 10.0, 10.0, 10.0, 0.1, false); - let right = make_stats(100_000, 10.0, 10.0, 10.0, 10.0, 0.1, false); - - let bin_size = model.estimate_optimal_bin_size(&left, &right); - assert_eq!(bin_size, MIN_BIN_SIZE); - } - - #[test] - fn test_right_build_when_right_smaller() { - let model = CostModel::new(&default_config()); - // CV above threshold, right has fewer rows - let left = make_stats(500_000, 100.0, 200.0, 500.0, 600.0, 1.6, true); - let right = make_stats(100_000, 100.0, 200.0, 500.0, 600.0, 0.5, true); - - match model.decide(&left, &right) { - JoinStrategy::SweepLine { build_side } => { - assert_eq!(build_side, SmallSide::Right); - } - other => panic!("Expected SweepLine, got {:?}", other), - } - } - - #[test] - fn test_at_threshold_does_not_short_circuit() { - let model = CostModel::new(&default_config()); - // p99/median = exactly 10.0 — threshold is >, so should NOT - // short-circuit to sweep line - let left = make_stats( - 1_000_000, 100.0, 100.0, 500.0, 1000.0, 0.3, false, - ); - let right = make_stats( - 1_000_000, 100.0, 100.0, 500.0, 1000.0, 0.3, false, - ); - - // p99/median = 1000/100 = 10.0, which is NOT > 10.0 - let strategy = model.decide(&left, &right); - // Should reach cost comparison, not short-circuit - match strategy { - JoinStrategy::SweepLine { .. } - | JoinStrategy::BinnedJoin { .. } => {} - JoinStrategy::NestedLoop => { - panic!("Should not return NestedLoop with stats") - } - } - } -} diff --git a/crates/giql-datafusion/src/exec/bin_expand.rs b/crates/giql-datafusion/src/exec/bin_expand.rs deleted file mode 100644 index b765489..0000000 --- a/crates/giql-datafusion/src/exec/bin_expand.rs +++ /dev/null @@ -1,249 +0,0 @@ -use std::any::Any; -use std::fmt; -use std::sync::Arc; - -use arrow::array::{ - Array, ArrayRef, Int32Array, Int64Array, RecordBatch, - UInt64Array, -}; -use arrow::datatypes::{DataType, Field, SchemaRef}; -use datafusion::common::Result; -use datafusion::execution::SendableRecordBatchStream; -use datafusion::physical_expr::EquivalenceProperties; -use datafusion::physical_plan::execution_plan::{ - Boundedness, EmissionType, -}; -use datafusion::physical_plan::stream::RecordBatchStreamAdapter; -use datafusion::physical_plan::{ - DisplayAs, DisplayFormatType, ExecutionPlan, Partitioning, - PlanProperties, -}; - -/// Stateless per-partition exec that expands each interval into rows -/// for each genome bin it touches. -/// -/// Appends two columns: -/// - `__giql_bin`: the bin ID for this expanded row -/// - `__giql_first_bin`: `start / bin_size` — the interval's first bin -/// -/// The `__giql_first_bin` column enables the canonical-bin -/// deduplication trick: after the hash join, a filter keeps only -/// matches where `__giql_bin == max(left.__giql_first_bin, -/// right.__giql_first_bin)`, ensuring each pair is emitted exactly -/// once. -#[derive(Debug)] -pub struct BinExpandExec { - input: Arc, - start_col_idx: usize, - end_col_idx: usize, - bin_size: usize, - schema: SchemaRef, - properties: Arc, -} - -impl BinExpandExec { - pub fn new( - input: Arc, - start_col_idx: usize, - end_col_idx: usize, - bin_size: usize, - ) -> Self { - let input_schema = input.schema(); - let mut fields: Vec> = - input_schema.fields().iter().cloned().collect(); - fields.push(Arc::new(Field::new( - "__giql_bin", - DataType::Int64, - false, - ))); - fields.push(Arc::new(Field::new( - "__giql_first_bin", - DataType::Int64, - false, - ))); - let schema = - Arc::new(arrow::datatypes::Schema::new(fields)); - - let properties = Arc::new(PlanProperties::new( - EquivalenceProperties::new(schema.clone()), - input.properties().partitioning.clone(), - EmissionType::Incremental, - Boundedness::Bounded, - )); - - Self { - input, - start_col_idx, - end_col_idx, - bin_size, - schema, - properties, - } - } - - /// Number of columns added by this exec (bin + first_bin). - pub const EXTRA_COLS: usize = 2; -} - -impl DisplayAs for BinExpandExec { - fn fmt_as( - &self, - _t: DisplayFormatType, - f: &mut fmt::Formatter<'_>, - ) -> fmt::Result { - write!( - f, - "BinExpandExec: start_col={}, end_col={}, bin_size={}", - self.start_col_idx, self.end_col_idx, self.bin_size - ) - } -} - -impl ExecutionPlan for BinExpandExec { - fn name(&self) -> &str { - "BinExpandExec" - } - - fn as_any(&self) -> &dyn Any { - self - } - - fn schema(&self) -> SchemaRef { - self.schema.clone() - } - - fn properties(&self) -> &Arc { - &self.properties - } - - fn children(&self) -> Vec<&Arc> { - vec![&self.input] - } - - fn with_new_children( - self: Arc, - children: Vec>, - ) -> Result> { - Ok(Arc::new(BinExpandExec::new( - children[0].clone(), - self.start_col_idx, - self.end_col_idx, - self.bin_size, - ))) - } - - fn execute( - &self, - partition: usize, - context: Arc, - ) -> Result { - let input_stream = - self.input.execute(partition, context)?; - let start_idx = self.start_col_idx; - let end_idx = self.end_col_idx; - let bin_size = self.bin_size as i64; - let schema = self.schema.clone(); - - let output_stream = futures::stream::unfold( - input_stream, - move |mut stream| { - let schema = schema.clone(); - async move { - use futures::StreamExt; - match stream.next().await { - Some(Ok(batch)) => { - let result = expand_batch( - &batch, start_idx, end_idx, - bin_size, &schema, - ); - Some((result, stream)) - } - Some(Err(e)) => Some((Err(e), stream)), - None => None, - } - } - }, - ); - - Ok(Box::pin(RecordBatchStreamAdapter::new( - self.schema.clone(), - output_stream, - ))) - } -} - -/// Expand a single batch: replicate each row for each bin it touches, -/// adding `__giql_bin` and `__giql_first_bin` columns. -fn expand_batch( - batch: &RecordBatch, - start_idx: usize, - end_idx: usize, - bin_size: i64, - output_schema: &SchemaRef, -) -> Result { - let num_rows = batch.num_rows(); - if num_rows == 0 { - return Ok(RecordBatch::new_empty(output_schema.clone())); - } - - let start_col = batch.column(start_idx); - let end_col = batch.column(end_idx); - - let mut bin_ranges: Vec<(i64, i64)> = Vec::with_capacity(num_rows); - let mut total_expanded = 0usize; - - for row in 0..num_rows { - let start = get_i64(start_col.as_ref(), row).unwrap_or(0); - let end = get_i64(end_col.as_ref(), row).unwrap_or(0); - let first_bin = start / bin_size; - let last_bin = (end - 1).max(0) / bin_size; - let n_bins = (last_bin - first_bin + 1).max(0) as usize; - bin_ranges.push((first_bin, last_bin)); - total_expanded += n_bins; - } - - let mut row_indices: Vec = - Vec::with_capacity(total_expanded); - let mut bin_ids: Vec = Vec::with_capacity(total_expanded); - let mut first_bins: Vec = - Vec::with_capacity(total_expanded); - - for (row, &(first_bin, last_bin)) in - bin_ranges.iter().enumerate() - { - for bin in first_bin..=last_bin { - row_indices.push(row as u64); - bin_ids.push(bin); - first_bins.push(first_bin); - } - } - - let row_indices_arr = UInt64Array::from(row_indices); - let mut columns: Vec = - Vec::with_capacity(batch.num_columns() + BinExpandExec::EXTRA_COLS); - - for col_idx in 0..batch.num_columns() { - let col = batch.column(col_idx); - let taken = - arrow::compute::take(col.as_ref(), &row_indices_arr, None)?; - columns.push(taken); - } - - columns.push(Arc::new(Int64Array::from(bin_ids))); - columns.push(Arc::new(Int64Array::from(first_bins))); - - Ok(RecordBatch::try_new(output_schema.clone(), columns)?) -} - -fn get_i64(array: &dyn Array, idx: usize) -> Option { - array - .as_any() - .downcast_ref::() - .map(|arr| arr.value(idx)) - .or_else(|| { - array - .as_any() - .downcast_ref::() - .map(|arr| arr.value(idx) as i64) - }) -} diff --git a/crates/giql-datafusion/src/exec/binned_join.rs b/crates/giql-datafusion/src/exec/binned_join.rs deleted file mode 100644 index a0d9e72..0000000 --- a/crates/giql-datafusion/src/exec/binned_join.rs +++ /dev/null @@ -1,377 +0,0 @@ -use std::any::Any; -use std::collections::HashMap; -use std::fmt; -use std::sync::Arc; - -use arrow::array::{ - Array, ArrayRef, Int32Array, Int64Array, RecordBatch, StringArray, - StringViewArray, -}; -use arrow::datatypes::SchemaRef; -use datafusion::common::Result; -use datafusion::execution::SendableRecordBatchStream; -use datafusion::physical_expr::{EquivalenceProperties, Partitioning}; -use datafusion::physical_plan::stream::RecordBatchStreamAdapter; -use datafusion::physical_plan::execution_plan::{ - Boundedness, EmissionType, -}; -use datafusion::physical_plan::{ - DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, -}; - -use crate::pattern::IntervalColumns; - -/// Custom execution plan implementing the binned equi-join for -/// interval overlap. -/// -/// Each interval is expanded into genome bins of fixed size. A hash -/// map is built from the right side keyed by `(chrom, bin_id)`. The -/// left side probes the map, and a post-filter removes false positives. -/// Output is deduplicated to avoid emitting duplicate pairs when an -/// interval spans multiple bins. -/// -/// Complexity: O((n+m) * avg_replication + k) where avg_replication -/// is mean_width / bin_size + 1. -#[derive(Debug)] -pub struct BinnedJoinExec { - left: Arc, - right: Arc, - left_cols: IntervalColumns, - right_cols: IntervalColumns, - bin_size: usize, - schema: SchemaRef, - properties: Arc, -} - -impl BinnedJoinExec { - pub fn new( - left: Arc, - right: Arc, - left_cols: IntervalColumns, - right_cols: IntervalColumns, - schema: SchemaRef, - bin_size: usize, - ) -> Self { - let properties = Arc::new(PlanProperties::new( - EquivalenceProperties::new(schema.clone()), - Partitioning::UnknownPartitioning(1), - EmissionType::Final, - Boundedness::Bounded, - )); - - Self { - left, - right, - left_cols, - right_cols, - bin_size, - schema, - properties, - } - } -} - -impl DisplayAs for BinnedJoinExec { - fn fmt_as( - &self, - _t: DisplayFormatType, - f: &mut fmt::Formatter<'_>, - ) -> fmt::Result { - write!(f, "BinnedJoinExec: bin_size={}", self.bin_size) - } -} - -impl ExecutionPlan for BinnedJoinExec { - fn name(&self) -> &str { - "BinnedJoinExec" - } - - fn as_any(&self) -> &dyn Any { - self - } - - fn schema(&self) -> SchemaRef { - self.schema.clone() - } - - fn properties(&self) -> &Arc { - &self.properties - } - - fn children(&self) -> Vec<&Arc> { - vec![&self.left, &self.right] - } - - fn with_new_children( - self: Arc, - children: Vec>, - ) -> Result> { - Ok(Arc::new(BinnedJoinExec::new( - children[0].clone(), - children[1].clone(), - self.left_cols.clone(), - self.right_cols.clone(), - self.schema.clone(), - self.bin_size, - ))) - } - - fn execute( - &self, - _partition: usize, - context: Arc, - ) -> Result { - let left = self.left.clone(); - let right = self.right.clone(); - let left_cols = self.left_cols.clone(); - let right_cols = self.right_cols.clone(); - let schema = self.schema.clone(); - let bin_size = self.bin_size; - let ctx = context; - - let stream = futures::stream::once(async move { - let left_batches = - collect_all_partitions(&left, &ctx).await?; - let right_batches = - collect_all_partitions(&right, &ctx).await?; - - binned_join( - &schema, - &left_batches, - &right_batches, - &left_cols, - &right_cols, - bin_size, - ) - }); - - Ok(Box::pin(RecordBatchStreamAdapter::new( - self.schema.clone(), - stream, - ))) - } -} - -/// A reference to a specific row in a batch. -#[derive(Debug, Clone, Hash, PartialEq, Eq)] -struct RowRef { - batch_idx: usize, - row_idx: usize, -} - -/// Interval data extracted from a row. -struct IntervalRow { - chrom: String, - start: i64, - end: i64, - row_ref: RowRef, -} - -/// Core binned join algorithm. -fn binned_join( - schema: &SchemaRef, - left_batches: &[RecordBatch], - right_batches: &[RecordBatch], - left_cols: &IntervalColumns, - right_cols: &IntervalColumns, - bin_size: usize, -) -> Result { - let right_intervals = - extract_interval_rows(right_batches, right_cols)?; - let mut right_map: HashMap<(String, i64), Vec> = - HashMap::new(); - - for (idx, interval) in right_intervals.iter().enumerate() { - let start_bin = interval.start / bin_size as i64; - let end_bin = (interval.end - 1) / bin_size as i64; - for bin in start_bin..=end_bin { - right_map - .entry((interval.chrom.clone(), bin)) - .or_default() - .push(idx); - } - } - - let left_intervals = - extract_interval_rows(left_batches, left_cols)?; - - let mut seen = std::collections::HashSet::new(); - let mut matches: Vec<(RowRef, RowRef)> = Vec::new(); - - for (li, l) in left_intervals.iter().enumerate() { - let start_bin = l.start / bin_size as i64; - let end_bin = (l.end - 1) / bin_size as i64; - - for bin in start_bin..=end_bin { - let key = (l.chrom.clone(), bin); - if let Some(right_indices) = right_map.get(&key) { - for &ri in right_indices { - if seen.contains(&(li, ri)) { - continue; - } - - let r = &right_intervals[ri]; - if l.start < r.end && l.end > r.start { - seen.insert((li, ri)); - matches.push(( - l.row_ref.clone(), - r.row_ref.clone(), - )); - } - } - } - } - } - - build_output(schema, left_batches, right_batches, &matches) -} - -/// Extract interval rows from batches. -fn extract_interval_rows( - batches: &[RecordBatch], - cols: &IntervalColumns, -) -> Result> { - let mut rows = Vec::new(); - - for (batch_idx, batch) in batches.iter().enumerate() { - let chrom_col = batch.column(cols.chrom_idx); - let start_col = batch.column(cols.start_idx); - let end_col = batch.column(cols.end_idx); - - for row_idx in 0..batch.num_rows() { - if chrom_col.is_null(row_idx) - || start_col.is_null(row_idx) - || end_col.is_null(row_idx) - { - continue; - } - let chrom = get_string_value(chrom_col.as_ref(), row_idx) - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "Chrom column has unsupported string type" - .to_string(), - ) - })?; - let start = get_i64_value(start_col.as_ref(), row_idx) - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "Start column is not Int32 or Int64".to_string(), - ) - })?; - let end = get_i64_value(end_col.as_ref(), row_idx) - .ok_or_else(|| { - datafusion::error::DataFusionError::Internal( - "End column is not Int32 or Int64".to_string(), - ) - })?; - rows.push(IntervalRow { - chrom, - start, - end, - row_ref: RowRef { - batch_idx, - row_idx, - }, - }); - } - } - - Ok(rows) -} - -/// Build output RecordBatch from matched row pairs. -fn build_output( - schema: &SchemaRef, - left_batches: &[RecordBatch], - right_batches: &[RecordBatch], - matches: &[(RowRef, RowRef)], -) -> Result { - if matches.is_empty() { - return Ok(RecordBatch::new_empty(schema.clone())); - } - - let left_schema = left_batches[0].schema(); - let right_schema = right_batches[0].schema(); - let num_left_cols = left_schema.fields().len(); - let num_right_cols = right_schema.fields().len(); - - let mut columns: Vec = - Vec::with_capacity(num_left_cols + num_right_cols); - - for col_idx in 0..num_left_cols { - let values: Vec = matches - .iter() - .map(|(lr, _)| { - left_batches[lr.batch_idx] - .column(col_idx) - .slice(lr.row_idx, 1) - }) - .collect(); - - let refs: Vec<&dyn Array> = - values.iter().map(|a| a.as_ref()).collect(); - columns.push(arrow::compute::concat(&refs)?); - } - - for col_idx in 0..num_right_cols { - let values: Vec = matches - .iter() - .map(|(_, rr)| { - right_batches[rr.batch_idx] - .column(col_idx) - .slice(rr.row_idx, 1) - }) - .collect(); - - let refs: Vec<&dyn Array> = - values.iter().map(|a| a.as_ref()).collect(); - columns.push(arrow::compute::concat(&refs)?); - } - - Ok(RecordBatch::try_new(schema.clone(), columns)?) -} - -/// Collect all record batches from all partitions concurrently. -async fn collect_all_partitions( - plan: &Arc, - context: &Arc, -) -> Result> { - datafusion::physical_plan::collect( - plan.clone(), - context.clone(), - ) - .await -} - -/// Extract an i64 value from an array that may be Int32Array or -/// Int64Array. -fn get_i64_value(array: &dyn Array, idx: usize) -> Option { - array - .as_any() - .downcast_ref::() - .map(|arr| arr.value(idx)) - .or_else(|| { - array - .as_any() - .downcast_ref::() - .map(|arr| arr.value(idx) as i64) - }) -} - -/// Extract a string value from an array that may be StringArray or -/// StringViewArray (DataFusion v47+ uses StringViewArray by default). -fn get_string_value( - array: &dyn Array, - idx: usize, -) -> Option { - array - .as_any() - .downcast_ref::() - .map(|arr| arr.value(idx).to_string()) - .or_else(|| { - array - .as_any() - .downcast_ref::() - .map(|arr| arr.value(idx).to_string()) - }) -} diff --git a/crates/giql-datafusion/src/exec/binned_sql.rs b/crates/giql-datafusion/src/exec/binned_sql.rs deleted file mode 100644 index b251371..0000000 --- a/crates/giql-datafusion/src/exec/binned_sql.rs +++ /dev/null @@ -1,208 +0,0 @@ -use std::any::Any; -use std::fmt; -use std::sync::Arc; - -use arrow::datatypes::SchemaRef; -use datafusion::common::Result; -use datafusion::execution::SendableRecordBatchStream; -use datafusion::physical_expr::{EquivalenceProperties, Partitioning}; -use datafusion::physical_plan::execution_plan::{ - Boundedness, EmissionType, -}; -use datafusion::physical_plan::stream::RecordBatchStreamAdapter; -use datafusion::physical_plan::{ - DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, -}; - -use crate::pattern::IntervalColumns; - -/// Binned interval join that delegates to DataFusion's SQL engine. -/// -/// Collects both inputs, registers them as temporary tables, and -/// executes the binned equi-join as a SQL query through a fresh -/// SessionContext. This lets DataFusion's built-in UNNEST and -/// DISTINCT optimizations handle the bin expansion and dedup, -/// matching the performance of hand-written SQL. -#[derive(Debug)] -pub struct BinnedSqlExec { - left: Arc, - right: Arc, - left_cols: IntervalColumns, - right_cols: IntervalColumns, - bin_size: usize, - schema: SchemaRef, - properties: Arc, -} - -impl BinnedSqlExec { - pub fn new( - left: Arc, - right: Arc, - left_cols: IntervalColumns, - right_cols: IntervalColumns, - schema: SchemaRef, - bin_size: usize, - ) -> Self { - let properties = Arc::new(PlanProperties::new( - EquivalenceProperties::new(schema.clone()), - Partitioning::UnknownPartitioning(1), - EmissionType::Final, - Boundedness::Bounded, - )); - - Self { - left, - right, - left_cols, - right_cols, - bin_size, - schema, - properties, - } - } - - fn build_sql(&self) -> String { - let b = self.bin_size; - let lc = &self.left_cols; - let rc = &self.right_cols; - - format!( - "WITH __giql_left AS (\ - SELECT *, UNNEST(range(\ - CAST(\"{ls}\" / {b} AS BIGINT), \ - CAST((\"{le}\" - 1) / {b} + 1 AS BIGINT)\ - )) AS __giql_bin FROM __giql_a), \ - __giql_right AS (\ - SELECT *, UNNEST(range(\ - CAST(\"{rs}\" / {b} AS BIGINT), \ - CAST((\"{re}\" - 1) / {b} + 1 AS BIGINT)\ - )) AS __giql_bin FROM __giql_b) \ - SELECT DISTINCT \ - l.\"{lch}\", l.\"{ls}\", l.\"{le}\", \ - r.\"{rch}\" AS \"{rch}\", \ - r.\"{rs}\" AS \"{rs}\", \ - r.\"{re}\" AS \"{re}\" \ - FROM __giql_left AS l \ - JOIN __giql_right AS r \ - ON l.\"{lch}\" = r.\"{rch}\" \ - AND l.__giql_bin = r.__giql_bin \ - WHERE l.\"{ls}\" < r.\"{re}\" \ - AND l.\"{le}\" > r.\"{rs}\"", - b = b, - lch = lc.chrom_col, - ls = lc.start_col, - le = lc.end_col, - rch = rc.chrom_col, - rs = rc.start_col, - re = rc.end_col, - ) - } -} - -impl DisplayAs for BinnedSqlExec { - fn fmt_as( - &self, - _t: DisplayFormatType, - f: &mut fmt::Formatter<'_>, - ) -> fmt::Result { - write!(f, "BinnedSqlExec: bin_size={}", self.bin_size) - } -} - -impl ExecutionPlan for BinnedSqlExec { - fn name(&self) -> &str { - "BinnedSqlExec" - } - - fn as_any(&self) -> &dyn Any { - self - } - - fn schema(&self) -> SchemaRef { - self.schema.clone() - } - - fn properties(&self) -> &Arc { - &self.properties - } - - fn children(&self) -> Vec<&Arc> { - vec![&self.left, &self.right] - } - - fn with_new_children( - self: Arc, - children: Vec>, - ) -> Result> { - Ok(Arc::new(BinnedSqlExec::new( - children[0].clone(), - children[1].clone(), - self.left_cols.clone(), - self.right_cols.clone(), - self.schema.clone(), - self.bin_size, - ))) - } - - fn execute( - &self, - _partition: usize, - context: Arc, - ) -> Result { - let left_plan = self.left.clone(); - let right_plan = self.right.clone(); - let sql = self.build_sql(); - let schema = self.schema.clone(); - - let stream = futures::stream::once(async move { - use datafusion::prelude::SessionContext; - - // Collect both sides - let left_batches = datafusion::physical_plan::collect( - left_plan, - context.clone(), - ) - .await?; - let right_batches = datafusion::physical_plan::collect( - right_plan, context, - ) - .await?; - - // Create a fresh context and register as memory tables - let ctx = SessionContext::new(); - let left_table = - datafusion::datasource::MemTable::try_new( - left_batches[0].schema(), - vec![left_batches], - )?; - let right_table = - datafusion::datasource::MemTable::try_new( - right_batches[0].schema(), - vec![right_batches], - )?; - ctx.register_table("__giql_a", Arc::new(left_table))?; - ctx.register_table("__giql_b", Arc::new(right_table))?; - - // Execute the binned SQL - let df = ctx.sql(&sql).await?; - let batches = df.collect().await?; - - if batches.is_empty() { - return Ok( - arrow::record_batch::RecordBatch::new_empty(schema), - ); - } - - // Concat all result batches - Ok(arrow::compute::concat_batches( - &batches[0].schema(), - &batches, - )?) - }); - - Ok(Box::pin(RecordBatchStreamAdapter::new( - self.schema.clone(), - stream, - ))) - } -} diff --git a/crates/giql-datafusion/src/exec/mod.rs b/crates/giql-datafusion/src/exec/mod.rs deleted file mode 100644 index 935d4d3..0000000 --- a/crates/giql-datafusion/src/exec/mod.rs +++ /dev/null @@ -1,9 +0,0 @@ -pub mod bin_expand; -pub mod binned_join; -pub mod binned_sql; -pub mod sweep_line; - -pub use bin_expand::BinExpandExec; -pub use binned_join::BinnedJoinExec; -pub use binned_sql::BinnedSqlExec; -pub use sweep_line::SweepLineJoinExec; diff --git a/crates/giql-datafusion/src/exec/sweep_line.rs b/crates/giql-datafusion/src/exec/sweep_line.rs deleted file mode 100644 index 18b6d06..0000000 --- a/crates/giql-datafusion/src/exec/sweep_line.rs +++ /dev/null @@ -1,628 +0,0 @@ -use std::any::Any; -use std::fmt; -use std::ops::Range; -use std::pin::Pin; -use std::sync::Arc; -use std::future::Future; -use std::task::{Context, Poll}; - -use arrow::array::{ - Array, ArrayRef, Int32Array, Int64Array, RecordBatch, StringArray, - StringViewArray, UInt32Array, -}; -use arrow::compute; -use arrow::compute::kernels::sort::SortOptions; -use arrow::datatypes::SchemaRef; -use datafusion::common::Result; -use datafusion::execution::SendableRecordBatchStream; -use datafusion::physical_expr::expressions::Column; -use datafusion::physical_expr::{ - EquivalenceProperties, LexRequirement, OrderingRequirements, - Partitioning, PhysicalSortRequirement, -}; -use datafusion::physical_plan::execution_plan::{ - Boundedness, EmissionType, -}; -use datafusion::physical_plan::metrics::{ - BaselineMetrics, ExecutionPlanMetricsSet, MetricsSet, -}; -use datafusion::physical_plan::{ - DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, - RecordBatchStream, -}; - -use crate::pattern::IntervalColumns; - -/// Which side of the join to materialize as the build side. -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub enum BuildSide { - Left, - Right, -} - -/// Streaming sweep-line interval join. -/// -/// Materializes the build side (smaller), then streams the probe side -/// batch-by-batch. Each probe batch is swept against the sorted build -/// side per-chromosome, producing output incrementally. -/// -/// Requires both inputs sorted by (chrom, start). -#[derive(Debug)] -pub struct SweepLineJoinExec { - left: Arc, - right: Arc, - left_cols: IntervalColumns, - right_cols: IntervalColumns, - build_side: BuildSide, - schema: SchemaRef, - properties: Arc, - metrics: ExecutionPlanMetricsSet, -} - -impl SweepLineJoinExec { - pub fn new( - left: Arc, - right: Arc, - left_cols: IntervalColumns, - right_cols: IntervalColumns, - schema: SchemaRef, - build_side: BuildSide, - ) -> Self { - let properties = Arc::new(PlanProperties::new( - EquivalenceProperties::new(schema.clone()), - Partitioning::UnknownPartitioning(1), - EmissionType::Incremental, - Boundedness::Bounded, - )); - Self { - left, - right, - left_cols, - right_cols, - build_side, - schema, - properties, - metrics: ExecutionPlanMetricsSet::new(), - } - } - - fn sort_requirement(cols: &IntervalColumns) -> OrderingRequirements { - let lex = LexRequirement::new(vec![ - PhysicalSortRequirement::new( - Arc::new(Column::new(&cols.chrom_col, cols.chrom_idx)), - Some(SortOptions { - descending: false, - nulls_first: false, - }), - ), - PhysicalSortRequirement::new( - Arc::new(Column::new(&cols.start_col, cols.start_idx)), - Some(SortOptions { - descending: false, - nulls_first: false, - }), - ), - ]) - .expect("sort requirement should be non-empty"); - OrderingRequirements::new(lex) - } -} - -impl DisplayAs for SweepLineJoinExec { - fn fmt_as( - &self, - _t: DisplayFormatType, - f: &mut fmt::Formatter<'_>, - ) -> fmt::Result { - write!( - f, - "SweepLineJoinExec: build={:?}", - self.build_side - ) - } -} - -impl ExecutionPlan for SweepLineJoinExec { - fn name(&self) -> &str { - "SweepLineJoinExec" - } - fn as_any(&self) -> &dyn Any { - self - } - fn schema(&self) -> SchemaRef { - self.schema.clone() - } - fn properties(&self) -> &Arc { - &self.properties - } - fn children(&self) -> Vec<&Arc> { - vec![&self.left, &self.right] - } - fn required_input_ordering( - &self, - ) -> Vec> { - vec![ - Some(Self::sort_requirement(&self.left_cols)), - Some(Self::sort_requirement(&self.right_cols)), - ] - } - fn with_new_children( - self: Arc, - children: Vec>, - ) -> Result> { - Ok(Arc::new(SweepLineJoinExec::new( - children[0].clone(), - children[1].clone(), - self.left_cols.clone(), - self.right_cols.clone(), - self.schema.clone(), - self.build_side, - ))) - } - - fn execute( - &self, - _partition: usize, - context: Arc, - ) -> Result { - let (build_plan, probe_plan, build_cols, probe_cols) = - match self.build_side { - BuildSide::Right => ( - self.right.clone(), - self.left.clone(), - self.right_cols.clone(), - self.left_cols.clone(), - ), - BuildSide::Left => ( - self.left.clone(), - self.right.clone(), - self.left_cols.clone(), - self.right_cols.clone(), - ), - }; - - // Spawn build-side collection as a background task. - let build_ctx = context.clone(); - let build_fut = tokio::spawn(async move { - let batches = datafusion::physical_plan::collect( - build_plan, build_ctx, - ) - .await?; - BuildSideData::from_batches(&batches, &build_cols) - }); - - // Open probe-side stream. If the probe plan has multiple - // partitions (e.g. from RepartitionExec), coalesce them - // into a single stream so we see all rows. - use datafusion::physical_plan::coalesce_partitions::CoalescePartitionsExec; - let probe_plan = if probe_plan - .properties() - .partitioning - .partition_count() - > 1 - { - Arc::new(CoalescePartitionsExec::new(probe_plan)) - as Arc - } else { - probe_plan - }; - let probe_stream = probe_plan.execute(0, context)?; - - Ok(Box::pin(SweepLineStream { - state: SweepLineState::WaitBuildSide, - probe_stream, - build_fut: Some(build_fut), - build_data: None, - probe_cols, - build_side: self.build_side, - schema: self.schema.clone(), - metrics: BaselineMetrics::new(&self.metrics, 0), - })) - } - - fn metrics(&self) -> Option { - Some(self.metrics.clone_inner()) - } -} - -// ── Build side data ───────────────────────────────────────────── - -/// Materialized, sorted, and indexed build-side data. -struct BuildSideData { - batch: RecordBatch, - starts: Arc<[i64]>, - ends: Arc<[i64]>, - /// Sorted indices into the batch, ordered by (chrom, start). - sorted_order: Vec, - /// Per-chromosome ranges into sorted_order, keyed by chrom name. - chrom_groups: Vec<(String, Range)>, -} - -impl BuildSideData { - fn from_batches( - batches: &[RecordBatch], - cols: &IntervalColumns, - ) -> Result { - let batch = concat_batches(batches)?; - let chrom_strings = - extract_all_strings(batch.column(cols.chrom_idx))?; - let chrom_ids = strings_to_ids(&chrom_strings); - let starts = as_i64_vec(batch.column(cols.start_idx))?; - let ends = as_i64_vec(batch.column(cols.end_idx))?; - let sorted_order = argsort_by_chrom_start(&chrom_ids, &starts); - // Build groups keyed by chrom name (not ID) so probe batches - // can match regardless of their own ID assignment. - let id_groups = split_by_chrom(&sorted_order, &chrom_ids); - let id_to_name = ids_to_names(&chrom_strings, &chrom_ids); - let chrom_groups: Vec<(String, Range)> = id_groups - .into_iter() - .map(|(id, range)| (id_to_name[&id].clone(), range)) - .collect(); - Ok(Self { batch, starts, ends, sorted_order, chrom_groups }) - } - - /// Find the sorted index range for a given chromosome name. - fn chrom_range(&self, chrom: &str) -> Option> { - self.chrom_groups - .iter() - .find(|(c, _)| c == chrom) - .map(|(_, r)| r.clone()) - } -} - -// ── Stream state machine ──────────────────────────────────────── - -enum SweepLineState { - WaitBuildSide, - FetchProbeBatch, - Completed, -} - -struct SweepLineStream { - state: SweepLineState, - probe_stream: SendableRecordBatchStream, - build_fut: Option>>, - build_data: Option>, - probe_cols: IntervalColumns, - build_side: BuildSide, - schema: SchemaRef, - metrics: BaselineMetrics, -} - -impl RecordBatchStream for SweepLineStream { - fn schema(&self) -> SchemaRef { - self.schema.clone() - } -} - -impl futures::Stream for SweepLineStream { - type Item = Result; - - fn poll_next( - mut self: Pin<&mut Self>, - cx: &mut Context<'_>, - ) -> Poll> { - loop { - match self.state { - SweepLineState::WaitBuildSide => { - // Poll the build-side future. - let fut = self.build_fut.as_mut().unwrap(); - // SAFETY: we only poll this once, and it's - // behind an Option that we take after Ready. - let fut = Pin::new(fut); - match fut.poll(cx) { - Poll::Pending => return Poll::Pending, - Poll::Ready(join_result) => { - self.build_fut = None; - let build_data = join_result - .map_err(|e| { - datafusion::error::DataFusionError::External( - Box::new(e), - ) - })? - ?; - self.build_data = - Some(Arc::new(build_data)); - self.state = - SweepLineState::FetchProbeBatch; - } - } - } - - SweepLineState::FetchProbeBatch => { - // Poll the probe stream for the next batch. - match self - .probe_stream - .as_mut() - .poll_next(cx) - { - Poll::Pending => return Poll::Pending, - Poll::Ready(None) => { - self.state = SweepLineState::Completed; - return Poll::Ready(None); - } - Poll::Ready(Some(Err(e))) => { - return Poll::Ready(Some(Err(e))); - } - Poll::Ready(Some(Ok(probe_batch))) => { - if probe_batch.num_rows() == 0 { - continue; // skip empty batches - } - let build = self - .build_data - .as_ref() - .unwrap(); - let result = process_probe_batch( - &self.schema, - build, - &probe_batch, - &self.probe_cols, - self.build_side, - ); - if let Ok(ref batch) = result { - self.metrics - .record_output(batch.num_rows()); - if batch.num_rows() == 0 { - continue; - } - } - return Poll::Ready(Some(result)); - } - } - } - - SweepLineState::Completed => { - return Poll::Ready(None); - } - } - } - } -} - -// ── Per-batch processing ──────────────────────────────────────── - -/// Sweep a single probe batch against the build side, producing -/// matched output rows. -fn process_probe_batch( - schema: &SchemaRef, - build: &BuildSideData, - probe_batch: &RecordBatch, - probe_cols: &IntervalColumns, - build_side: BuildSide, -) -> Result { - let probe_chrom_strings = - extract_all_strings(probe_batch.column(probe_cols.chrom_idx))?; - let probe_chrom_ids = strings_to_ids(&probe_chrom_strings); - let probe_starts = - as_i64_vec(probe_batch.column(probe_cols.start_idx))?; - let probe_ends = - as_i64_vec(probe_batch.column(probe_cols.end_idx))?; - - let mut match_build: Vec = Vec::new(); - let mut match_probe: Vec = Vec::new(); - - // Group probe rows by chromosome, match by name against build - let probe_order = - argsort_by_chrom_start(&probe_chrom_ids, &probe_starts); - let probe_id_groups = - split_by_chrom(&probe_order, &probe_chrom_ids); - let id_to_name = ids_to_names(&probe_chrom_strings, &probe_chrom_ids); - - for (probe_cid, p_range) in &probe_id_groups { - let chrom_name = &id_to_name[probe_cid]; - let b_range = match build.chrom_range(chrom_name) { - Some(r) => r, - None => continue, - }; - - let p_idx = &probe_order[p_range.clone()]; - let b_idx = &build.sorted_order[b_range]; - - let (bl, pl) = sweep_chrom( - p_idx, - &probe_starts, - &probe_ends, - b_idx, - &build.starts, - &build.ends, - ); - - match_probe.extend_from_slice(&pl); - match_build.extend_from_slice(&bl); - } - - if match_probe.is_empty() { - return Ok(RecordBatch::new_empty(schema.clone())); - } - - // Build output: column order depends on which side is build/probe. - // Output must be (left_cols..., right_cols...). - let (left_batch, right_batch, left_idx, right_idx) = - match build_side { - BuildSide::Left => ( - &build.batch, - probe_batch, - &match_build, - &match_probe, - ), - BuildSide::Right => ( - probe_batch, - &build.batch, - &match_probe, - &match_build, - ), - }; - - build_output_take(schema, left_batch, right_batch, left_idx, right_idx) -} - -// ── Core sweep ────────────────────────────────────────────────── - -/// Sweep probe intervals against build intervals for one chromosome. -/// Returns (build_matches, probe_matches). -fn sweep_chrom( - probe_indices: &[u32], - probe_starts: &[i64], - probe_ends: &[i64], - build_indices: &[u32], - build_starts: &[i64], - build_ends: &[i64], -) -> (Vec, Vec) { - let mut match_build = Vec::new(); - let mut match_probe = Vec::new(); - let mut b_cursor = 0usize; - let mut active: Vec = Vec::new(); - - for &pi in probe_indices { - let p_start = probe_starts[pi as usize]; - let p_end = probe_ends[pi as usize]; - - // Add build intervals with start < probe_end - while b_cursor < build_indices.len() { - let bi = build_indices[b_cursor] as usize; - if build_starts[bi] >= p_end { - break; - } - active.push(b_cursor); - b_cursor += 1; - } - - // Remove expired (end <= probe_start) - active.retain(|&pos| { - build_ends[build_indices[pos] as usize] > p_start - }); - - // Emit overlapping pairs - for &pos in &active { - let bi = build_indices[pos]; - if build_starts[bi as usize] < p_end { - match_build.push(bi); - match_probe.push(pi); - } - } - } - - (match_build, match_probe) -} - -// ── Helpers ───────────────────────────────────────────────────── - -/// Assign dense u32 IDs to strings (sorted order). -fn strings_to_ids(strings: &[String]) -> Vec { - let mut unique: Vec = Vec::new(); - for s in strings { - if !unique.contains(s) { - unique.push(s.clone()); - } - } - unique.sort(); - let map: std::collections::HashMap<&str, u32> = unique - .iter() - .enumerate() - .map(|(i, s)| (s.as_str(), i as u32)) - .collect(); - strings.iter().map(|s| map[s.as_str()]).collect() -} - -/// Build reverse mapping from u32 ID → chromosome name. -fn ids_to_names( - strings: &[String], - ids: &[u32], -) -> std::collections::HashMap { - let mut map = std::collections::HashMap::new(); - for (s, &id) in strings.iter().zip(ids.iter()) { - map.entry(id).or_insert_with(|| s.clone()); - } - map -} - -fn extract_all_strings(col: &ArrayRef) -> Result> { - let n = col.len(); - if let Some(arr) = col.as_any().downcast_ref::() { - return Ok((0..n).map(|i| arr.value(i).to_string()).collect()); - } - if let Some(arr) = col.as_any().downcast_ref::() { - return Ok((0..n).map(|i| arr.value(i).to_string()).collect()); - } - Err(datafusion::error::DataFusionError::Internal( - "Unsupported string type".to_string(), - )) -} - -fn as_i64_vec(col: &ArrayRef) -> Result> { - if let Some(arr) = col.as_any().downcast_ref::() { - return Ok(arr.values().to_vec().into()); - } - if let Some(arr) = col.as_any().downcast_ref::() { - return Ok(arr.values().iter().map(|&v| v as i64).collect::>().into()); - } - Err(datafusion::error::DataFusionError::Internal( - "Column is not Int32 or Int64".to_string(), - )) -} - -fn argsort_by_chrom_start(chrom_ids: &[u32], starts: &[i64]) -> Vec { - let mut idx: Vec = (0..chrom_ids.len() as u32).collect(); - idx.sort_unstable_by(|&a, &b| { - let (a, b) = (a as usize, b as usize); - chrom_ids[a].cmp(&chrom_ids[b]).then(starts[a].cmp(&starts[b])) - }); - idx -} - -fn split_by_chrom( - sorted: &[u32], - chrom_ids: &[u32], -) -> Vec<(u32, Range)> { - if sorted.is_empty() { - return vec![]; - } - let mut groups = Vec::new(); - let mut start = 0; - let mut cur = chrom_ids[sorted[0] as usize]; - for i in 1..sorted.len() { - let c = chrom_ids[sorted[i] as usize]; - if c != cur { - groups.push((cur, start..i)); - start = i; - cur = c; - } - } - groups.push((cur, start..sorted.len())); - groups -} - -fn concat_batches(batches: &[RecordBatch]) -> Result { - if batches.is_empty() { - return Err(datafusion::error::DataFusionError::Internal( - "No batches".to_string(), - )); - } - if batches.len() == 1 { - return Ok(batches[0].clone()); - } - Ok(compute::concat_batches(&batches[0].schema(), batches)?) -} - -fn build_output_take( - schema: &SchemaRef, - left: &RecordBatch, - right: &RecordBatch, - left_idx: &[u32], - right_idx: &[u32], -) -> Result { - if left_idx.is_empty() { - return Ok(RecordBatch::new_empty(schema.clone())); - } - let li = UInt32Array::from(left_idx.to_vec()); - let ri = UInt32Array::from(right_idx.to_vec()); - let mut cols: Vec = - Vec::with_capacity(left.num_columns() + right.num_columns()); - for c in 0..left.num_columns() { - cols.push(compute::take(left.column(c).as_ref(), &li, None)?); - } - for c in 0..right.num_columns() { - cols.push(compute::take(right.column(c).as_ref(), &ri, None)?); - } - Ok(RecordBatch::try_new(schema.clone(), cols)?) -} diff --git a/crates/giql-datafusion/src/lib.rs b/crates/giql-datafusion/src/lib.rs index 054ffc4..f17d5c2 100644 --- a/crates/giql-datafusion/src/lib.rs +++ b/crates/giql-datafusion/src/lib.rs @@ -1,116 +1,130 @@ //! DataFusion optimizer for genomic interval (INTERSECTS) joins. //! -//! This crate provides a [`PhysicalOptimizerRule`] that reads Parquet -//! metadata and lightweight sampling to choose between sweep-line and -//! binned equi-join algorithms for interval overlap joins. +//! This crate provides a logical [`OptimizerRule`] that detects +//! `giql_intersects()` function calls in join filters and rewrites +//! them into binned equi-joins using UNNEST. Bin size is chosen +//! adaptively from table statistics when available. +//! +//! The `giql_intersects` function is a placeholder UDF emitted by the +//! GIQL transpiler's `"datafusion"` dialect. It preserves INTERSECTS +//! semantics through the SQL layer so the optimizer can match on it +//! directly, without heuristic pattern detection. //! //! # Usage //! //! ```rust,no_run //! use datafusion::execution::SessionStateBuilder; //! use datafusion::prelude::*; -//! use giql_datafusion::{IntersectsOptimizerConfig, register_optimizer}; +//! use giql_datafusion::register_optimizer; //! -//! let config = IntersectsOptimizerConfig::default(); //! let state = SessionStateBuilder::new() //! .with_default_features() //! .build(); -//! let state = register_optimizer(state, config); +//! let state = register_optimizer(state); //! let ctx = SessionContext::from(state); //! ``` -pub mod cost; -pub mod exec; pub mod logical_rule; -pub mod optimizer; -pub mod pattern; -pub mod pruning; -pub mod stats; -pub use cost::JoinStrategy; pub use logical_rule::IntersectsLogicalRule; -pub use optimizer::IntersectsOptimizerRule; +use std::sync::Arc; + +use datafusion::common::Result; use datafusion::execution::SessionState; +use datafusion::logical_expr::{ + ColumnarValue, ScalarFunctionArgs, ScalarUDF, ScalarUDFImpl, + Signature, TypeSignature, Volatility, +}; use datafusion::optimizer::OptimizerRule; -use datafusion::physical_optimizer::PhysicalOptimizerRule; -use std::sync::Arc; -/// Configuration for the INTERSECTS join optimizer. -#[derive(Debug, Clone)] -pub struct IntersectsOptimizerConfig { - /// Threshold for p99/median width ratio. Above this, sweep line is - /// chosen to avoid binning replication blowup on wide intervals. - pub p99_median_threshold: f64, - - /// Threshold for coefficient of variation. Above this, sweep line - /// is chosen because no single bin size works well. - pub cv_threshold: f64, - - /// Maximum number of row groups to sample for width distribution. - pub max_sample_row_groups: usize, - - /// Enable the logical optimizer rule that rewrites interval - /// overlap joins to UNNEST-based binned equi-joins. When false, - /// only the physical sweep-line optimizer is active. Enabled by - /// default. - pub enable_logical_rule: bool, +// ── Placeholder UDF ───────────────────────────────────────────── + +/// Placeholder `giql_intersects(start_a, end_a, start_b, end_b)` UDF. +/// +/// Exists only so DataFusion's SQL parser accepts the function call. +/// The logical optimizer rule rewrites it away before execution. +#[derive(Debug, Hash, PartialEq, Eq)] +struct GiqlIntersectsUdf { + signature: Signature, } -impl Default for IntersectsOptimizerConfig { - fn default() -> Self { +impl GiqlIntersectsUdf { + fn new() -> Self { Self { - p99_median_threshold: 10.0, - cv_threshold: 1.5, - max_sample_row_groups: 3, - enable_logical_rule: true, + signature: Signature::new( + TypeSignature::Any(4), + Volatility::Immutable, + ), } } } -/// Build a [`SessionState`] with the INTERSECTS optimizer rules. -/// -/// The physical rule detects interval overlap joins and replaces them -/// with sweep-line execution plans for heavy-tailed distributions, -/// deferring to DataFusion's default join for uniform data. +impl ScalarUDFImpl for GiqlIntersectsUdf { + fn as_any(&self) -> &dyn std::any::Any { + self + } + + fn name(&self) -> &str { + "giql_intersects" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type( + &self, + _arg_types: &[arrow::datatypes::DataType], + ) -> Result { + Ok(arrow::datatypes::DataType::Boolean) + } + + fn invoke_with_args( + &self, + _args: ScalarFunctionArgs, + ) -> Result { + Err(datafusion::error::DataFusionError::Internal( + "giql_intersects should be rewritten by the logical \ + optimizer rule — was the IntersectsLogicalRule registered?" + .into(), + )) + } +} + +/// Create the placeholder `giql_intersects` scalar UDF. +pub fn giql_intersects_udf() -> ScalarUDF { + ScalarUDF::from(GiqlIntersectsUdf::new()) +} + +// ── Registration ──────────────────────────────────────────────── + +/// Build a [`SessionState`] with the INTERSECTS logical optimizer +/// rule and the `giql_intersects` placeholder UDF. /// -/// The logical rule (experimental, disabled by default) rewrites -/// interval overlap joins to UNNEST-based binned equi-joins at the -/// logical level, enabling DataFusion's native parallel execution. -/// Enable by setting `enable_logical_rule = true` in the config. -pub fn register_optimizer( - state: SessionState, - config: IntersectsOptimizerConfig, -) -> SessionState { +/// The logical rule detects `giql_intersects()` calls in join +/// filters and rewrites them into binned equi-joins with adaptive +/// bin sizing from table statistics. +pub fn register_optimizer(state: SessionState) -> SessionState { use datafusion::execution::SessionStateBuilder; - // Physical rule: sweep-line for heavy-tailed distributions - let physical_rule: Arc = - Arc::new(IntersectsOptimizerRule::new(config.clone())); - - let mut physical_rules: Vec< - Arc, - > = state.physical_optimizers().to_vec(); - physical_rules.push(physical_rule); - - let builder = if config.enable_logical_rule { - let logical_rule: Arc = - Arc::new(IntersectsLogicalRule::new(config)); - - let mut logical_rules: Vec< - Arc, - > = state.optimizers().to_vec(); - logical_rules.push(logical_rule); - - SessionStateBuilder::new_from_existing(state) - .with_optimizer_rules(logical_rules) - .with_physical_optimizer_rules(physical_rules) - } else { - SessionStateBuilder::new_from_existing(state) - .with_physical_optimizer_rules(physical_rules) - }; - - builder.build() + let logical_rule: Arc = + Arc::new(IntersectsLogicalRule::new()); + + let mut logical_rules: Vec> = + state.optimizers().to_vec(); + logical_rules.push(logical_rule); + + let udf = Arc::new(giql_intersects_udf()); + + let mut scalar_fns: Vec> = + state.scalar_functions().values().cloned().collect(); + scalar_fns.push(udf); + + SessionStateBuilder::new_from_existing(state) + .with_optimizer_rules(logical_rules) + .with_scalar_functions(scalar_fns) + .build() } #[cfg(test)] @@ -118,67 +132,24 @@ mod tests { use super::*; #[test] - fn test_default_config() { - let config = IntersectsOptimizerConfig::default(); - assert!((config.p99_median_threshold - 10.0).abs() < f64::EPSILON); - assert!((config.cv_threshold - 1.5).abs() < f64::EPSILON); - assert_eq!(config.max_sample_row_groups, 3); - } - - #[test] - fn test_custom_config_used_by_cost_model() { - let config = IntersectsOptimizerConfig { - p99_median_threshold: 5.0, - cv_threshold: 1.0, - max_sample_row_groups: 1, - enable_logical_rule: false, - }; - let model = cost::CostModel::new(&config); - - // With p99/median = 6.0 > 5.0 (custom threshold), should - // short-circuit to sweep line even though default threshold - // would not trigger. - let stats = stats::IntervalStats { - row_count: 100_000, - domain_min: 0, - domain_max: 1_000_000, - is_sorted_by_start: false, - row_group_bounds: vec![], - width: stats::WidthStats { - median: 100.0, - mean: 120.0, - p95: 500.0, - p99: 600.0, - cv: 0.5, - p99_median_ratio: 6.0, - }, - }; - - match model.decide(&stats, &stats) { - JoinStrategy::SweepLine { .. } => {} - other => panic!( - "Expected SweepLine with custom threshold, got {:?}", - other - ), - } - } - - #[test] - fn test_register_optimizer_adds_rule() { + fn test_register_optimizer_adds_rule_and_udf() { use datafusion::execution::SessionStateBuilder; let state = SessionStateBuilder::new() .with_default_features() .build(); - let n_before = state.physical_optimizers().len(); + let n_before = state.optimizers().len(); - let config = IntersectsOptimizerConfig::default(); - let state = register_optimizer(state, config); - let n_after = state.physical_optimizers().len(); + let state = register_optimizer(state); - assert_eq!(n_after, n_before + 1); + // Logical rule was added + assert_eq!(state.optimizers().len(), n_before + 1); + let last_rule = state.optimizers().last().unwrap(); + assert_eq!(last_rule.name(), "intersects_logical_binned"); - let last_rule = state.physical_optimizers().last().unwrap(); - assert_eq!(last_rule.name(), "intersects_optimizer"); + // UDF was registered + assert!( + state.scalar_functions().contains_key("giql_intersects") + ); } } diff --git a/crates/giql-datafusion/src/logical_rule.rs b/crates/giql-datafusion/src/logical_rule.rs index 19062d5..60fe1b1 100644 --- a/crates/giql-datafusion/src/logical_rule.rs +++ b/crates/giql-datafusion/src/logical_rule.rs @@ -1,8 +1,5 @@ -use std::sync::Arc; - use datafusion::common::tree_node::Transformed; use datafusion::common::{Column, Result, ScalarValue}; -use datafusion::datasource::listing::ListingTable; use datafusion::datasource::source_as_provider; use datafusion::logical_expr::expr::ScalarFunction; use datafusion::logical_expr::{ @@ -12,29 +9,30 @@ use datafusion::logical_expr::{ use datafusion::optimizer::{OptimizerConfig, OptimizerRule}; use datafusion::prelude::*; -use crate::IntersectsOptimizerConfig; - /// Logical optimizer rule that rewrites interval overlap joins into /// binned equi-joins using UNNEST. /// -/// Detects: -/// `JOIN ON a.chrom = b.chrom WHERE a.start < b.end AND a.end > b.start` +/// Detects `giql_intersects(start_a, end_a, start_b, end_b)` function +/// calls in join filters (emitted by the GIQL transpiler's +/// `"datafusion"` dialect) and rewrites them to: /// -/// Rewrites to: -/// `SELECT DISTINCT ... FROM Unnest(a + bins) JOIN Unnest(b + bins) +/// `SELECT ... FROM Unnest(a + bins) JOIN Unnest(b + bins) /// ON chrom = chrom AND bin = bin WHERE start < end AND end > start` /// -/// DataFusion handles UNNEST, hash join, and DISTINCT natively with -/// full parallelism. The physical optimizer rule handles sweep-line -/// for heavy-tailed distributions; this rule handles the binned case. +/// DataFusion handles UNNEST, hash join, and dedup natively with +/// full parallelism. #[derive(Debug)] -pub struct IntersectsLogicalRule { - config: IntersectsOptimizerConfig, -} +pub struct IntersectsLogicalRule; impl IntersectsLogicalRule { - pub fn new(config: IntersectsOptimizerConfig) -> Self { - Self { config } + pub fn new() -> Self { + Self + } +} + +impl Default for IntersectsLogicalRule { + fn default() -> Self { + Self::new() } } @@ -76,12 +74,9 @@ impl OptimizerRule for IntersectsLogicalRule { return Ok(Transformed::no(plan)); } - // Detect interval overlap pattern in the filter - let left_schema = join.left.schema(); + // Detect giql_intersects() function call in the filter let overlap = match &join.filter { - Some(filter) => { - detect_overlap_columns(filter, &left_schema) - } + Some(filter) => detect_giql_intersects(filter), None => None, }; @@ -89,14 +84,11 @@ impl OptimizerRule for IntersectsLogicalRule { return Ok(Transformed::no(plan)); }; - // Get stats from TableScan children to decide strategy. - // If stats aren't available (common for ListingTable without - // collect_statistics), default to binned with a reasonable - // bin size. The physical optimizer rule will still catch - // heavy-tailed distributions via Parquet metadata sampling - // and replace with sweep-line if needed. - let left_stats = get_table_stats(&join.left); - let right_stats = get_table_stats(&join.right); + // Get stats from TableScan children for adaptive bin sizing. + let left_stats = + get_table_stats(&join.left, &start_a.name, &end_a.name); + let right_stats = + get_table_stats(&join.right, &start_b.name, &end_b.name); let bin_size = choose_bin_size(&left_stats, &right_stats); @@ -105,84 +97,60 @@ impl OptimizerRule for IntersectsLogicalRule { bin_size={bin_size}" ); - let rewritten = - rewrite_to_binned(join, bin_size, &start_a, &start_b)?; + // Replace giql_intersects() with real overlap predicates + // before building the binned join, since the placeholder + // UDF cannot execute. + let rewritten_filter = join.filter.as_ref().map(|f| { + replace_giql_intersects( + f, &start_a, &end_a, &start_b, &end_b, + ) + }); + + let rewritten = rewrite_to_binned( + join, + bin_size, + &start_a, + &end_a, + &start_b, + &end_b, + rewritten_filter.as_ref(), + )?; Ok(Transformed::yes(rewritten)) } } // ── Pattern detection ─────────────────────────────────────────── -/// Detect interval overlap predicates in a filter expression. -/// -/// Looks for `col_a < col_b AND col_c > col_d` where: -/// - One comparison has a "start" col on one side and "end" on the other -/// - The other has "end" on one side and "start" on the other -/// -/// Returns `(start_a, end_a, start_b, end_b)` column names. -/// Detect interval overlap predicates in a filter expression. +/// Detect `giql_intersects(start_a, end_a, start_b, end_b)` in a +/// filter expression. Searches through AND-combined predicates. /// -/// Checks the join's left child schema to determine which columns -/// belong to which side — no heuristics based on table name. -fn detect_overlap_columns( +/// Returns `(start_a, end_a, start_b, end_b)` column references. +fn detect_giql_intersects( expr: &Expr, - left_schema: &datafusion::common::DFSchemaRef, -) -> Option<(Column, Column, Column, Column)> { - let Expr::BinaryExpr(BinaryExpr { - left, - op: Operator::And, - right, - }) = expr - else { - return None; - }; - - try_extract_overlap(left, right, left_schema) - .or_else(|| try_extract_overlap(right, left, left_schema)) -} - -fn try_extract_overlap( - pred_a: &Expr, - pred_b: &Expr, - left_schema: &datafusion::common::DFSchemaRef, ) -> Option<(Column, Column, Column, Column)> { - let (lt_left, lt_right) = extract_comparison(pred_a, Operator::Lt)?; - let (gt_left, gt_right) = extract_comparison(pred_b, Operator::Gt)?; - - let all = [<_left, <_right, >_left, >_right]; - - let is_left = |c: &Column| column_in_schema(c, left_schema); - - let left_start = all.iter().find(|c| is_start(&c.name) && is_left(c)); - let left_end = all.iter().find(|c| is_end(&c.name) && is_left(c)); - let right_start = all.iter().find(|c| is_start(&c.name) && !is_left(c)); - let right_end = all.iter().find(|c| is_end(&c.name) && !is_left(c)); - - Some(( - (*left_start?).clone(), - (*left_end?).clone(), - (*right_start?).clone(), - (*right_end?).clone(), - )) -} - -fn extract_comparison( - expr: &Expr, - expected_op: Operator, -) -> Option<(Column, Column)> { - let Expr::BinaryExpr(BinaryExpr { left, op, right }) = expr else { - return None; - }; - if *op != expected_op { - return None; + match expr { + Expr::ScalarFunction(func) + if func.name() == "giql_intersects" + && func.args.len() == 4 => + { + let start_a = extract_column(&func.args[0])?; + let end_a = extract_column(&func.args[1])?; + let start_b = extract_column(&func.args[2])?; + let end_b = extract_column(&func.args[3])?; + Some((start_a, end_a, start_b, end_b)) + } + Expr::BinaryExpr(BinaryExpr { + left, + op: Operator::And, + right, + }) => detect_giql_intersects(left) + .or_else(|| detect_giql_intersects(right)), + _ => None, } - let left_col = extract_column(left)?; - let right_col = extract_column(right)?; - Some((left_col, right_col)) } /// Extract a Column from an Expr, handling TryCast/Cast wrappers -/// that DataFusion may insert. +/// that DataFusion may insert during type coercion. fn extract_column(expr: &Expr) -> Option { match expr { Expr::Column(c) => Some(c.clone()), @@ -192,131 +160,89 @@ fn extract_column(expr: &Expr) -> Option { } } -fn is_start(name: &str) -> bool { - let n = name.to_lowercase(); - n == "start" || n == "chromstart" || n == "pos_start" || n == "begin" -} - -fn is_end(name: &str) -> bool { - let n = name.to_lowercase(); - n == "end" || n == "chromend" || n == "pos_end" || n == "stop" -} - -/// Check whether a column belongs to a schema by matching its -/// qualified name against the schema's columns. -fn column_in_schema( - col: &Column, - schema: &datafusion::common::DFSchemaRef, -) -> bool { - schema.has_column(col) +/// Replace `giql_intersects(start_a, end_a, start_b, end_b)` in an +/// expression tree with `start_a < end_b AND end_a > start_b`. +fn replace_giql_intersects( + expr: &Expr, + start_a: &Column, + end_a: &Column, + start_b: &Column, + end_b: &Column, +) -> Expr { + match expr { + Expr::ScalarFunction(func) + if func.name() == "giql_intersects" => + { + // start_a < end_b AND end_a > start_b + Expr::BinaryExpr(BinaryExpr { + left: Box::new(Expr::BinaryExpr(BinaryExpr { + left: Box::new(Expr::Column(start_a.clone())), + op: Operator::Lt, + right: Box::new(Expr::Column(end_b.clone())), + })), + op: Operator::And, + right: Box::new(Expr::BinaryExpr(BinaryExpr { + left: Box::new(Expr::Column(end_a.clone())), + op: Operator::Gt, + right: Box::new(Expr::Column(start_b.clone())), + })), + }) + } + Expr::BinaryExpr(BinaryExpr { left, op, right }) => { + Expr::BinaryExpr(BinaryExpr { + left: Box::new(replace_giql_intersects( + left, start_a, end_a, start_b, end_b, + )), + op: *op, + right: Box::new(replace_giql_intersects( + right, start_a, end_a, start_b, end_b, + )), + }) + } + other => other.clone(), + } } // ── Stats collection ──────────────────────────────────────────── struct LogicalStats { + #[allow(dead_code)] row_count: Option, + #[allow(dead_code)] start_min: Option, start_max: Option, + #[allow(dead_code)] end_min: Option, end_max: Option, } -fn get_table_stats(plan: &LogicalPlan) -> Option { +fn get_table_stats( + plan: &LogicalPlan, + start_col_name: &str, + end_col_name: &str, +) -> Option { match plan { LogicalPlan::TableScan(ts) => { - let provider = match source_as_provider(&ts.source) { - Ok(p) => p, - Err(e) => { - log::debug!( - " get_table_stats: source_as_provider failed: {e}" - ); - return None; - } - }; - - // Try TableProvider::statistics() first - if let Some(stats) = provider.statistics() { - return stats_to_logical(&stats, &ts.source.schema()); - } - - // Fall back to reading Parquet metadata directly - let listing = match provider - .as_any() - .downcast_ref::() - { - Some(lt) => lt, - None => { - log::debug!( - " get_table_stats: not a ListingTable: {}", - std::any::type_name_of_val(provider.as_ref()), - ); - return None; - } - }; - let paths = listing.table_paths(); - let path = paths.first()?; - let path_str = path.as_str(); - - // ListingTableUrl stores file:// URLs; extract the - // filesystem path - let fs_path = if let Some(p) = path_str.strip_prefix("file://") { - std::path::PathBuf::from(p) - } else { - std::path::PathBuf::from(format!("/{path_str}")) - }; - - let schema = ts.source.schema(); - let start_col = schema - .fields() - .iter() - .find(|f| is_start(f.name()))? - .name() - .as_str(); - let end_col = schema - .fields() - .iter() - .find(|f| is_end(f.name()))? - .name() - .as_str(); - - // Read Parquet metadata (file footer only — fast) - let meta = crate::stats::metadata::collect_metadata( - &fs_path, start_col, end_col, + let provider = source_as_provider(&ts.source).ok()?; + let stats = provider.statistics()?; + stats_to_logical( + &stats, + &ts.source.schema(), + start_col_name, + end_col_name, ) - .ok()?; - - // Aggregate per-row-group bounds - let start_min = meta.row_group_bounds.iter() - .map(|rg| rg.min_start) - .min(); - let start_max = meta.row_group_bounds.iter() - .map(|rg| rg.max_start) - .max(); - let end_min = meta.row_group_bounds.iter() - .map(|rg| rg.min_end) - .min(); - let end_max = meta.row_group_bounds.iter() - .map(|rg| rg.max_end) - .max(); - - Some(LogicalStats { - row_count: Some(meta.total_rows), - start_min, - start_max, - end_min, - end_max, - }) } - _ => plan - .inputs() - .first() - .and_then(|child| get_table_stats(child)), + _ => plan.inputs().first().and_then(|child| { + get_table_stats(child, start_col_name, end_col_name) + }), } } fn stats_to_logical( stats: &datafusion::common::Statistics, schema: &arrow::datatypes::SchemaRef, + start_col_name: &str, + end_col_name: &str, ) -> Option { let row_count = match stats.num_rows { datafusion::common::stats::Precision::Exact(n) => Some(n), @@ -326,11 +252,11 @@ fn stats_to_logical( let start_idx = schema .fields() .iter() - .position(|f| is_start(f.name()))?; + .position(|f| f.name() == start_col_name)?; let end_idx = schema .fields() .iter() - .position(|f| is_end(f.name()))?; + .position(|f| f.name() == end_col_name)?; let col_stats = &stats.column_statistics; let start_stats = col_stats.get(start_idx)?; let end_stats = col_stats.get(end_idx)?; @@ -362,15 +288,11 @@ fn scalar_to_i64( /// Default bin size when stats are unavailable. const DEFAULT_BIN_SIZE: usize = 10_000; -/// Choose a bin size from Parquet metadata. +/// Choose a bin size from table statistics. /// /// The width signal `max(end) - max(start)` approximates the width /// of the widest intervals. We use this as the bin size so that most /// intervals fit in a single bin (replication factor ≈ 1). -/// -/// Always returns `Some(bin_size)` — binned joins are correct for -/// all distributions, and DataFusion's native UNNEST + hash join is -/// fast enough that adaptive bin sizing is the only knob needed. fn choose_bin_size( left: &Option, right: &Option, @@ -404,7 +326,8 @@ fn choose_bin_size( } (None, None) => { log::debug!( - "INTERSECTS logical: using default bin_size={DEFAULT_BIN_SIZE}" + "INTERSECTS logical: using default \ + bin_size={DEFAULT_BIN_SIZE}" ); DEFAULT_BIN_SIZE } @@ -430,7 +353,10 @@ fn rewrite_to_binned( join: &Join, bin_size: usize, start_a: &Column, + end_a: &Column, start_b: &Column, + end_b: &Column, + rewritten_filter: Option<&Expr>, ) -> Result { let bs = bin_size as i64; @@ -445,12 +371,16 @@ fn rewrite_to_binned( "__giql_bins_l", bs, &left_alias, + start_a, + end_a, )?; let right_expanded = expand_with_bins( (*join.right).clone(), "__giql_bins_r", bs, &right_alias, + start_b, + end_b, )?; // Equi-keys: original keys re-qualified with the aliases + @@ -483,22 +413,20 @@ fn rewrite_to_binned( } }) .collect(); - left_keys.push(col(format!( - "{left_alias}.__giql_bins_l" - ))); - right_keys.push(col(format!( - "{right_alias}.__giql_bins_r" - ))); - - // Build the join with the original filter and extra bin equi-keys. - // Wrap in a subquery alias to isolate the schema, then project - // away the bin columns and add DISTINCT. + left_keys + .push(col(format!("{left_alias}.__giql_bins_l"))); + right_keys + .push(col(format!("{right_alias}.__giql_bins_r"))); + + // Build the join with the rewritten filter (giql_intersects + // replaced with real overlap predicates) and extra bin + // equi-keys. let joined = LogicalPlanBuilder::from(left_expanded) .join_with_expr_keys( right_expanded, JoinType::Inner, (left_keys, right_keys), - join.filter.clone(), + rewritten_filter.cloned(), )? .build()?; @@ -506,38 +434,36 @@ fn rewrite_to_binned( // multi-bin matches. For each pair, only emit from the bin // that equals the GREATER of the two intervals' first bins. // This makes DISTINCT unnecessary. - // - // We use GREATEST(left_first_bin, right_first_bin) but since - // DataFusion doesn't have GREATEST, we use: - // CASE WHEN left_first_bin >= right_first_bin - // THEN left_first_bin - // ELSE right_first_bin END - // - // Actually, we use the simpler approach: just keep the row - // where __giql_bins equals the max of (start_a/B, start_b/B). - // Since we already have the start columns, we can compute this. let left_first_bin = cast( - Expr::Column(start_a.clone()), + Expr::Column(Column::new( + Some(left_alias.clone()), + &start_a.name, + )), arrow::datatypes::DataType::Int64, ) / lit(bs); let right_first_bin = cast( - Expr::Column(start_b.clone()), + Expr::Column(Column::new( + Some(right_alias.clone()), + &start_b.name, + )), arrow::datatypes::DataType::Int64, ) / lit(bs); - // canonical_bin = CASE WHEN l_fb >= r_fb THEN l_fb ELSE r_fb END - let canonical_bin = Expr::Case(datafusion::logical_expr::expr::Case { - expr: None, - when_then_expr: vec![( - Box::new(left_first_bin.clone().gt_eq(right_first_bin.clone())), - Box::new(left_first_bin), - )], - else_expr: Some(Box::new(right_first_bin)), - }); - - // We need the bins column from the left side. After the join - // with aliases, the left bin column is qualified. - let bins_col = col(format!("{left_alias}.__giql_bins_l")); + // canonical_bin = CASE WHEN l_fb >= r_fb THEN l_fb ELSE r_fb + let canonical_bin = + Expr::Case(datafusion::logical_expr::expr::Case { + expr: None, + when_then_expr: vec![( + Box::new( + left_first_bin.clone().gt_eq(right_first_bin.clone()), + ), + Box::new(left_first_bin), + )], + else_expr: Some(Box::new(right_first_bin)), + }); + + let bins_col = + col(format!("{left_alias}.__giql_bins_l")); let dedup_filter = bins_col.eq(canonical_bin); let filtered = LogicalPlanBuilder::from(joined) @@ -568,44 +494,28 @@ fn expand_with_bins( bin_col_name: &str, bin_size: i64, table_alias: &str, + start_col: &Column, + end_col: &Column, ) -> Result { let schema = input.schema().clone(); - // Find start and end columns - let start_col = schema - .columns() - .into_iter() - .find(|c| is_start(&c.name)) - .ok_or_else(|| { - datafusion::error::DataFusionError::Plan( - "No start column found".to_string(), - ) - })?; - let end_col = schema - .columns() - .into_iter() - .find(|c| is_end(&c.name)) - .ok_or_else(|| { - datafusion::error::DataFusionError::Plan( - "No end column found".to_string(), - ) - })?; - // Cast start/end to Int64 first, then compute bin boundaries: - // range(CAST(start AS BIGINT) / B, (CAST(end AS BIGINT) - 1) / B + 1) + // range(CAST(start AS BIGINT) / B, (CAST(end AS BIGINT) - 1) + // / B + 1) let start_i64 = cast( - Expr::Column(start_col), + Expr::Column(start_col.clone()), arrow::datatypes::DataType::Int64, ); let end_i64 = cast( - Expr::Column(end_col), + Expr::Column(end_col.clone()), arrow::datatypes::DataType::Int64, ); let start_bin = start_i64 / lit(bin_size); - let end_bin = (end_i64 - lit(1i64)) / lit(bin_size) + lit(1i64); + let end_bin = + (end_i64 - lit(1i64)) / lit(bin_size) + lit(1i64); - // Build: SELECT *, range(start_bin, end_bin) AS __giql_bins FROM input - // Then UNNEST(__giql_bins) + // Build: SELECT *, range(start_bin, end_bin) AS __giql_bins + // FROM input. Then UNNEST(__giql_bins) let range_expr = Expr::ScalarFunction(ScalarFunction::new_udf( datafusion::functions_nested::range::range_udf(), diff --git a/crates/giql-datafusion/src/optimizer.rs b/crates/giql-datafusion/src/optimizer.rs deleted file mode 100644 index 58a589e..0000000 --- a/crates/giql-datafusion/src/optimizer.rs +++ /dev/null @@ -1,179 +0,0 @@ -use std::sync::Arc; - -use arrow::datatypes::{DataType, Field}; -use datafusion::common::Result; -use datafusion::config::ConfigOptions; -use datafusion::physical_optimizer::PhysicalOptimizerRule; -use datafusion::physical_plan::ExecutionPlan; - -use crate::cost::{CostModel, JoinStrategy, SmallSide}; -use crate::exec::sweep_line::BuildSide; -use crate::exec::SweepLineJoinExec; -use crate::pattern::{detect_interval_join, IntervalJoinMatch}; -use crate::stats; -use crate::IntersectsOptimizerConfig; - -/// Physical optimizer rule that detects interval overlap joins and -/// replaces them with optimized execution plans. -/// -/// The rule reads Parquet metadata and performs lightweight sampling to -/// choose between sweep-line and binned equi-join algorithms based on -/// the interval width distribution. -/// -/// - **Sweep line**: Replaces the join with a custom `SweepLineJoinExec` -/// that sorts and sweeps. Best for heavy-tailed or high-variance -/// width distributions. -/// - **Binned join**: Wraps each input in a `BinExpandExec` that -/// expands intervals into genome bins, then lets DataFusion's -/// parallel `HashJoinExec` join on `(chrom, bin_id)`. Best for -/// uniform-width data. -#[derive(Debug)] -pub struct IntersectsOptimizerRule { - config: IntersectsOptimizerConfig, -} - -impl IntersectsOptimizerRule { - pub fn new(config: IntersectsOptimizerConfig) -> Self { - Self { config } - } - - fn optimize_plan( - &self, - plan: Arc, - ) -> Result> { - log::debug!( - "INTERSECTS optimizer: visiting node: {}", - plan.name() - ); - if let Some(join_match) = detect_interval_join(&plan)? { - return self.maybe_replace_join(plan, join_match); - } - - let children: Vec> = plan - .children() - .into_iter() - .map(|child| self.optimize_plan(child.clone())) - .collect::>>()?; - - if children.is_empty() { - return Ok(plan); - } - - plan.with_new_children(children) - } - - fn maybe_replace_join( - &self, - original_plan: Arc, - join_match: IntervalJoinMatch, - ) -> Result> { - let cost_model = CostModel::new(&self.config); - - let left_stats = self.collect_stats( - &join_match.left_parquet_paths, - &join_match.left_cols.start_col, - &join_match.left_cols.end_col, - ); - let right_stats = self.collect_stats( - &join_match.right_parquet_paths, - &join_match.right_cols.start_col, - &join_match.right_cols.end_col, - ); - - let strategy = match (&left_stats, &right_stats) { - (Some(left), Some(right)) => cost_model.decide(left, right), - _ => { - log::debug!( - "INTERSECTS optimizer: no Parquet stats available, \ - deferring to DataFusion" - ); - return Ok(original_plan); - } - }; - - log::debug!("INTERSECTS optimizer: selected {strategy:?}"); - - match strategy { - JoinStrategy::SweepLine { build_side } => { - let bs = match build_side { - SmallSide::Left => BuildSide::Left, - SmallSide::Right => BuildSide::Right, - }; - Ok(Arc::new(SweepLineJoinExec::new( - join_match.left, - join_match.right, - join_match.left_cols, - join_match.right_cols, - join_match.output_schema, - bs, - ))) - } - JoinStrategy::BinnedJoin { .. } => { - // For binned joins, DataFusion's default hash join - // on chrom with the range filter is already well - // optimized. The physical plan rewrite overhead - // (BinExpandExec + modified HashJoinExec) exceeds - // the gain from bin-based hashing. Defer to - // DataFusion's built-in join. - log::debug!( - "INTERSECTS optimizer: binned strategy selected, \ - deferring to DataFusion" - ); - Ok(original_plan) - } - JoinStrategy::NestedLoop => { - log::info!( - "INTERSECTS optimizer: deferring to DataFusion's \ - built-in join" - ); - Ok(original_plan) - } - } - } - - fn collect_stats( - &self, - paths: &[std::path::PathBuf], - start_col: &str, - end_col: &str, - ) -> Option { - if paths.is_empty() { - return None; - } - - let path = &paths[0]; - match stats::collect_parquet_stats( - path, - start_col, - end_col, - self.config.max_sample_row_groups, - ) { - Ok(stats) => Some(stats), - Err(e) => { - log::warn!( - "INTERSECTS optimizer: failed to collect stats \ - from {path:?}: {e}" - ); - None - } - } - } -} - -impl PhysicalOptimizerRule for IntersectsOptimizerRule { - fn optimize( - &self, - plan: Arc, - _config: &ConfigOptions, - ) -> Result> { - self.optimize_plan(plan) - } - - fn name(&self) -> &str { - "intersects_optimizer" - } - - fn schema_check(&self) -> bool { - true - } -} diff --git a/crates/giql-datafusion/src/pattern.rs b/crates/giql-datafusion/src/pattern.rs deleted file mode 100644 index a239b39..0000000 --- a/crates/giql-datafusion/src/pattern.rs +++ /dev/null @@ -1,410 +0,0 @@ -use std::path::PathBuf; -use std::sync::Arc; - -use arrow::datatypes::SchemaRef; -use datafusion::common::{JoinSide, Result}; -use datafusion::physical_plan::joins::utils::ColumnIndex; -use datafusion::physical_plan::ExecutionPlan; - -/// Column indices for the genomic interval columns on one side of a -/// join. -#[derive(Debug, Clone)] -pub struct IntervalColumns { - /// Column name for chromosome. - pub chrom_col: String, - /// Column name for interval start. - pub start_col: String, - /// Column name for interval end. - pub end_col: String, - /// Column index for chromosome in the side's schema. - pub chrom_idx: usize, - /// Column index for start in the side's schema. - pub start_idx: usize, - /// Column index for end in the side's schema. - pub end_idx: usize, -} - -/// A detected interval overlap join pattern in the physical plan. -#[derive(Debug)] -pub struct IntervalJoinMatch { - /// The left input execution plan. - pub left: Arc, - /// The right input execution plan. - pub right: Arc, - /// Interval column indices on the left side. - pub left_cols: IntervalColumns, - /// Interval column indices on the right side. - pub right_cols: IntervalColumns, - /// Output schema of the join node being replaced. - pub output_schema: SchemaRef, - /// Parquet file paths from the left source, if available. - pub left_parquet_paths: Vec, - /// Parquet file paths from the right source, if available. - pub right_parquet_paths: Vec, -} - -/// Attempt to detect an interval overlap join in the given execution -/// plan node. -/// -/// Looks for join nodes (hash join, nested loop join) with predicates -/// matching the pattern: -/// -/// ```text -/// left.chrom = right.chrom -/// AND left.start < right.end -/// AND left.end > right.start -/// ``` -pub fn detect_interval_join( - plan: &Arc, -) -> Result> { - let plan_name = plan.name(); - - match plan_name { - "NestedLoopJoinExec" => detect_from_nested_loop_join(plan), - "HashJoinExec" => detect_from_hash_join(plan), - _ => Ok(None), - } -} - -/// Detect interval overlap in a NestedLoopJoinExec. -fn detect_from_nested_loop_join( - _plan: &Arc, -) -> Result> { - // NestedLoopJoinExec detection requires walking the full filter - // expression tree to find all three predicates (chrom equality + - // range overlap). This is deferred to a future iteration — the - // HashJoinExec path handles the common case where DataFusion - // separates the equi-key (chrom) from the range filter. - Ok(None) -} - -/// Detect interval overlap in a HashJoinExec. -fn detect_from_hash_join( - plan: &Arc, -) -> Result> { - use datafusion::physical_plan::joins::HashJoinExec; - - let hj = match plan.as_any().downcast_ref::() { - Some(hj) => hj, - None => return Ok(None), - }; - - let filter = match hj.filter() { - Some(f) => f, - None => return Ok(None), - }; - - let left = hj.left().clone(); - let right = hj.right().clone(); - let left_schema = left.schema(); - let right_schema = right.schema(); - - // Extract the equi-join key — should be a chromosome column - let on = hj.on(); - if on.is_empty() { - return Ok(None); - } - - use datafusion::physical_expr::expressions::Column; - - let (left_chrom_key, right_chrom_key) = &on[0]; - let left_chrom_col = - match left_chrom_key.as_any().downcast_ref::() { - Some(c) => c, - None => return Ok(None), - }; - let right_chrom_col = - match right_chrom_key.as_any().downcast_ref::() { - Some(c) => c, - None => return Ok(None), - }; - let left_chrom_name = left_chrom_col.name().to_string(); - let right_chrom_name = right_chrom_col.name().to_string(); - - let left_chrom_idx = match left_schema - .fields() - .iter() - .position(|f| f.name() == &left_chrom_name) - { - Some(idx) => idx, - None => return Ok(None), - }; - let right_chrom_idx = match right_schema - .fields() - .iter() - .position(|f| f.name() == &right_chrom_name) - { - Some(idx) => idx, - None => return Ok(None), - }; - - // Extract start/end columns from the filter expression - let filter_expr = filter.expression(); - let column_indices = filter.column_indices(); - let range_cols = match extract_range_columns_from_filter( - filter_expr, - column_indices, - &left_schema, - &right_schema, - ) { - Some(cols) => cols, - None => { - log::debug!( - "INTERSECTS optimizer: HashJoinExec filter didn't \ - match. filter={:?}, indices={:?}", - filter_expr, column_indices, - ); - return Ok(None); - } - }; - - let left_start_idx = match left_schema - .fields() - .iter() - .position(|f| f.name() == &range_cols.0) - { - Some(idx) => idx, - None => return Ok(None), - }; - let left_end_idx = match left_schema - .fields() - .iter() - .position(|f| f.name() == &range_cols.1) - { - Some(idx) => idx, - None => return Ok(None), - }; - let right_start_idx = match right_schema - .fields() - .iter() - .position(|f| f.name() == &range_cols.2) - { - Some(idx) => idx, - None => return Ok(None), - }; - let right_end_idx = match right_schema - .fields() - .iter() - .position(|f| f.name() == &range_cols.3) - { - Some(idx) => idx, - None => return Ok(None), - }; - - let left_cols = IntervalColumns { - chrom_col: left_chrom_name, - start_col: range_cols.0, - end_col: range_cols.1, - chrom_idx: left_chrom_idx, - start_idx: left_start_idx, - end_idx: left_end_idx, - }; - - let right_cols = IntervalColumns { - chrom_col: right_chrom_name, - start_col: range_cols.2, - end_col: range_cols.3, - chrom_idx: right_chrom_idx, - start_idx: right_start_idx, - end_idx: right_end_idx, - }; - - let left_parquet_paths = find_parquet_paths(&left); - let right_parquet_paths = find_parquet_paths(&right); - - Ok(Some(IntervalJoinMatch { - left, - right, - left_cols, - right_cols, - output_schema: plan.schema(), - left_parquet_paths, - right_parquet_paths, - })) -} - -/// Extract range column names from a HashJoin filter expression. -/// -/// The filter should contain the interval overlap condition -/// `A.start < B.end AND A.end > B.start`, but DataFusion may reorder -/// the operands arbitrarily. We resolve all four columns by name and -/// side, then match them to the canonical form. -/// -/// Returns `Some((left_start, left_end, right_start, right_end))`. -fn extract_range_columns_from_filter( - expr: &Arc, - column_indices: &[ColumnIndex], - left_schema: &SchemaRef, - right_schema: &SchemaRef, -) -> Option<(String, String, String, String)> { - use datafusion::logical_expr::Operator; - use datafusion::physical_expr::expressions::{BinaryExpr, Column}; - - let binary = expr.as_any().downcast_ref::()?; - if *binary.op() != Operator::And { - return None; - } - - // Collect all four column references from both predicates. - // Each predicate is either Lt or Gt with two Column operands. - let pred_a = binary.left(); - let pred_b = binary.right(); - - let cols_a = extract_comparison_columns( - pred_a, column_indices, left_schema, right_schema, - )?; - let cols_b = extract_comparison_columns( - pred_b, column_indices, left_schema, right_schema, - )?; - - // We have two predicates, each with a "lesser" and "greater" side: - // Lt(A, B) means A < B - // Gt(A, B) means A > B - // - // For interval overlap, the two predicates are (in any order): - // some_start < some_end (one from left, one from right) - // some_end > some_start (one from left, one from right) - // - // Rather than parsing the comparison semantics, we simply collect - // all four resolved columns, then identify left_start, left_end, - // right_start, right_end by matching (name, side). - - let all_cols = [&cols_a.0, &cols_a.1, &cols_b.0, &cols_b.1]; - - let left_start = all_cols.iter().find(|c| is_left(&c.side) && is_start_col(&c.name))?; - let left_end = all_cols.iter().find(|c| is_left(&c.side) && is_end_col(&c.name))?; - let right_start = all_cols.iter().find(|c| is_right(&c.side) && is_start_col(&c.name))?; - let right_end = all_cols.iter().find(|c| is_right(&c.side) && is_end_col(&c.name))?; - - Some(( - left_start.name.clone(), - left_end.name.clone(), - right_start.name.clone(), - right_end.name.clone(), - )) -} - -fn is_start_col(name: &str) -> bool { - let lower = name.to_lowercase(); - lower == "start" || lower == "chromstart" || lower == "pos_start" || lower == "begin" -} - -fn is_end_col(name: &str) -> bool { - let lower = name.to_lowercase(); - lower == "end" || lower == "chromend" || lower == "pos_end" || lower == "stop" -} - -/// A resolved column: name + which side of the join it's on. -#[derive(Debug)] -struct ResolvedColumn { - name: String, - side: JoinSide, -} - -fn is_left(side: &JoinSide) -> bool { - matches!(side, JoinSide::Left) -} - -fn is_right(side: &JoinSide) -> bool { - matches!(side, JoinSide::Right) -} - -/// Extract the two column operands of a Lt or Gt comparison. -fn extract_comparison_columns( - expr: &Arc, - column_indices: &[ColumnIndex], - left_schema: &SchemaRef, - right_schema: &SchemaRef, -) -> Option<(ResolvedColumn, ResolvedColumn)> { - use datafusion::logical_expr::Operator; - use datafusion::physical_expr::expressions::{BinaryExpr, Column}; - - let binary = expr.as_any().downcast_ref::()?; - match binary.op() { - Operator::Lt | Operator::Gt | Operator::LtEq | Operator::GtEq => {} - _ => return None, - } - - let lhs = binary.left().as_any().downcast_ref::()?; - let rhs = binary.right().as_any().downcast_ref::()?; - - let lhs_resolved = - resolve_column(lhs.index(), column_indices, left_schema, right_schema)?; - let rhs_resolved = - resolve_column(rhs.index(), column_indices, left_schema, right_schema)?; - - // Ensure the two columns are from different sides - if std::mem::discriminant(&lhs_resolved.side) - == std::mem::discriminant(&rhs_resolved.side) - { - return None; - } - - Some((lhs_resolved, rhs_resolved)) -} - -/// Resolve a filter-local column index to a name and join side. -fn resolve_column( - filter_idx: usize, - column_indices: &[ColumnIndex], - left_schema: &SchemaRef, - right_schema: &SchemaRef, -) -> Option { - if filter_idx >= column_indices.len() { - return None; - } - - let col_idx = &column_indices[filter_idx]; - let (schema, side) = match col_idx.side { - JoinSide::Left => (left_schema, JoinSide::Left), - JoinSide::Right => (right_schema, JoinSide::Right), - _ => return None, - }; - - if col_idx.index >= schema.fields().len() { - return None; - } - let name = schema.field(col_idx.index).name().clone(); - Some(ResolvedColumn { name, side }) -} - -/// Recursively find Parquet file paths in the plan tree. -/// -/// DataFusion's object store stores paths relative to the filesystem -/// root (no leading `/`). We prepend `/` to reconstruct the absolute -/// path so that `File::open` works. -fn find_parquet_paths(plan: &Arc) -> Vec { - use datafusion::datasource::physical_plan::parquet::source::ParquetSource; - use datafusion::datasource::source::DataSourceExec; - - let mut paths = Vec::new(); - - if let Some(ds_exec) = - plan.as_any().downcast_ref::() - { - if let Some((file_config, _parquet_source)) = - ds_exec.downcast_to_file_source::() - { - for group in &file_config.file_groups { - for file in group.iter() { - let loc = file.object_meta.location.as_ref(); - // object_store strips the leading / from absolute - // paths. Reconstruct it for filesystem access. - let fs_path = if loc.starts_with('/') { - PathBuf::from(loc) - } else { - PathBuf::from(format!("/{loc}")) - }; - paths.push(fs_path); - } - } - return paths; - } - } - - for child in plan.children() { - paths.extend(find_parquet_paths(child)); - } - - paths -} diff --git a/crates/giql-datafusion/src/pruning.rs b/crates/giql-datafusion/src/pruning.rs deleted file mode 100644 index 91271ca..0000000 --- a/crates/giql-datafusion/src/pruning.rs +++ /dev/null @@ -1,165 +0,0 @@ -use crate::stats::IntervalStats; - -/// Domain bounds for one side of the join, derived from row group -/// metadata. -#[derive(Debug, Clone)] -pub struct DomainBounds { - /// Global minimum start position across all row groups. - pub min_start: i64, - /// Global maximum end position across all row groups. - pub max_end: i64, -} - -impl From<&IntervalStats> for DomainBounds { - fn from(stats: &IntervalStats) -> Self { - Self { - min_start: stats.domain_min, - max_end: stats.domain_max, - } - } -} - -/// Generate pruning predicates from domain bounds. -/// -/// These predicates are always true for the join result set but help -/// the database engine skip row groups that are entirely outside the -/// other side's range. They should be injected as additional filter -/// predicates on the `ParquetExec` nodes before the join. -/// -/// Returns predicate expressions as `(column_name, op, literal_value)` -/// tuples that can be converted to DataFusion `PhysicalExpr` nodes. -pub fn generate_pruning_predicates( - left_bounds: &DomainBounds, - right_bounds: &DomainBounds, -) -> Vec { - vec![ - // Left rows that start after right's max end cannot overlap - PruningPredicate { - side: JoinSide::Left, - column: PruningColumn::Start, - op: PruningOp::Lt, - value: right_bounds.max_end, - }, - // Left rows that end before right's min start cannot overlap - PruningPredicate { - side: JoinSide::Left, - column: PruningColumn::End, - op: PruningOp::Gt, - value: right_bounds.min_start, - }, - // Right rows that start after left's max end cannot overlap - PruningPredicate { - side: JoinSide::Right, - column: PruningColumn::Start, - op: PruningOp::Lt, - value: left_bounds.max_end, - }, - // Right rows that end before left's min start cannot overlap - PruningPredicate { - side: JoinSide::Right, - column: PruningColumn::End, - op: PruningOp::Gt, - value: left_bounds.min_start, - }, - ] -} - -/// A pruning predicate to inject on a ParquetExec node. -#[derive(Debug, Clone)] -pub struct PruningPredicate { - /// Which side of the join this predicate applies to. - pub side: JoinSide, - /// Which interval column to filter. - pub column: PruningColumn, - /// Comparison operator. - pub op: PruningOp, - /// Literal value to compare against. - pub value: i64, -} - -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub enum JoinSide { - Left, - Right, -} - -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub enum PruningColumn { - Start, - End, -} - -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub enum PruningOp { - Lt, - Gt, -} - -#[cfg(test)] -mod tests { - use super::*; - - #[test] - fn test_generate_pruning_predicates() { - let left = DomainBounds { - min_start: 1000, - max_end: 50000, - }; - let right = DomainBounds { - min_start: 2000, - max_end: 60000, - }; - - let preds = generate_pruning_predicates(&left, &right); - assert_eq!(preds.len(), 4); - - // Left start < 60000 (right max_end) - assert_eq!(preds[0].side, JoinSide::Left); - assert_eq!(preds[0].column, PruningColumn::Start); - assert_eq!(preds[0].op, PruningOp::Lt); - assert_eq!(preds[0].value, 60000); - - // Left end > 2000 (right min_start) - assert_eq!(preds[1].side, JoinSide::Left); - assert_eq!(preds[1].column, PruningColumn::End); - assert_eq!(preds[1].op, PruningOp::Gt); - assert_eq!(preds[1].value, 2000); - - // Right start < 50000 (left max_end) - assert_eq!(preds[2].side, JoinSide::Right); - assert_eq!(preds[2].column, PruningColumn::Start); - assert_eq!(preds[2].op, PruningOp::Lt); - assert_eq!(preds[2].value, 50000); - - // Right end > 1000 (left min_start) - assert_eq!(preds[3].side, JoinSide::Right); - assert_eq!(preds[3].column, PruningColumn::End); - assert_eq!(preds[3].op, PruningOp::Gt); - assert_eq!(preds[3].value, 1000); - } - - #[test] - fn test_domain_bounds_from_interval_stats() { - use crate::stats::{IntervalStats, WidthStats}; - - let stats = IntervalStats { - row_count: 1000, - domain_min: 500, - domain_max: 50000, - is_sorted_by_start: false, - row_group_bounds: vec![], - width: WidthStats { - median: 100.0, - mean: 100.0, - p95: 100.0, - p99: 100.0, - cv: 0.0, - p99_median_ratio: 1.0, - }, - }; - - let bounds = DomainBounds::from(&stats); - assert_eq!(bounds.min_start, 500); - assert_eq!(bounds.max_end, 50000); - } -} diff --git a/crates/giql-datafusion/src/stats/metadata.rs b/crates/giql-datafusion/src/stats/metadata.rs deleted file mode 100644 index bd2415e..0000000 --- a/crates/giql-datafusion/src/stats/metadata.rs +++ /dev/null @@ -1,273 +0,0 @@ -use std::fs::File; -use std::path::Path; - -use datafusion::common::Result; -use parquet::file::reader::FileReader; -use parquet::file::serialized_reader::SerializedFileReader; -use parquet::file::statistics::Statistics; - -use super::types::RowGroupBounds; - -/// Statistics extracted from Parquet file footer metadata only (no data -/// pages read). This is effectively free — it reads the file footer -/// which is already cached by the OS after open. -#[derive(Debug, Clone)] -pub struct MetadataStats { - /// Per-row-group bounds for start/end columns. - pub row_group_bounds: Vec, - /// Total row count across all row groups. - pub total_rows: usize, - /// Global minimum start position. - pub domain_min: i64, - /// Global maximum end position. - pub domain_max: i64, - /// Whether the file declares itself sorted by the start column. - pub is_sorted_by_start: bool, - /// Whether page-level column index is present. - pub has_page_index: bool, -} - -/// Collect metadata-tier statistics from a Parquet file footer. -/// -/// Reads only the file footer — no data pages are touched. Returns -/// per-row-group column statistics for the start and end columns, plus -/// file-level properties like sort order and page index presence. -pub fn collect_metadata( - path: &Path, - start_col: &str, - end_col: &str, -) -> Result { - let file = File::open(path).map_err(|e| { - datafusion::error::DataFusionError::External(Box::new(e)) - })?; - let reader = SerializedFileReader::new(file).map_err(|e| { - datafusion::error::DataFusionError::External(Box::new(e)) - })?; - - let parquet_metadata = reader.metadata(); - let file_metadata = parquet_metadata.file_metadata(); - let schema = file_metadata.schema_descr(); - - // Find column indices for start and end - let start_idx = find_column_index(schema, start_col)?; - let end_idx = find_column_index(schema, end_col)?; - - // Check sort order from file metadata - let is_sorted_by_start = check_sort_order(file_metadata, start_col, schema); - - let num_row_groups = parquet_metadata.num_row_groups(); - let mut row_group_bounds = Vec::with_capacity(num_row_groups); - let mut total_rows: usize = 0; - let mut global_min_start = i64::MAX; - let mut global_max_end = i64::MIN; - let mut has_page_index = false; - - for rg_idx in 0..num_row_groups { - let rg_metadata = parquet_metadata.row_group(rg_idx); - let row_count = rg_metadata.num_rows() as usize; - total_rows += row_count; - - let start_col_meta = rg_metadata.column(start_idx); - let end_col_meta = rg_metadata.column(end_idx); - - // Check page index on first row group - if rg_idx == 0 { - has_page_index = start_col_meta.offset_index_offset().is_some() - || start_col_meta.column_index_offset().is_some(); - } - - let (min_start, max_start) = extract_i64_min_max( - start_col_meta.statistics(), - start_col, - )?; - let (min_end, max_end) = extract_i64_min_max( - end_col_meta.statistics(), - end_col, - )?; - - if min_start < global_min_start { - global_min_start = min_start; - } - if max_end > global_max_end { - global_max_end = max_end; - } - - row_group_bounds.push(RowGroupBounds { - min_start, - max_start, - min_end, - max_end, - row_count, - }); - } - - Ok(MetadataStats { - row_group_bounds, - total_rows, - domain_min: global_min_start, - domain_max: global_max_end, - is_sorted_by_start, - has_page_index, - }) -} - -/// Find the column index for a named column in the Parquet schema. -fn find_column_index( - schema: &parquet::schema::types::SchemaDescriptor, - col_name: &str, -) -> Result { - for i in 0..schema.num_columns() { - if schema.column(i).name() == col_name { - return Ok(i); - } - } - Err(datafusion::error::DataFusionError::Plan(format!( - "Column '{col_name}' not found in Parquet schema" - ))) -} - -/// Check whether the file declares itself sorted by the start column. -fn check_sort_order( - file_metadata: &parquet::file::metadata::FileMetaData, - start_col: &str, - schema: &parquet::schema::types::SchemaDescriptor, -) -> bool { - // Check key-value metadata for sorting_columns - if let Some(kv_metadata) = file_metadata.key_value_metadata() { - for kv in kv_metadata { - if kv.key == "sorting_columns" || kv.key == "pandas.sort_columns" { - if let Some(ref value) = kv.value { - if value.contains(start_col) { - return true; - } - } - } - } - } - - // Check if column order metadata indicates ascending on start column - if let Some(sort_order) = file_metadata.column_orders() { - if let Ok(start_idx) = find_column_index(schema, start_col) { - if start_idx < sort_order.len() { - // TypeDefinedOrder means the natural ordering applies, - // which combined with sorted row groups suggests sorted data. - // However, this only indicates comparison semantics, not - // that data is actually sorted. We need sorting_columns - // metadata for a definitive answer. Return false here. - } - } - } - - false -} - -/// Extract min and max i64 values from Parquet column statistics. -fn extract_i64_min_max( - statistics: Option<&Statistics>, - col_name: &str, -) -> Result<(i64, i64)> { - match statistics { - Some(Statistics::Int32(stats)) => { - let min_val = stats.min_opt().ok_or_else(|| { - datafusion::error::DataFusionError::Plan(format!( - "Column '{col_name}' Int32 stats missing min" - )) - })?; - let max_val = stats.max_opt().ok_or_else(|| { - datafusion::error::DataFusionError::Plan(format!( - "Column '{col_name}' Int32 stats missing max" - )) - })?; - Ok((*min_val as i64, *max_val as i64)) - } - Some(Statistics::Int64(stats)) => { - let min_val = stats.min_opt().ok_or_else(|| { - datafusion::error::DataFusionError::Plan(format!( - "Column '{col_name}' Int64 stats missing min" - )) - })?; - let max_val = stats.max_opt().ok_or_else(|| { - datafusion::error::DataFusionError::Plan(format!( - "Column '{col_name}' Int64 stats missing max" - )) - })?; - Ok((*min_val, *max_val)) - } - Some(_) => Err(datafusion::error::DataFusionError::Plan(format!( - "Column '{col_name}' has unsupported statistics type for interval bounds" - ))), - None => Err(datafusion::error::DataFusionError::Plan(format!( - "Column '{col_name}' has no statistics in Parquet metadata" - ))), - } -} - -#[cfg(test)] -mod tests { - use super::*; - use arrow::array::Int64Array; - use arrow::datatypes::{DataType, Field, Schema}; - use arrow::record_batch::RecordBatch; - use parquet::arrow::ArrowWriter; - use std::sync::Arc; - use tempfile::NamedTempFile; - - fn write_test_parquet( - starts: &[i64], - ends: &[i64], - ) -> NamedTempFile { - let schema = Arc::new(Schema::new(vec![ - Field::new("chrom", DataType::Utf8, false), - Field::new("start", DataType::Int64, false), - Field::new("end", DataType::Int64, false), - ])); - let chroms: Vec<&str> = vec!["chr1"; starts.len()]; - let batch = RecordBatch::try_new( - schema.clone(), - vec![ - Arc::new(arrow::array::StringArray::from(chroms)), - Arc::new(Int64Array::from(starts.to_vec())), - Arc::new(Int64Array::from(ends.to_vec())), - ], - ) - .unwrap(); - - let file = NamedTempFile::new().unwrap(); - let mut writer = - ArrowWriter::try_new(file.reopen().unwrap(), schema, None) - .unwrap(); - writer.write(&batch).unwrap(); - writer.close().unwrap(); - file - } - - #[test] - fn test_collect_metadata_basic() { - let starts = vec![100, 200, 300, 400, 500]; - let ends = vec![150, 280, 350, 450, 600]; - let file = write_test_parquet(&starts, &ends); - - let stats = - collect_metadata(file.path(), "start", "end").unwrap(); - - assert_eq!(stats.total_rows, 5); - assert_eq!(stats.domain_min, 100); - assert_eq!(stats.domain_max, 600); - assert_eq!(stats.row_group_bounds.len(), 1); - - let rg = &stats.row_group_bounds[0]; - assert_eq!(rg.min_start, 100); - assert_eq!(rg.max_start, 500); - assert_eq!(rg.min_end, 150); - assert_eq!(rg.max_end, 600); - assert_eq!(rg.row_count, 5); - } - - #[test] - fn test_collect_metadata_missing_column() { - let file = write_test_parquet(&[100], &[200]); - let result = - collect_metadata(file.path(), "nonexistent", "end"); - assert!(result.is_err()); - } -} diff --git a/crates/giql-datafusion/src/stats/mod.rs b/crates/giql-datafusion/src/stats/mod.rs deleted file mode 100644 index 6ce8aa3..0000000 --- a/crates/giql-datafusion/src/stats/mod.rs +++ /dev/null @@ -1,150 +0,0 @@ -pub mod metadata; -pub mod sampler; -pub mod types; - -pub use metadata::{collect_metadata, MetadataStats}; -pub use sampler::sample_widths; -pub use types::{IntervalStats, RowGroupBounds, WidthStats}; - -use datafusion::common::Result; -use std::path::Path; - -/// Collect full interval statistics from a Parquet file by combining -/// metadata (tier 1, free) with lightweight sampling (tier 2, -/// milliseconds). -/// -/// Returns `None` if the file cannot be read or lacks the required -/// columns. -pub fn collect_parquet_stats( - path: &Path, - start_col: &str, - end_col: &str, - max_sample_row_groups: usize, -) -> Result { - // Tier 1: file footer metadata - let meta = collect_metadata(path, start_col, end_col)?; - - // Select representative row groups for sampling - let sample_indices = - select_representative_row_groups(meta.row_group_bounds.len(), max_sample_row_groups); - - // Tier 2: lightweight sampling - let width = sample_widths(path, start_col, end_col, &sample_indices)?; - - Ok(IntervalStats { - row_count: meta.total_rows, - domain_min: meta.domain_min, - domain_max: meta.domain_max, - is_sorted_by_start: meta.is_sorted_by_start, - row_group_bounds: meta.row_group_bounds, - width, - }) -} - -/// Pick representative row groups for sampling: first, middle, last. -fn select_representative_row_groups( - num_row_groups: usize, - max_sample: usize, -) -> Vec { - if num_row_groups == 0 { - return vec![]; - } - if num_row_groups == 1 || max_sample == 1 { - return vec![0]; - } - - let last = num_row_groups - 1; - if num_row_groups == 2 || max_sample == 2 { - return vec![0, last]; - } - - // First, middle, last - let mid = num_row_groups / 2; - let mut indices = vec![0, mid, last]; - indices.truncate(max_sample); - indices -} - -#[cfg(test)] -mod tests { - use super::*; - - #[test] - fn test_select_representative_single() { - assert_eq!(select_representative_row_groups(1, 3), vec![0]); - } - - #[test] - fn test_select_representative_two() { - assert_eq!(select_representative_row_groups(2, 3), vec![0, 1]); - } - - #[test] - fn test_select_representative_many() { - assert_eq!( - select_representative_row_groups(10, 3), - vec![0, 5, 9] - ); - } - - #[test] - fn test_select_representative_max_one() { - assert_eq!(select_representative_row_groups(10, 1), vec![0]); - } - - #[test] - fn test_collect_parquet_stats_uniform() { - use arrow::array::{Int64Array, StringArray}; - use arrow::datatypes::{DataType, Field, Schema}; - use arrow::record_batch::RecordBatch; - use parquet::arrow::ArrowWriter; - use std::sync::Arc; - use tempfile::NamedTempFile; - - let schema = Arc::new(Schema::new(vec![ - Field::new("chrom", DataType::Utf8, false), - Field::new("start", DataType::Int64, false), - Field::new("end", DataType::Int64, false), - ])); - let starts: Vec = (0..50).map(|i| i * 200).collect(); - let ends: Vec = starts.iter().map(|s| s + 100).collect(); - let chroms: Vec<&str> = vec!["chr1"; 50]; - let batch = RecordBatch::try_new( - schema.clone(), - vec![ - Arc::new(StringArray::from(chroms)), - Arc::new(Int64Array::from(starts.clone())), - Arc::new(Int64Array::from(ends.clone())), - ], - ) - .unwrap(); - - let file = NamedTempFile::new().unwrap(); - let mut writer = - ArrowWriter::try_new(file.reopen().unwrap(), schema, None) - .unwrap(); - writer.write(&batch).unwrap(); - writer.close().unwrap(); - - let stats = - collect_parquet_stats(file.path(), "start", "end", 3) - .unwrap(); - - assert_eq!(stats.row_count, 50); - assert_eq!(stats.domain_min, 0); - assert_eq!(stats.domain_max, *ends.last().unwrap()); - assert!((stats.width.median - 100.0).abs() < 1e-6); - assert!(stats.width.cv < 0.01); - } - - #[test] - fn test_collect_parquet_stats_nonexistent_file() { - let result = collect_parquet_stats( - Path::new("/tmp/nonexistent_file.parquet"), - "start", - "end", - 3, - ); - assert!(result.is_err()); - } -} diff --git a/crates/giql-datafusion/src/stats/sampler.rs b/crates/giql-datafusion/src/stats/sampler.rs deleted file mode 100644 index 765ef8c..0000000 --- a/crates/giql-datafusion/src/stats/sampler.rs +++ /dev/null @@ -1,304 +0,0 @@ -use std::fs::File; -use std::path::Path; - -use arrow::array::{Array, Int64Array}; -use datafusion::common::Result; -use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder; -use parquet::arrow::ProjectionMask; - -use super::types::WidthStats; - -/// Read start and end columns from selected row groups and compute -/// width distribution statistics. -/// -/// Only two columns are read from each row group — no other data is -/// touched. For a multi-GB dataset this typically completes in -/// milliseconds. -pub fn sample_widths( - path: &Path, - start_col: &str, - end_col: &str, - row_group_indices: &[usize], -) -> Result { - if row_group_indices.is_empty() { - return Err(datafusion::error::DataFusionError::Plan( - "No row groups to sample".to_string(), - )); - } - - let file = File::open(path).map_err(|e| { - datafusion::error::DataFusionError::External(Box::new(e)) - })?; - - let builder = - ParquetRecordBatchReaderBuilder::try_new(file).map_err(|e| { - datafusion::error::DataFusionError::External(Box::new(e)) - })?; - - // Find column indices in the Arrow schema - let arrow_schema = builder.schema().clone(); - let start_idx = arrow_schema - .fields() - .iter() - .position(|f| f.name() == start_col) - .ok_or_else(|| { - datafusion::error::DataFusionError::Plan(format!( - "Column '{start_col}' not found in Parquet schema" - )) - })?; - let end_idx = arrow_schema - .fields() - .iter() - .position(|f| f.name() == end_col) - .ok_or_else(|| { - datafusion::error::DataFusionError::Plan(format!( - "Column '{end_col}' not found in Parquet schema" - )) - })?; - - // Project only the start and end columns using the Parquet schema - let parquet_schema = builder.parquet_schema(); - let projection = - ProjectionMask::leaves(parquet_schema, vec![start_idx, end_idx]); - - let reader = builder - .with_projection(projection) - .with_row_groups(row_group_indices.to_vec()) - .build() - .map_err(|e| { - datafusion::error::DataFusionError::External(Box::new(e)) - })?; - - let mut widths: Vec = Vec::new(); - - for batch_result in reader { - let batch = batch_result.map_err(|e| { - datafusion::error::DataFusionError::External(Box::new(e)) - })?; - - // Columns are projected, so index 0 = start, 1 = end - let starts = extract_i64_column(&batch, 0, start_col)?; - let ends = extract_i64_column(&batch, 1, end_col)?; - - for i in 0..batch.num_rows() { - if !starts.is_null(i) && !ends.is_null(i) { - let w = (ends.value(i) - starts.value(i)) as f64; - widths.push(w); - } - } - } - - if widths.is_empty() { - return Err(datafusion::error::DataFusionError::Plan( - "No valid intervals found in sampled row groups".to_string(), - )); - } - - Ok(compute_width_stats(&mut widths)) -} - -/// Compute width distribution statistics from a vector of widths. -pub(crate) fn compute_width_stats(widths: &mut [f64]) -> WidthStats { - widths.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); - - let n = widths.len(); - let median = percentile_sorted(widths, 0.5); - let p95 = percentile_sorted(widths, 0.95); - let p99 = percentile_sorted(widths, 0.99); - - let sum: f64 = widths.iter().sum(); - let mean = sum / n as f64; - - let variance: f64 = - widths.iter().map(|w| (w - mean).powi(2)).sum::() / n as f64; - let std_dev = variance.sqrt(); - let cv = if mean > 0.0 { std_dev / mean } else { 0.0 }; - - let p99_median_ratio = if median > 0.0 { p99 / median } else { 0.0 }; - - WidthStats { - median, - mean, - p95, - p99, - cv, - p99_median_ratio, - } -} - -/// Compute a percentile from a sorted slice using linear interpolation. -fn percentile_sorted(sorted: &[f64], p: f64) -> f64 { - if sorted.is_empty() { - return 0.0; - } - if sorted.len() == 1 { - return sorted[0]; - } - - let rank = p * (sorted.len() - 1) as f64; - let lower = rank.floor() as usize; - let upper = rank.ceil() as usize; - let frac = rank - lower as f64; - - if lower == upper { - sorted[lower] - } else { - sorted[lower] * (1.0 - frac) + sorted[upper] * frac - } -} - -/// Extract an i64 column from a record batch, handling both Int32 and -/// Int64 physical types. -fn extract_i64_column( - batch: &arrow::record_batch::RecordBatch, - col_idx: usize, - col_name: &str, -) -> Result { - let col = batch.column(col_idx); - - if let Some(arr) = col.as_any().downcast_ref::() { - return Ok(arr.clone()); - } - - if let Some(arr) = - col.as_any().downcast_ref::() - { - let converted: Int64Array = arr - .iter() - .map(|v| v.map(|x| x as i64)) - .collect(); - return Ok(converted); - } - - Err(datafusion::error::DataFusionError::Plan(format!( - "Column '{col_name}' is not Int32 or Int64" - ))) -} - -#[cfg(test)] -mod tests { - use super::*; - - #[test] - fn test_compute_width_stats_uniform() { - let mut widths = vec![100.0; 1000]; - let stats = compute_width_stats(&mut widths); - - assert!((stats.median - 100.0).abs() < 1e-6); - assert!((stats.mean - 100.0).abs() < 1e-6); - assert!((stats.cv).abs() < 1e-6); - assert!((stats.p99_median_ratio - 1.0).abs() < 1e-6); - } - - #[test] - fn test_compute_width_stats_heavy_tailed() { - // 950 intervals of width 100, 50 intervals of width 10000. - // With 5% outliers, p99 lands squarely in the 10000 range. - let mut widths: Vec = vec![100.0; 950]; - widths.extend(vec![10000.0; 50]); - let stats = compute_width_stats(&mut widths); - - assert!(stats.p99_median_ratio > 10.0); - assert!(stats.cv > 1.0); - } - - #[test] - fn test_percentile_sorted() { - let data = vec![1.0, 2.0, 3.0, 4.0, 5.0]; - assert!((percentile_sorted(&data, 0.0) - 1.0).abs() < 1e-6); - assert!((percentile_sorted(&data, 0.5) - 3.0).abs() < 1e-6); - assert!((percentile_sorted(&data, 1.0) - 5.0).abs() < 1e-6); - } - - #[test] - fn test_percentile_sorted_single() { - assert!((percentile_sorted(&[42.0], 0.5) - 42.0).abs() < 1e-6); - } - - #[test] - fn test_sample_widths_uniform_parquet() { - use arrow::array::{Int64Array, StringArray}; - use arrow::datatypes::{DataType, Field, Schema}; - use arrow::record_batch::RecordBatch; - use parquet::arrow::ArrowWriter; - use std::sync::Arc; - use tempfile::NamedTempFile; - - // Write a Parquet file with uniform 100bp intervals - let schema = Arc::new(Schema::new(vec![ - Field::new("chrom", DataType::Utf8, false), - Field::new("start", DataType::Int64, false), - Field::new("end", DataType::Int64, false), - ])); - let starts: Vec = (0..100).map(|i| i * 200).collect(); - let ends: Vec = starts.iter().map(|s| s + 100).collect(); - let chroms: Vec<&str> = vec!["chr1"; 100]; - let batch = RecordBatch::try_new( - schema.clone(), - vec![ - Arc::new(StringArray::from(chroms)), - Arc::new(Int64Array::from(starts)), - Arc::new(Int64Array::from(ends)), - ], - ) - .unwrap(); - - let file = NamedTempFile::new().unwrap(); - let mut writer = - ArrowWriter::try_new(file.reopen().unwrap(), schema, None) - .unwrap(); - writer.write(&batch).unwrap(); - writer.close().unwrap(); - - let stats = - sample_widths(file.path(), "start", "end", &[0]).unwrap(); - assert!((stats.median - 100.0).abs() < 1e-6); - assert!(stats.cv < 0.01); - } - - #[test] - fn test_sample_widths_missing_column() { - use arrow::array::{Int64Array, StringArray}; - use arrow::datatypes::{DataType, Field, Schema}; - use arrow::record_batch::RecordBatch; - use parquet::arrow::ArrowWriter; - use std::sync::Arc; - use tempfile::NamedTempFile; - - let schema = Arc::new(Schema::new(vec![ - Field::new("chrom", DataType::Utf8, false), - Field::new("start", DataType::Int64, false), - Field::new("end", DataType::Int64, false), - ])); - let batch = RecordBatch::try_new( - schema.clone(), - vec![ - Arc::new(StringArray::from(vec!["chr1"])), - Arc::new(Int64Array::from(vec![100])), - Arc::new(Int64Array::from(vec![200])), - ], - ) - .unwrap(); - let file = NamedTempFile::new().unwrap(); - let mut writer = - ArrowWriter::try_new(file.reopen().unwrap(), schema, None) - .unwrap(); - writer.write(&batch).unwrap(); - writer.close().unwrap(); - - let result = - sample_widths(file.path(), "nonexistent", "end", &[0]); - assert!(result.is_err()); - } - - #[test] - fn test_sample_widths_empty_row_groups() { - let result = sample_widths( - std::path::Path::new("/tmp/fake.parquet"), - "start", - "end", - &[], - ); - assert!(result.is_err()); - } -} diff --git a/crates/giql-datafusion/src/stats/types.rs b/crates/giql-datafusion/src/stats/types.rs deleted file mode 100644 index 44c7f9e..0000000 --- a/crates/giql-datafusion/src/stats/types.rs +++ /dev/null @@ -1,143 +0,0 @@ -/// Per-row-group statistics derived from Parquet column chunk metadata. -#[derive(Debug, Clone)] -pub struct RowGroupBounds { - /// Minimum value of the start column in this row group. - pub min_start: i64, - /// Maximum value of the start column in this row group. - pub max_start: i64, - /// Minimum value of the end column in this row group. - pub min_end: i64, - /// Maximum value of the end column in this row group. - pub max_end: i64, - /// Number of rows in this row group. - pub row_count: usize, -} - -impl RowGroupBounds { - /// Loose upper bound on interval width within this row group. - /// - /// No interval can be wider than `max(end) - min(start)`. - pub fn width_upper_bound(&self) -> i64 { - self.max_end - self.min_start - } - - /// Width signal from the high end of the sort order. - /// - /// If `max(end) - max(start)` is small, the widest intervals at - /// the end of the row group are narrow. - pub fn width_at_max(&self) -> i64 { - self.max_end - self.max_start - } - - /// Width signal from the low end of the sort order. - /// - /// If `min(end) - min(start)` is small, the earliest intervals - /// are narrow. - pub fn width_at_min(&self) -> i64 { - self.min_end - self.min_start - } -} - -/// Width distribution statistics computed from sampled intervals. -#[derive(Debug, Clone)] -pub struct WidthStats { - /// Median interval width. - pub median: f64, - /// Mean interval width. - pub mean: f64, - /// 95th percentile width. - pub p95: f64, - /// 99th percentile width. - pub p99: f64, - /// Coefficient of variation (std_dev / mean). - pub cv: f64, - /// Ratio of p99 to median width. - pub p99_median_ratio: f64, -} - -/// Aggregate interval statistics for a Parquet file, combining -/// metadata-derived bounds with sampled width distribution. -#[derive(Debug, Clone)] -pub struct IntervalStats { - /// Total row count across all row groups. - pub row_count: usize, - /// Global minimum start position. - pub domain_min: i64, - /// Global maximum end position. - pub domain_max: i64, - /// Whether the file is sorted by the start column - /// (from `sorting_columns` metadata). - pub is_sorted_by_start: bool, - /// Per-row-group bounds from column chunk statistics. - pub row_group_bounds: Vec, - /// Width distribution from sampling. - pub width: WidthStats, -} - -impl IntervalStats { - /// Domain span: total coordinate range covered by the file. - pub fn domain_span(&self) -> i64 { - self.domain_max - self.domain_min - } -} - -#[cfg(test)] -mod tests { - use super::*; - - #[test] - fn test_width_upper_bound() { - let rg = RowGroupBounds { - min_start: 100, - max_start: 400, - min_end: 200, - max_end: 500, - row_count: 10, - }; - assert_eq!(rg.width_upper_bound(), 400); - } - - #[test] - fn test_width_at_max() { - let rg = RowGroupBounds { - min_start: 100, - max_start: 400, - min_end: 200, - max_end: 500, - row_count: 10, - }; - assert_eq!(rg.width_at_max(), 100); - } - - #[test] - fn test_width_at_min() { - let rg = RowGroupBounds { - min_start: 100, - max_start: 400, - min_end: 200, - max_end: 500, - row_count: 10, - }; - assert_eq!(rg.width_at_min(), 100); - } - - #[test] - fn test_domain_span() { - let stats = IntervalStats { - row_count: 1000, - domain_min: 0, - domain_max: 1_000_000, - is_sorted_by_start: false, - row_group_bounds: vec![], - width: WidthStats { - median: 100.0, - mean: 100.0, - p95: 100.0, - p99: 100.0, - cv: 0.0, - p99_median_ratio: 1.0, - }, - }; - assert_eq!(stats.domain_span(), 1_000_000); - } -} diff --git a/crates/giql-datafusion/tests/integration_test.rs b/crates/giql-datafusion/tests/integration_test.rs deleted file mode 100644 index 85ba526..0000000 --- a/crates/giql-datafusion/tests/integration_test.rs +++ /dev/null @@ -1,269 +0,0 @@ -//! Integration tests for the INTERSECTS join optimizer. -//! -//! These tests exercise the full pipeline: create Parquet files → -//! register with DataFusion → add optimizer rule → execute -//! INTERSECTS join SQL → verify results. - -use std::path::Path; -use std::sync::Arc; - -use arrow::array::{Int64Array, StringArray}; -use arrow::datatypes::{DataType, Field, Schema}; -use arrow::record_batch::RecordBatch; -use datafusion::execution::SessionStateBuilder; -use datafusion::prelude::*; -use parquet::arrow::ArrowWriter; -use tempfile::TempDir; - -use giql_datafusion::{IntersectsOptimizerConfig, register_optimizer}; - -/// Write a Parquet file with the given genomic intervals. -fn write_intervals_parquet( - dir: &Path, - filename: &str, - chroms: &[&str], - starts: &[i64], - ends: &[i64], -) -> std::path::PathBuf { - let schema = Arc::new(Schema::new(vec![ - Field::new("chrom", DataType::Utf8, false), - Field::new("start", DataType::Int64, false), - Field::new("end", DataType::Int64, false), - ])); - - let batch = RecordBatch::try_new( - schema.clone(), - vec![ - Arc::new(StringArray::from(chroms.to_vec())), - Arc::new(Int64Array::from(starts.to_vec())), - Arc::new(Int64Array::from(ends.to_vec())), - ], - ) - .unwrap(); - - let path = dir.join(filename); - let file = std::fs::File::create(&path).unwrap(); - let mut writer = - ArrowWriter::try_new(file, schema, None).unwrap(); - writer.write(&batch).unwrap(); - writer.close().unwrap(); - path -} - -/// Create a SessionContext with the INTERSECTS optimizer registered. -fn make_ctx_with_optimizer() -> SessionContext { - let config = IntersectsOptimizerConfig::default(); - let state = SessionStateBuilder::new() - .with_default_features() - .build(); - let state = register_optimizer(state, config); - SessionContext::from(state) -} - -/// The standard INTERSECTS join SQL using the chrom/start/end -/// predicate pattern that the optimizer detects. -const INTERSECTS_SQL: &str = "\ - SELECT a.chrom, a.start, a.\"end\", \ - b.chrom AS chrom_b, b.start AS start_b, b.\"end\" AS end_b \ - FROM a JOIN b \ - ON a.chrom = b.chrom \ - AND a.start < b.\"end\" \ - AND a.\"end\" > b.start"; - -// ── Correctness tests ────────────────────────────────────────── - -#[tokio::test] -async fn test_overlapping_intervals_returns_pairs() { - let dir = TempDir::new().unwrap(); - let path_a = write_intervals_parquet( - dir.path(), - "a.parquet", - &["chr1", "chr1", "chr1"], - &[100, 300, 600], - &[250, 500, 800], - ); - let path_b = write_intervals_parquet( - dir.path(), - "b.parquet", - &["chr1", "chr1"], - &[200, 700], - &[400, 900], - ); - - let ctx = make_ctx_with_optimizer(); - ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) - .await - .unwrap(); - ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) - .await - .unwrap(); - - let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); - let batches = result.collect().await.unwrap(); - let total_rows: usize = - batches.iter().map(|b| b.num_rows()).sum(); - - // Expected overlaps: - // a[100,250) overlaps b[200,400) ✓ - // a[300,500) overlaps b[200,400) ✓ - // a[600,800) overlaps b[700,900) ✓ - assert_eq!(total_rows, 3); -} - -#[tokio::test] -async fn test_no_overlapping_intervals_returns_empty() { - let dir = TempDir::new().unwrap(); - let path_a = write_intervals_parquet( - dir.path(), - "a.parquet", - &["chr1", "chr1"], - &[100, 300], - &[200, 400], - ); - let path_b = write_intervals_parquet( - dir.path(), - "b.parquet", - &["chr1", "chr1"], - &[500, 700], - &[600, 800], - ); - - let ctx = make_ctx_with_optimizer(); - ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) - .await - .unwrap(); - ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) - .await - .unwrap(); - - let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); - let batches = result.collect().await.unwrap(); - let total_rows: usize = - batches.iter().map(|b| b.num_rows()).sum(); - - assert_eq!(total_rows, 0); -} - -#[tokio::test] -async fn test_different_chromosomes_returns_empty() { - let dir = TempDir::new().unwrap(); - let path_a = write_intervals_parquet( - dir.path(), - "a.parquet", - &["chr1", "chr1"], - &[100, 300], - &[500, 600], - ); - let path_b = write_intervals_parquet( - dir.path(), - "b.parquet", - &["chr2", "chr2"], - &[100, 300], - &[500, 600], - ); - - let ctx = make_ctx_with_optimizer(); - ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) - .await - .unwrap(); - ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) - .await - .unwrap(); - - let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); - let batches = result.collect().await.unwrap(); - let total_rows: usize = - batches.iter().map(|b| b.num_rows()).sum(); - - assert_eq!(total_rows, 0); -} - -#[tokio::test] -async fn test_adjacent_intervals_no_overlap() { - // Half-open interval semantics: [100,200) and [200,300) do NOT - // overlap because 200 is not < 200. - let dir = TempDir::new().unwrap(); - let path_a = write_intervals_parquet( - dir.path(), - "a.parquet", - &["chr1"], - &[100], - &[200], - ); - let path_b = write_intervals_parquet( - dir.path(), - "b.parquet", - &["chr1"], - &[200], - &[300], - ); - - let ctx = make_ctx_with_optimizer(); - ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) - .await - .unwrap(); - ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) - .await - .unwrap(); - - let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); - let batches = result.collect().await.unwrap(); - let total_rows: usize = - batches.iter().map(|b| b.num_rows()).sum(); - - assert_eq!(total_rows, 0); -} - -#[tokio::test] -async fn test_containment_counts_as_overlap() { - // [100,500) fully contains [200,300) - let dir = TempDir::new().unwrap(); - let path_a = write_intervals_parquet( - dir.path(), - "a.parquet", - &["chr1"], - &[100], - &[500], - ); - let path_b = write_intervals_parquet( - dir.path(), - "b.parquet", - &["chr1"], - &[200], - &[300], - ); - - let ctx = make_ctx_with_optimizer(); - ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) - .await - .unwrap(); - ctx.register_parquet("b", path_b.to_str().unwrap(), Default::default()) - .await - .unwrap(); - - let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); - let batches = result.collect().await.unwrap(); - let total_rows: usize = - batches.iter().map(|b| b.num_rows()).sum(); - - assert_eq!(total_rows, 1); -} - -// ── Optimizer registration test ──────────────────────────────── - -#[tokio::test] -async fn test_register_optimizer_present_in_state() { - let state = SessionStateBuilder::new() - .with_default_features() - .build(); - let n_before = state.physical_optimizers().len(); - - let config = IntersectsOptimizerConfig::default(); - let state = register_optimizer(state, config); - - assert_eq!(state.physical_optimizers().len(), n_before + 1); - assert_eq!( - state.physical_optimizers().last().unwrap().name(), - "intersects_optimizer" - ); -} diff --git a/crates/giql-datafusion/tests/logical_rule_test.rs b/crates/giql-datafusion/tests/logical_rule_test.rs index bf73902..8210ee7 100644 --- a/crates/giql-datafusion/tests/logical_rule_test.rs +++ b/crates/giql-datafusion/tests/logical_rule_test.rs @@ -4,7 +4,7 @@ //! - OptimizerRule trait implementation (name, apply_order, supports_rewrite) //! - Join type filtering (inner only, skip left/right/full outer) //! - Already-binned join detection (skip re-rewrite) -//! - Overlap pattern detection (start/end column name variants) +//! - giql_intersects() function detection //! - Adaptive bin sizing from table statistics //! - Canonical-bin dedup filter correctness //! - Full pipeline integration through DataFusion with the logical rule @@ -23,16 +23,12 @@ use parquet::arrow::ArrowWriter; use tempfile::TempDir; use giql_datafusion::logical_rule::IntersectsLogicalRule; -use giql_datafusion::{IntersectsOptimizerConfig, register_optimizer}; +use giql_datafusion::register_optimizer; // ── Helpers ───────────────────────────────────────────────────── -fn default_config() -> IntersectsOptimizerConfig { - IntersectsOptimizerConfig::default() -} - fn make_rule() -> IntersectsLogicalRule { - IntersectsLogicalRule::new(default_config()) + IntersectsLogicalRule::new() } fn write_intervals_parquet( @@ -94,29 +90,12 @@ fn write_intervals_parquet_custom_schema( path } -/// Create a SessionContext with the logical rule enabled. -fn make_ctx_with_logical_rule() -> SessionContext { - let config = IntersectsOptimizerConfig { - enable_logical_rule: true, - ..default_config() - }; - let state = SessionStateBuilder::new() - .with_default_features() - .build(); - let state = register_optimizer(state, config); - SessionContext::from(state) -} - -/// Create a SessionContext with the logical rule disabled. -fn make_ctx_without_logical_rule() -> SessionContext { - let config = IntersectsOptimizerConfig { - enable_logical_rule: false, - ..default_config() - }; +/// Create a SessionContext with the logical rule and UDF registered. +fn make_ctx() -> SessionContext { let state = SessionStateBuilder::new() .with_default_features() .build(); - let state = register_optimizer(state, config); + let state = register_optimizer(state); SessionContext::from(state) } @@ -125,8 +104,7 @@ const INTERSECTS_SQL: &str = "\ b.chrom AS chrom_b, b.start AS start_b, b.\"end\" AS end_b \ FROM a JOIN b \ ON a.chrom = b.chrom \ - AND a.start < b.\"end\" \ - AND a.\"end\" > b.start"; + AND giql_intersects(a.start, a.\"end\", b.start, b.\"end\")"; // ── OptimizerRule trait tests ─────────────────────────────────── @@ -159,13 +137,12 @@ fn test_rule_supports_rewrite() { #[test] fn test_rewrite_skips_non_join_plan() { - // Given a non-join logical plan (TableScan), + // Given a non-join logical plan (EmptyRelation), // When the rule is applied, // Then the plan is returned unchanged. let rule = make_rule(); let config = datafusion::optimizer::OptimizerContext::new(); - // Create a simple empty relation plan (not a join) let plan = LogicalPlan::EmptyRelation( datafusion::logical_expr::EmptyRelation { produce_one_row: false, @@ -181,10 +158,10 @@ fn test_rewrite_skips_non_join_plan() { #[tokio::test] async fn test_rewrite_skips_left_join() { - // Given a LEFT JOIN with interval overlap predicates, + // Given a LEFT JOIN with overlap predicates, // When the logical rule is applied, // Then the plan is not rewritten (only INNER joins are supported). - let ctx = SessionContext::new(); + let ctx = make_ctx(); let schema = Arc::new(Schema::new(vec![ Field::new("chrom", DataType::Utf8, false), Field::new("start", DataType::Int64, false), @@ -218,8 +195,7 @@ async fn test_rewrite_skips_left_join() { b.chrom, b.start, b.\"end\" \ FROM a LEFT JOIN b \ ON a.chrom = b.chrom \ - AND a.start < b.\"end\" \ - AND a.\"end\" > b.start"; + AND giql_intersects(a.start, a.\"end\", b.start, b.\"end\")"; let df = ctx.sql(left_join_sql).await.unwrap(); let plan = df.logical_plan().clone(); @@ -227,21 +203,16 @@ async fn test_rewrite_skips_left_join() { let rule = make_rule(); let config = datafusion::optimizer::OptimizerContext::new(); - // Walk the plan tree looking for join nodes let result = rule.rewrite(plan, &config).unwrap(); - // Either the plan is not transformed (because it's not a Join - // at top level), or if DataFusion restructured it, the rule - // should still not rewrite non-inner joins. - // The important thing is that the rule doesn't panic. let _ = result; } #[tokio::test] async fn test_rewrite_skips_right_join() { - // Given a RIGHT JOIN with interval overlap predicates, + // Given a RIGHT JOIN with overlap predicates, // When the logical rule is applied, // Then the plan is not rewritten (only INNER joins are supported). - let ctx = SessionContext::new(); + let ctx = make_ctx(); let schema = Arc::new(Schema::new(vec![ Field::new("chrom", DataType::Utf8, false), Field::new("start", DataType::Int64, false), @@ -275,8 +246,7 @@ async fn test_rewrite_skips_right_join() { b.chrom, b.start, b.\"end\" \ FROM a RIGHT JOIN b \ ON a.chrom = b.chrom \ - AND a.start < b.\"end\" \ - AND a.\"end\" > b.start"; + AND giql_intersects(a.start, a.\"end\", b.start, b.\"end\")"; let df = ctx.sql(right_join_sql).await.unwrap(); let plan = df.logical_plan().clone(); @@ -290,10 +260,10 @@ async fn test_rewrite_skips_right_join() { #[tokio::test] async fn test_rewrite_skips_full_outer_join() { - // Given a FULL OUTER JOIN with interval overlap predicates, + // Given a FULL OUTER JOIN with overlap predicates, // When the logical rule is applied, // Then the plan is not rewritten. - let ctx = SessionContext::new(); + let ctx = make_ctx(); let schema = Arc::new(Schema::new(vec![ Field::new("chrom", DataType::Utf8, false), Field::new("start", DataType::Int64, false), @@ -327,8 +297,7 @@ async fn test_rewrite_skips_full_outer_join() { b.chrom, b.start, b.\"end\" \ FROM a FULL OUTER JOIN b \ ON a.chrom = b.chrom \ - AND a.start < b.\"end\" \ - AND a.\"end\" > b.start"; + AND giql_intersects(a.start, a.\"end\", b.start, b.\"end\")"; let df = ctx.sql(full_join_sql).await.unwrap(); let plan = df.logical_plan().clone(); @@ -340,70 +309,67 @@ async fn test_rewrite_skips_full_outer_join() { let _ = result; } -// ── Register optimizer with logical rule enabled/disabled ─────── +// ── Raw overlap predicates are NOT rewritten ──────────────────── -#[test] -fn test_register_optimizer_with_logical_rule_enabled() { - // Given a default config with enable_logical_rule = true, - // When register_optimizer is called, - // Then both logical and physical rules are added. - let state = SessionStateBuilder::new() - .with_default_features() - .build(); - let n_logical_before = state.optimizers().len(); - let n_physical_before = state.physical_optimizers().len(); - - let config = IntersectsOptimizerConfig { - enable_logical_rule: true, - ..default_config() - }; - let state = register_optimizer(state, config); - - assert_eq!( - state.optimizers().len(), - n_logical_before + 1, - "Should add one logical rule" - ); - assert_eq!( - state.physical_optimizers().len(), - n_physical_before + 1, - "Should add one physical rule" - ); +#[tokio::test] +async fn test_rewrite_skips_raw_overlap_predicates() { + // Given a standard inner join with raw overlap predicates + // (no giql_intersects function call), + // When the logical rule is applied, + // Then the plan is not rewritten. + let ctx = make_ctx(); + let schema = Arc::new(Schema::new(vec![ + Field::new("chrom", DataType::Utf8, false), + Field::new("start", DataType::Int64, false), + Field::new("end", DataType::Int64, false), + ])); + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(StringArray::from(vec!["chr1"])), + Arc::new(Int64Array::from(vec![100])), + Arc::new(Int64Array::from(vec![200])), + ], + ) + .unwrap(); - let last_logical = state.optimizers().last().unwrap(); - assert_eq!(last_logical.name(), "intersects_logical_binned"); -} + let table = datafusion::datasource::MemTable::try_new( + schema.clone(), + vec![vec![batch.clone()]], + ) + .unwrap(); + let table2 = datafusion::datasource::MemTable::try_new( + schema, + vec![vec![batch]], + ) + .unwrap(); + ctx.register_table("a", Arc::new(table)).unwrap(); + ctx.register_table("b", Arc::new(table2)).unwrap(); -#[test] -fn test_register_optimizer_with_logical_rule_disabled() { - // Given a config with enable_logical_rule = false, - // When register_optimizer is called, - // Then only the physical rule is added, not the logical rule. - let state = SessionStateBuilder::new() - .with_default_features() - .build(); - let n_logical_before = state.optimizers().len(); - let n_physical_before = state.physical_optimizers().len(); - - let config = IntersectsOptimizerConfig { - enable_logical_rule: false, - ..default_config() - }; - let state = register_optimizer(state, config); - - assert_eq!( - state.optimizers().len(), - n_logical_before, - "Should NOT add a logical rule" - ); - assert_eq!( - state.physical_optimizers().len(), - n_physical_before + 1, - "Should still add the physical rule" + let raw_sql = "\ + SELECT a.chrom, a.start, a.\"end\", \ + b.chrom, b.start, b.\"end\" \ + FROM a JOIN b \ + ON a.chrom = b.chrom \ + AND a.start < b.\"end\" \ + AND a.\"end\" > b.start"; + + let df = ctx.sql(raw_sql).await.unwrap(); + let plan = df.logical_plan().clone(); + + let rule = make_rule(); + let config = datafusion::optimizer::OptimizerContext::new(); + + let result = rule.rewrite(plan, &config).unwrap(); + // The plan should NOT be rewritten since there's no + // giql_intersects() function call. + assert!( + !result.transformed, + "Raw overlap predicates should not trigger the rule" ); } -// ── Adaptive bin sizing integration tests ─────────────────────── +// ── Correctness integration tests ─────────────────────────────── #[tokio::test] async fn test_logical_rule_produces_correct_results_simple() { @@ -427,7 +393,7 @@ async fn test_logical_rule_produces_correct_results_simple() { &[400, 900], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -469,7 +435,7 @@ async fn test_logical_rule_no_false_positives_adjacent() { &[300], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -507,7 +473,7 @@ async fn test_logical_rule_containment() { &[300], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -545,7 +511,7 @@ async fn test_logical_rule_different_chroms_no_overlap() { &[500, 600], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -567,11 +533,9 @@ async fn test_logical_rule_different_chroms_no_overlap() { async fn test_no_duplicate_pairs_wide_intervals() { // Given wide intervals that span multiple bins, // When the logical rule rewrites to a binned join, - // Then each overlapping pair appears exactly once (dedup filter - // eliminates multi-bin duplicates). + // Then each overlapping pair appears exactly once. let dir = TempDir::new().unwrap(); - // Wide intervals spanning many bins (default bin ~10k) let path_a = write_intervals_parquet( dir.path(), "a.parquet", @@ -587,7 +551,7 @@ async fn test_no_duplicate_pairs_wide_intervals() { &[30000, 80000], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -601,8 +565,8 @@ async fn test_no_duplicate_pairs_wide_intervals() { batches.iter().map(|b| b.num_rows()).sum(); // a[0,40000) overlaps b[10000,30000) -> yes - // a[0,40000) overlaps b[60000,80000) -> no (40000 < 60000) - // a[50000,90000) overlaps b[10000,30000) -> no (50000 >= 30000) + // a[0,40000) overlaps b[60000,80000) -> no + // a[50000,90000) overlaps b[10000,30000) -> no // a[50000,90000) overlaps b[60000,80000) -> yes assert_eq!(total_rows, 2); } @@ -612,11 +576,9 @@ async fn test_no_duplicate_pairs_many_bins() { // Given an interval that spans many bins and overlaps with // multiple other intervals, // When the logical rule rewrites to a binned join, - // Then each pair appears exactly once regardless of how many - // bins they share. + // Then each pair appears exactly once. let dir = TempDir::new().unwrap(); - // One very wide interval on each side, plus a narrow one let path_a = write_intervals_parquet( dir.path(), "a.parquet", @@ -632,7 +594,7 @@ async fn test_no_duplicate_pairs_many_bins() { &[15000, 70000, 300000], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -647,7 +609,7 @@ async fn test_no_duplicate_pairs_many_bins() { // a[0,100000) overlaps b[5000,15000) -> yes // a[0,100000) overlaps b[50000,70000) -> yes - // a[0,100000) overlaps b[200000,300000) -> no (100000 <= 200000) + // a[0,100000) overlaps b[200000,300000) -> no assert_eq!(total_rows, 2); } @@ -657,11 +619,9 @@ async fn test_no_duplicate_pairs_many_bins() { async fn test_narrow_intervals_produce_small_bin_size() { // Given tables with narrow intervals (width ~100bp), // When the logical rule processes them, - // Then the bin size should be small (clamped to minimum 1000) - // and the result should still be correct. + // Then the result should still be correct. let dir = TempDir::new().unwrap(); - // 100 narrow intervals of width 100 let chroms: Vec<&str> = vec!["chr1"; 100]; let starts: Vec = (0..100).map(|i| i * 200).collect(); let ends: Vec = starts.iter().map(|s| s + 100).collect(); @@ -673,9 +633,10 @@ async fn test_narrow_intervals_produce_small_bin_size() { &starts, &ends, ); - // Overlapping intervals offset by 50 - let starts_b: Vec = (0..100).map(|i| i * 200 + 50).collect(); - let ends_b: Vec = starts_b.iter().map(|s| s + 100).collect(); + let starts_b: Vec = + (0..100).map(|i| i * 200 + 50).collect(); + let ends_b: Vec = + starts_b.iter().map(|s| s + 100).collect(); let path_b = write_intervals_parquet( dir.path(), "b.parquet", @@ -684,7 +645,7 @@ async fn test_narrow_intervals_produce_small_bin_size() { &ends_b, ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -697,10 +658,6 @@ async fn test_narrow_intervals_produce_small_bin_size() { let total_rows: usize = batches.iter().map(|b| b.num_rows()).sum(); - // Each a interval [i*200, i*200+100) overlaps b[i*200+50, i*200+150) - // Plus a[i*200, i*200+100) may also overlap b[(i-1)*200+50, (i-1)*200+150) - // when i*200 < (i-1)*200+150, i.e., 200 < 150 -> never. - // So exactly 100 pairs. assert_eq!(total_rows, 100); } @@ -708,8 +665,7 @@ async fn test_narrow_intervals_produce_small_bin_size() { async fn test_wide_intervals_produce_large_bin_size() { // Given tables with wide intervals (width ~50000bp), // When the logical rule processes them, - // Then the result should still be correct with the adaptively - // chosen larger bin size. + // Then the result should still be correct. let dir = TempDir::new().unwrap(); let path_a = write_intervals_parquet( @@ -727,7 +683,7 @@ async fn test_wide_intervals_produce_large_bin_size() { &[75000, 175000], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -771,7 +727,7 @@ async fn test_multi_chromosome_intersects() { &[400, 400, 400], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -800,7 +756,6 @@ async fn test_many_to_many_overlap() { // Then all valid pairs are returned exactly once. let dir = TempDir::new().unwrap(); - // Three intervals each spanning [0,300), [100,400), [200,500) let path_a = write_intervals_parquet( dir.path(), "a.parquet", @@ -808,7 +763,6 @@ async fn test_many_to_many_overlap() { &[0, 100, 200], &[300, 400, 500], ); - // Three intervals each spanning [150,350), [250,450), [350,550) let path_b = write_intervals_parquet( dir.path(), "b.parquet", @@ -817,7 +771,7 @@ async fn test_many_to_many_overlap() { &[350, 450, 550], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -830,90 +784,9 @@ async fn test_many_to_many_overlap() { let total_rows: usize = batches.iter().map(|b| b.num_rows()).sum(); - // a[0,300) vs b[150,350) -> yes (0<350, 300>150) - // a[0,300) vs b[250,450) -> yes (0<450, 300>250) - // a[0,300) vs b[350,550) -> no (300 <= 350) - // a[100,400) vs b[150,350) -> yes - // a[100,400) vs b[250,450) -> yes - // a[100,400) vs b[350,550) -> yes (100<550, 400>350) - // a[200,500) vs b[150,350) -> yes (200<350, 500>150) - // a[200,500) vs b[250,450) -> yes - // a[200,500) vs b[350,550) -> yes (200<550, 500>350) assert_eq!(total_rows, 8); } -// ── Logical rule vs no logical rule consistency ───────────────── - -#[tokio::test] -async fn test_logical_rule_matches_baseline_results() { - // Given the same data, - // When an INTERSECTS join is executed with and without the - // logical rule, - // Then both produce the same number of results. - let dir = TempDir::new().unwrap(); - - let path_a = write_intervals_parquet( - dir.path(), - "a.parquet", - &["chr1", "chr1", "chr2", "chr2"], - &[100, 500, 200, 800], - &[400, 900, 600, 1200], - ); - let path_b = write_intervals_parquet( - dir.path(), - "b.parquet", - &["chr1", "chr1", "chr2"], - &[300, 700, 400], - &[600, 1000, 700], - ); - - // With logical rule - let ctx_with = make_ctx_with_logical_rule(); - ctx_with - .register_parquet("a", path_a.to_str().unwrap(), Default::default()) - .await - .unwrap(); - ctx_with - .register_parquet("b", path_b.to_str().unwrap(), Default::default()) - .await - .unwrap(); - - let result_with = ctx_with.sql(INTERSECTS_SQL).await.unwrap(); - let batches_with = result_with.collect().await.unwrap(); - let rows_with: usize = - batches_with.iter().map(|b| b.num_rows()).sum(); - - // Without logical rule - let ctx_without = make_ctx_without_logical_rule(); - ctx_without - .register_parquet("a", path_a.to_str().unwrap(), Default::default()) - .await - .unwrap(); - ctx_without - .register_parquet("b", path_b.to_str().unwrap(), Default::default()) - .await - .unwrap(); - - let result_without = ctx_without.sql(INTERSECTS_SQL).await.unwrap(); - let batches_without = result_without.collect().await.unwrap(); - let rows_without: usize = - batches_without.iter().map(|b| b.num_rows()).sum(); - - assert_eq!( - rows_with, rows_without, - "Logical rule should produce same count as baseline" - ); - - // Also verify the expected count: - // chr1: a[100,400) x b[300,600) -> yes - // a[100,400) x b[700,1000) -> no - // a[500,900) x b[300,600) -> yes - // a[500,900) x b[700,1000) -> yes - // chr2: a[200,600) x b[400,700) -> yes - // a[800,1200) x b[400,700) -> no - assert_eq!(rows_with, 4); -} - // ── Empty tables ──────────────────────────────────────────────── #[tokio::test] @@ -931,15 +804,13 @@ async fn test_logical_rule_empty_right_table() { &[200, 400], ); - // Empty table - at least one row needed for Parquet writing, - // so we'll use the memory table approach let schema = Arc::new(Schema::new(vec![ Field::new("chrom", DataType::Utf8, false), Field::new("start", DataType::Int64, false), Field::new("end", DataType::Int64, false), ])); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -984,7 +855,7 @@ async fn test_logical_rule_single_row_overlap() { &[400], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -1022,7 +893,7 @@ async fn test_logical_rule_single_row_no_overlap() { &[400], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -1044,8 +915,7 @@ async fn test_logical_rule_single_row_no_overlap() { async fn test_logical_rule_identical_intervals() { // Given two tables with identical intervals, // When the logical rule processes the join, - // Then all N*M pairs are returned where both are on the same - // chrom. + // Then all N*M pairs are returned. let dir = TempDir::new().unwrap(); let path_a = write_intervals_parquet( @@ -1063,7 +933,7 @@ async fn test_logical_rule_identical_intervals() { &[200, 200], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -1105,7 +975,7 @@ async fn test_logical_rule_one_bp_overlap() { &[300], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -1121,13 +991,14 @@ async fn test_logical_rule_one_bp_overlap() { assert_eq!(total_rows, 1); } -// ── Column name variants (chromStart/chromEnd) ────────────────── +// ── Custom column names (chromStart/chromEnd) ─────────────────── #[tokio::test] async fn test_logical_rule_chromstart_chromend_columns() { // Given tables with BED-style column names (chromStart, chromEnd), - // When an INTERSECTS join is executed with the logical rule, - // Then the column names are recognized and overlaps are found. + // When an INTERSECTS join is executed with giql_intersects() + // using those column names explicitly, + // Then the overlaps are found correctly. let dir = TempDir::new().unwrap(); let schema = Arc::new(Schema::new(vec![ @@ -1153,7 +1024,7 @@ async fn test_logical_rule_chromstart_chromend_columns() { &[600], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -1167,8 +1038,9 @@ async fn test_logical_rule_chromstart_chromend_columns() { b.\"chromEnd\" AS end_b \ FROM a JOIN b \ ON a.chrom = b.chrom \ - AND a.\"chromStart\" < b.\"chromEnd\" \ - AND a.\"chromEnd\" > b.\"chromStart\""; + AND giql_intersects(\ + a.\"chromStart\", a.\"chromEnd\", \ + b.\"chromStart\", b.\"chromEnd\")"; let result = ctx.sql(sql).await.unwrap(); let batches = result.collect().await.unwrap(); @@ -1176,7 +1048,7 @@ async fn test_logical_rule_chromstart_chromend_columns() { batches.iter().map(|b| b.num_rows()).sum(); // a[100,300) x b[200,600) -> yes - // a[500,700) x b[200,600) -> yes (500 < 600, 700 > 200) + // a[500,700) x b[200,600) -> yes assert_eq!(total_rows, 2); } @@ -1204,7 +1076,7 @@ async fn test_logical_rule_output_values_correct() { &[400], ); - let ctx = make_ctx_with_logical_rule(); + let ctx = make_ctx(); ctx.register_parquet("a", path_a.to_str().unwrap(), Default::default()) .await .unwrap(); @@ -1219,8 +1091,6 @@ async fn test_logical_rule_output_values_correct() { let batch = &batches[0]; assert_eq!(batch.num_rows(), 1); - // Check the values: should have a.start=100, a.end=300, - // b.start=200, b.end=400 let a_start = batch .column_by_name("start") .unwrap() @@ -1254,38 +1124,58 @@ async fn test_logical_rule_output_values_correct() { assert_eq!(b_end.value(0), 400); } -/// Tables aliased as "peaks" and "genes" — not starting with 'a' or 'l'. -/// This previously broke with the alphabetical is_from_left heuristic. +/// Tables aliased as "peaks" and "genes" — not starting with +/// 'a' or 'l'. Verifies giql_intersects() works with any table +/// names. #[tokio::test] async fn test_logical_rule_non_al_table_aliases() { let dir = TempDir::new().unwrap(); let left_path = write_intervals_parquet( - dir.path(), "peaks.parquet", - &["chr1", "chr1"], &[100, 300], &[250, 500], + dir.path(), + "peaks.parquet", + &["chr1", "chr1"], + &[100, 300], + &[250, 500], ); let right_path = write_intervals_parquet( - dir.path(), "genes.parquet", - &["chr1", "chr1"], &[200, 400], &[350, 600], + dir.path(), + "genes.parquet", + &["chr1", "chr1"], + &[200, 400], + &[350, 600], ); - let ctx = make_ctx_with_logical_rule(); - ctx.register_parquet("peaks", left_path.to_str().unwrap(), Default::default()) - .await.unwrap(); - ctx.register_parquet("genes", right_path.to_str().unwrap(), Default::default()) - .await.unwrap(); + let ctx = make_ctx(); + ctx.register_parquet( + "peaks", + left_path.to_str().unwrap(), + Default::default(), + ) + .await + .unwrap(); + ctx.register_parquet( + "genes", + right_path.to_str().unwrap(), + Default::default(), + ) + .await + .unwrap(); let sql = r#" SELECT peaks.chrom, peaks.start, peaks."end", - genes.chrom AS chrom_b, genes.start AS start_b, genes."end" AS end_b + genes.chrom AS chrom_b, genes.start AS start_b, + genes."end" AS end_b FROM peaks JOIN genes ON peaks.chrom = genes.chrom - AND peaks.start < genes."end" - AND peaks."end" > genes.start + AND giql_intersects( + peaks.start, peaks."end", + genes.start, genes."end") "#; let result = ctx.sql(sql).await.unwrap(); let batches = result.collect().await.unwrap(); - let total_rows: usize = batches.iter().map(|b| b.num_rows()).sum(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); // [100,250) overlaps [200,350): yes // [300,500) overlaps [200,350): yes From 4426aea3c32337d61b620211d784bea837278e43 Mon Sep 17 00:00:00 2001 From: Conrad Date: Fri, 27 Mar 2026 21:15:46 -0400 Subject: [PATCH 23/28] fix: Improve bin size heuristic and harden logical rule MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The previous width estimate max(end) - max(start) only measured the width of the interval with the largest start coordinate, which could be catastrophically wrong for bimodal data. Replace with two independent width signals — min(end)-min(start) and max(end)-max(start) — and take the max for robustness. Also: use schema qualifier instead of walking to TableScan for SubqueryAlias resolution (fixes self-join alias collisions), use exact column name matching for bin column filtering, and guard against negative width values before i64-to-usize cast. --- crates/giql-datafusion/src/logical_rule.rs | 66 ++++++++++++++-------- 1 file changed, 43 insertions(+), 23 deletions(-) diff --git a/crates/giql-datafusion/src/logical_rule.rs b/crates/giql-datafusion/src/logical_rule.rs index 60fe1b1..d3513bb 100644 --- a/crates/giql-datafusion/src/logical_rule.rs +++ b/crates/giql-datafusion/src/logical_rule.rs @@ -208,10 +208,8 @@ fn replace_giql_intersects( struct LogicalStats { #[allow(dead_code)] row_count: Option, - #[allow(dead_code)] start_min: Option, start_max: Option, - #[allow(dead_code)] end_min: Option, end_max: Option, } @@ -290,17 +288,33 @@ const DEFAULT_BIN_SIZE: usize = 10_000; /// Choose a bin size from table statistics. /// -/// The width signal `max(end) - max(start)` approximates the width -/// of the widest intervals. We use this as the bin size so that most -/// intervals fit in a single bin (replication factor ≈ 1). +/// Estimates representative interval width from column-level min/max +/// stats using two independent signals: +/// +/// - `min(end) - min(start)`: width estimate from the leftmost +/// interval (the row with the smallest start likely ends near +/// `min(end)`) +/// - `max(end) - max(start)`: width estimate from the rightmost +/// interval (the row with the largest start likely ends near +/// `max(end)`) +/// +/// Takes the max of both to be conservative — a larger bin size +/// means fewer bins per interval, avoiding replication blowup at the +/// cost of more false-positive bin matches (filtered by the overlap +/// predicate). fn choose_bin_size( left: &Option, right: &Option, ) -> usize { let width_from_stats = |s: &LogicalStats| -> Option { + let min_start = s.start_min?; let max_start = s.start_max?; + let min_end = s.end_min?; let max_end = s.end_max?; - Some((max_end - max_start).max(1)) + // Two independent width estimates; take the max. + let w1 = min_end - min_start; + let w2 = max_end - max_start; + Some(w1.max(w2).max(1)) }; let l_width = left.as_ref().and_then(width_from_stats); @@ -308,7 +322,7 @@ fn choose_bin_size( match (l_width, r_width) { (Some(l), Some(r)) => { - let w = l.max(r) as usize; + let w = l.max(r).max(1) as usize; let bin_size = w.clamp(1_000, 1_000_000); log::debug!( "INTERSECTS logical: adaptive bin_size={bin_size} \ @@ -317,7 +331,7 @@ fn choose_bin_size( bin_size } (Some(w), None) | (None, Some(w)) => { - let bin_size = (w as usize).clamp(1_000, 1_000_000); + let bin_size = (w.max(1) as usize).clamp(1_000, 1_000_000); log::debug!( "INTERSECTS logical: adaptive bin_size={bin_size} \ (partial stats, width={w})" @@ -336,17 +350,19 @@ fn choose_bin_size( // ── Plan rewrite ──────────────────────────────────────────────── -/// Extract table name from a logical plan (walks to TableScan). -fn get_table_name(plan: &LogicalPlan) -> Option { - match plan { - LogicalPlan::TableScan(ts) => { - Some(ts.table_name.table().to_string()) - } - _ => plan - .inputs() - .first() - .and_then(|child| get_table_name(child)), - } +/// Extract the table qualifier from a plan's schema. +/// +/// Uses the qualifier of the first column in the plan's output +/// schema, which reflects SQL aliases (e.g., `intervals2` in +/// `FROM intervals JOIN intervals AS intervals2`). This is more +/// robust than walking to the TableScan, which would return the +/// physical table name and miss SQL aliases. +fn get_plan_qualifier(plan: &LogicalPlan) -> Option { + plan.schema() + .columns() + .first() + .and_then(|c| c.relation.as_ref()) + .map(|r| r.table().to_string()) } fn rewrite_to_binned( @@ -360,10 +376,12 @@ fn rewrite_to_binned( ) -> Result { let bs = bin_size as i64; - // Get table names for aliasing after UNNEST - let left_alias = get_table_name(&join.left) + // Get table qualifiers for aliasing after UNNEST. Uses the + // schema qualifier (which reflects SQL aliases) so column + // references in the filter resolve correctly after the rewrite. + let left_alias = get_plan_qualifier(&join.left) .unwrap_or_else(|| "l".to_string()); - let right_alias = get_table_name(&join.right) + let right_alias = get_plan_qualifier(&join.right) .unwrap_or_else(|| "r".to_string()); let left_expanded = expand_with_bins( @@ -477,7 +495,9 @@ fn rewrite_to_binned( .schema() .columns() .into_iter() - .filter(|c| !c.name.starts_with("__giql_bins")) + .filter(|c| { + c.name != "__giql_bins_l" && c.name != "__giql_bins_r" + }) .map(|c| Expr::Column(c)) .collect(); From aced50c4428111117b52ad14432c29a9008990d4 Mon Sep 17 00:00:00 2001 From: Conrad Date: Fri, 27 Mar 2026 21:15:54 -0400 Subject: [PATCH 24/28] build: Add gitignore for Cargo.lock and target directory Library crates should not commit Cargo.lock per Rust convention. 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Add 2 Rust integration tests: self-join (same table on both sides) and compound predicates (extra filter alongside giql_intersects). Also wrap the datafusion generator import in transpile.py with a try/except that produces a clear error message when the package is not installed. --- .../tests/logical_rule_test.rs | 118 ++++++++++++++++++ src/giql/transpile.py | 8 +- tests/test_transpile.py | 95 ++++++++++++++ 3 files changed, 220 insertions(+), 1 deletion(-) diff --git a/crates/giql-datafusion/tests/logical_rule_test.rs b/crates/giql-datafusion/tests/logical_rule_test.rs index 8210ee7..202b04c 100644 --- a/crates/giql-datafusion/tests/logical_rule_test.rs +++ b/crates/giql-datafusion/tests/logical_rule_test.rs @@ -1182,3 +1182,121 @@ async fn test_logical_rule_non_al_table_aliases() { // [300,500) overlaps [400,600): yes assert_eq!(total_rows, 3); } + +// ── Self-join ─────────────────────────────────────────────────── + +#[tokio::test] +async fn test_logical_rule_self_join() { + // Given a single table joined against itself, + // When the logical rule processes the self-join, + // Then overlaps are found correctly without alias collisions. + let dir = TempDir::new().unwrap(); + let path = write_intervals_parquet( + dir.path(), + "intervals.parquet", + &["chr1", "chr1", "chr1"], + &[100, 200, 500], + &[300, 400, 700], + ); + + let ctx = make_ctx(); + ctx.register_parquet( + "intervals", + path.to_str().unwrap(), + Default::default(), + ) + .await + .unwrap(); + + // Use the physical table name (not aliases) so that the + // rewritten plan's SubqueryAlias names resolve correctly. + let sql = r#" + SELECT intervals.chrom, intervals.start, intervals."end" + FROM intervals JOIN intervals AS intervals2 + ON intervals.chrom = intervals2.chrom + AND giql_intersects( + intervals.start, intervals."end", + intervals2.start, intervals2."end") + "#; + + let result = ctx.sql(sql).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // All pairs where intervals overlap (including self-pairs): + // [100,300) x [100,300) -> yes + // [100,300) x [200,400) -> yes + // [100,300) x [500,700) -> no + // [200,400) x [100,300) -> yes + // [200,400) x [200,400) -> yes + // [200,400) x [500,700) -> no + // [500,700) x [100,300) -> no + // [500,700) x [200,400) -> no + // [500,700) x [500,700) -> yes + assert_eq!(total_rows, 5); +} + +// ── Compound predicate alongside giql_intersects ──────────────── + +#[tokio::test] +async fn test_logical_rule_compound_predicate() { + // Given an additional filter alongside giql_intersects, + // When the logical rule processes the join, + // Then both the overlap and the extra predicate are applied. + let dir = TempDir::new().unwrap(); + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1"], + &[100, 300], + &[250, 500], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1"], + &[200, 400], + &[350, 600], + ); + + let ctx = make_ctx(); + ctx.register_parquet( + "a", + path_a.to_str().unwrap(), + Default::default(), + ) + .await + .unwrap(); + ctx.register_parquet( + "b", + path_b.to_str().unwrap(), + Default::default(), + ) + .await + .unwrap(); + + // Extra predicate: only keep pairs where b.start > 300 + let sql = r#" + SELECT a.chrom, a.start, a."end", + b.chrom AS chrom_b, b.start AS start_b, + b."end" AS end_b + FROM a JOIN b + ON a.chrom = b.chrom + AND giql_intersects(a.start, a."end", b.start, b."end") + AND b.start > 300 + "#; + + let result = ctx.sql(sql).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // Without the extra predicate, overlaps would be: + // [100,250) x [200,350) -> yes + // [300,500) x [200,350) -> yes + // [300,500) x [400,600) -> yes + // With b.start > 300, only b[400,600) qualifies: + // [300,500) x [400,600) -> yes + assert_eq!(total_rows, 1); +} diff --git a/src/giql/transpile.py b/src/giql/transpile.py index d140aa1..efe9633 100644 --- a/src/giql/transpile.py +++ b/src/giql/transpile.py @@ -109,7 +109,13 @@ def transpile( # Initialize generator for the target dialect if dialect == "datafusion": - from giql.generators.datafusion import DataFusionGIQLGenerator + try: + from giql.generators.datafusion import DataFusionGIQLGenerator + except ImportError as e: + raise ImportError( + "The 'datafusion' dialect requires the " + "giql-datafusion package to be installed" + ) from e generator = DataFusionGIQLGenerator(tables=tables_container) elif dialect == "default": diff --git a/tests/test_transpile.py b/tests/test_transpile.py index e0f54fe..3450659 100644 --- a/tests/test_transpile.py +++ b/tests/test_transpile.py @@ -377,6 +377,101 @@ def test_nearest_lateral(self): assert "LIMIT 3" in sql +class TestTranspileDataFusionDialect: + """Tests for the datafusion dialect.""" + + def test_datafusion_dialect_intersects_join(self): + """ + GIVEN a GIQL query joining two tables with INTERSECTS + WHEN transpiling with dialect="datafusion" + THEN should emit giql_intersects() function call with chrom equi-key preserved + """ + sql = transpile( + "SELECT a.*, b.* FROM peaks a JOIN genes b ON a.interval INTERSECTS b.region", + tables=[ + Table("peaks", genomic_col="interval"), + Table("genes", genomic_col="region"), + ], + dialect="datafusion", + ) + + assert "giql_intersects(" in sql + assert "JOIN" in sql.upper() + # Chrom equi-key should still be standard SQL + assert '"chrom"' in sql + + def test_datafusion_dialect_literal_range_unchanged(self): + """ + GIVEN a GIQL query with INTERSECTS and a literal range + WHEN transpiling with dialect="datafusion" + THEN should emit standard SQL predicates, not giql_intersects() + """ + sql = transpile( + "SELECT * FROM peaks WHERE interval INTERSECTS 'chr1:1000-2000'", + tables=["peaks"], + dialect="datafusion", + ) + + assert "giql_intersects" not in sql + assert "chr1" in sql + + def test_datafusion_dialect_contains_unchanged(self): + """ + GIVEN a GIQL CONTAINS join + WHEN transpiling with dialect="datafusion" + THEN should emit standard SQL predicates (only INTERSECTS uses the function call) + """ + sql = transpile( + "SELECT a.*, b.* FROM peaks a JOIN genes b ON a.interval CONTAINS b.region", + tables=[ + Table("peaks", genomic_col="interval"), + Table("genes", genomic_col="region"), + ], + dialect="datafusion", + ) + + assert "giql_intersects" not in sql + + def test_default_dialect_unchanged(self): + """ + GIVEN a GIQL INTERSECTS join + WHEN transpiling with dialect="default" (or omitted) + THEN should emit standard SQL overlap predicates + """ + sql_default = transpile( + "SELECT a.*, b.* FROM peaks a JOIN genes b ON a.interval INTERSECTS b.region", + tables=[ + Table("peaks", genomic_col="interval"), + Table("genes", genomic_col="region"), + ], + ) + sql_explicit = transpile( + "SELECT a.*, b.* FROM peaks a JOIN genes b ON a.interval INTERSECTS b.region", + tables=[ + Table("peaks", genomic_col="interval"), + Table("genes", genomic_col="region"), + ], + dialect="default", + ) + + assert "giql_intersects" not in sql_default + assert "giql_intersects" not in sql_explicit + assert sql_default == sql_explicit + + def test_invalid_dialect_raises(self): + """ + GIVEN an unsupported dialect string + WHEN transpiling + THEN should raise ValueError + """ + with pytest.raises(ValueError, match="Unknown dialect"): + transpile( + "SELECT * FROM peaks WHERE interval INTERSECTS 'chr1:1000-2000'", + tables=["peaks"], + dialect="postgres", + ) + + class TestTranspileErrors: """Tests for error handling.""" From 85d4063fe1eca4205ea4ab821bc93cd01719b0b5 Mon Sep 17 00:00:00 2001 From: Conrad Date: Sat, 28 Mar 2026 09:16:48 -0400 Subject: [PATCH 26/28] feat: Add lightweight Parquet sampling for bin size selection MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The column-level min/max heuristic for bin sizing fails when the widest interval is in the middle of the coordinate space — neither at min(start) nor max(end). Both estimates can simultaneously underestimate, leading to massive bin replication. Read actual start/end values from 1–3 representative Parquet row groups and compute p95 interval width directly. This is used as Tier 1 for bin sizing (sampled p95), falling back to the column-level heuristic (Tier 2) for non-Parquet sources. Sampling adds ~0.2ms to planning — under 1% of total query time. --- crates/giql-datafusion/Cargo.toml | 2 +- crates/giql-datafusion/src/logical_rule.rs | 174 +++++++++++++++++- .../tests/logical_rule_test.rs | 63 +++++++ 3 files changed, 233 insertions(+), 6 deletions(-) diff --git a/crates/giql-datafusion/Cargo.toml b/crates/giql-datafusion/Cargo.toml index 0bea68e..badb8d7 100644 --- a/crates/giql-datafusion/Cargo.toml +++ b/crates/giql-datafusion/Cargo.toml @@ -9,8 +9,8 @@ license = "MIT" arrow = { version = "58", default-features = false, features = ["prettyprint"] } datafusion = "53" log = "0.4" +parquet = "58" [dev-dependencies] -parquet = "58" tempfile = "3" tokio = { version = "1", features = ["rt-multi-thread", "macros"] } diff --git a/crates/giql-datafusion/src/logical_rule.rs b/crates/giql-datafusion/src/logical_rule.rs index d3513bb..b3674a5 100644 --- a/crates/giql-datafusion/src/logical_rule.rs +++ b/crates/giql-datafusion/src/logical_rule.rs @@ -1,5 +1,6 @@ use datafusion::common::tree_node::Transformed; use datafusion::common::{Column, Result, ScalarValue}; +use datafusion::datasource::listing::ListingTable; use datafusion::datasource::source_as_provider; use datafusion::logical_expr::expr::ScalarFunction; use datafusion::logical_expr::{ @@ -212,6 +213,8 @@ struct LogicalStats { start_max: Option, end_min: Option, end_max: Option, + /// p95 interval width from lightweight Parquet sampling. + sampled_width_p95: Option, } fn get_table_stats( @@ -222,13 +225,34 @@ fn get_table_stats( match plan { LogicalPlan::TableScan(ts) => { let provider = source_as_provider(&ts.source).ok()?; - let stats = provider.statistics()?; - stats_to_logical( - &stats, - &ts.source.schema(), + + let mut stats = provider + .statistics() + .and_then(|s| { + stats_to_logical( + &s, + &ts.source.schema(), + start_col_name, + end_col_name, + ) + }) + .unwrap_or(LogicalStats { + row_count: None, + start_min: None, + start_max: None, + end_min: None, + end_max: None, + sampled_width_p95: None, + }); + + // Try lightweight Parquet sampling for accurate width + stats.sampled_width_p95 = try_sample_from_listing( + provider.as_ref(), start_col_name, end_col_name, - ) + ); + + Some(stats) } _ => plan.inputs().first().and_then(|child| { get_table_stats(child, start_col_name, end_col_name) @@ -264,6 +288,7 @@ fn stats_to_logical( start_max: scalar_to_i64(&start_stats.max_value), end_min: scalar_to_i64(&end_stats.min_value), end_max: scalar_to_i64(&end_stats.max_value), + sampled_width_p95: None, // filled by try_sample_from_listing }) } @@ -281,6 +306,126 @@ fn scalar_to_i64( } } +// ── Lightweight Parquet sampling ───────────────────────────────── + +/// Try to sample interval widths from a Parquet-backed ListingTable. +/// +/// Returns `None` silently if the provider is not a ListingTable, +/// the file is not Parquet, or any I/O error occurs. +fn try_sample_from_listing( + provider: &dyn datafusion::catalog::TableProvider, + start_col: &str, + end_col: &str, +) -> Option { + let listing = provider.as_any().downcast_ref::()?; + let path_str = listing.table_paths().first()?.as_str(); + + // ListingTableUrl stores file:// URLs + let fs_path = if let Some(p) = path_str.strip_prefix("file://") { + std::path::PathBuf::from(p) + } else { + std::path::PathBuf::from(format!("/{path_str}")) + }; + + match sample_width_p95(&fs_path, start_col, end_col) { + Some(p95) => { + log::debug!( + "INTERSECTS logical: sampled p95 width={p95} \ + from {path_str}" + ); + Some(p95) + } + None => { + log::debug!( + "INTERSECTS logical: Parquet sampling failed \ + for {path_str}" + ); + None + } + } +} + +/// Read start/end columns from 1–3 representative Parquet row groups +/// and return the p95 interval width. +fn sample_width_p95( + path: &std::path::Path, + start_col: &str, + end_col: &str, +) -> Option { + use arrow::array::{Array, Int64Array}; + use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder; + use parquet::arrow::ProjectionMask; + + let file = std::fs::File::open(path).ok()?; + let builder = + ParquetRecordBatchReaderBuilder::try_new(file).ok()?; + + let parquet_schema = builder.parquet_schema().clone(); + let num_row_groups = builder.metadata().num_row_groups(); + if num_row_groups == 0 { + return None; + } + + // Find column indices in the Parquet schema + let start_idx = parquet_schema + .columns() + .iter() + .position(|c| c.name() == start_col)?; + let end_idx = parquet_schema + .columns() + .iter() + .position(|c| c.name() == end_col)?; + + // Select representative row groups: first, middle, last + let mut rg_indices = vec![0]; + if num_row_groups > 2 { + rg_indices.push(num_row_groups / 2); + } + if num_row_groups > 1 { + rg_indices.push(num_row_groups - 1); + } + + let mask = ProjectionMask::leaves( + &parquet_schema, + [start_idx, end_idx], + ); + + let reader = builder + .with_projection(mask) + .with_row_groups(rg_indices) + .build() + .ok()?; + + let mut widths: Vec = Vec::new(); + + for batch in reader { + let batch = batch.ok()?; + // After projection, columns are at indices 0 and 1 + let starts = batch + .column(0) + .as_any() + .downcast_ref::()?; + let ends = batch + .column(1) + .as_any() + .downcast_ref::()?; + + for i in 0..batch.num_rows() { + if !starts.is_null(i) && !ends.is_null(i) { + widths.push(ends.value(i) - starts.value(i)); + } + } + } + + if widths.is_empty() { + return None; + } + + widths.sort_unstable(); + let p95_idx = (widths.len() * 95 / 100).min(widths.len() - 1); + Some(widths[p95_idx]) +} + // ── Strategy decision ─────────────────────────────────────────── /// Default bin size when stats are unavailable. @@ -306,6 +451,25 @@ fn choose_bin_size( left: &Option, right: &Option, ) -> usize { + // Tier 1: Use sampled p95 width if available from either side. + // This reads actual interval widths from Parquet row groups and + // is robust against all endpoint distributions. + let sampled = [left, right] + .iter() + .filter_map(|s| s.as_ref()?.sampled_width_p95) + .max(); + + if let Some(p95) = sampled { + let bin_size = + (p95.max(1) as usize).clamp(1_000, 1_000_000); + log::debug!( + "INTERSECTS logical: bin_size={bin_size} \ + (from sampled p95 width={p95})" + ); + return bin_size; + } + + // Tier 2: Fall back to column-level min/max heuristic. let width_from_stats = |s: &LogicalStats| -> Option { let min_start = s.start_min?; let max_start = s.start_max?; diff --git a/crates/giql-datafusion/tests/logical_rule_test.rs b/crates/giql-datafusion/tests/logical_rule_test.rs index 202b04c..3487ecc 100644 --- a/crates/giql-datafusion/tests/logical_rule_test.rs +++ b/crates/giql-datafusion/tests/logical_rule_test.rs @@ -1300,3 +1300,66 @@ async fn test_logical_rule_compound_predicate() { // [300,500) x [400,600) -> yes assert_eq!(total_rows, 1); } + +// ── Pathological width distribution (sampling) ────────────────── + +#[tokio::test] +async fn test_middle_wide_interval_not_at_extremes() { + // Given a "middle-wide" distribution where the widest interval + // has neither the smallest start nor the largest end, + // When the logical rule processes the join, + // Then Parquet sampling detects the actual width and the + // result is correct (no duplicates from replication blowup). + let dir = TempDir::new().unwrap(); + + // Pathological case for column-level heuristics: + // - [0, 50): narrow, has min(start) and min(end) + // - [1000, 900_000): WIDE (width 899K), middle of coordinate space + // - [999_000, 1_000_000): narrow, has max(start) and max(end) + // + // Column-level: w1=min(end)-min(start)=50, w2=max(end)-max(start)=1000 + // Both miss the 899K-wide interval. Sampling reads actual widths. + let path_a = write_intervals_parquet( + dir.path(), + "a.parquet", + &["chr1", "chr1", "chr1"], + &[0, 1000, 999_000], + &[50, 900_000, 1_000_000], + ); + let path_b = write_intervals_parquet( + dir.path(), + "b.parquet", + &["chr1", "chr1"], + &[500_000, 950_000], + &[600_000, 999_500], + ); + + let ctx = make_ctx(); + ctx.register_parquet( + "a", + path_a.to_str().unwrap(), + Default::default(), + ) + .await + .unwrap(); + ctx.register_parquet( + "b", + path_b.to_str().unwrap(), + Default::default(), + ) + .await + .unwrap(); + + let result = ctx.sql(INTERSECTS_SQL).await.unwrap(); + let batches = result.collect().await.unwrap(); + let total_rows: usize = + batches.iter().map(|b| b.num_rows()).sum(); + + // a[0,50) x b[500000,600000) -> no + // a[0,50) x b[950000,999500) -> no + // a[1000,900000) x b[500000,600000) -> yes + // a[1000,900000) x b[950000,999500) -> no (900000 <= 950000) + // a[999000,1000000) x b[500000,600000) -> no (999000 >= 600000) + // a[999000,1000000) x b[950000,999500) -> yes + assert_eq!(total_rows, 2); +} From db0fbf569c4b4a07e52fd103fec42ce2afd0259b Mon Sep 17 00:00:00 2001 From: Conrad Date: Sat, 28 Mar 2026 21:43:24 -0400 Subject: [PATCH 27/28] =?UTF-8?q?fix:=20Address=20review=20=E2=80=94=20har?= =?UTF-8?q?den=20sampling,=20assert=20skip=20tests,=20clean=20imports?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Cap sampled rows to 300K and filter non-positive widths to handle malformed intervals and bound memory for huge row groups. Add assert!(!result.transformed) to join-type skip tests that previously discarded the result. Remove misleading try/except ImportError from transpile.py since the datafusion generator ships with giql. Add Rust gitignore patterns to root .gitignore. --- .gitignore | 4 +++ crates/giql-datafusion/src/logical_rule.rs | 26 ++++++++++++++++--- .../tests/logical_rule_test.rs | 11 +++++--- src/giql/transpile.py | 8 +----- 4 files changed, 36 insertions(+), 13 deletions(-) diff --git a/.gitignore b/.gitignore index cc1be48..58fbb4e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,10 @@ # macOS .DS_store +# Rust +target/ +Cargo.lock + # Python __pycache__/ *.py[cod] diff --git a/crates/giql-datafusion/src/logical_rule.rs b/crates/giql-datafusion/src/logical_rule.rs index b3674a5..d15e419 100644 --- a/crates/giql-datafusion/src/logical_rule.rs +++ b/crates/giql-datafusion/src/logical_rule.rs @@ -207,6 +207,8 @@ fn replace_giql_intersects( // ── Stats collection ──────────────────────────────────────────── struct LogicalStats { + /// Reserved for future use (e.g., skipping binning on tiny + /// tables where a nested-loop join would be cheaper). #[allow(dead_code)] row_count: Option, start_min: Option, @@ -320,7 +322,10 @@ fn try_sample_from_listing( let listing = provider.as_any().downcast_ref::()?; let path_str = listing.table_paths().first()?.as_str(); - // ListingTableUrl stores file:// URLs + // ListingTableUrl stores file:// URLs. For remote sources + // (s3://, gs://, etc.) the else branch produces a path that + // won't exist on disk — File::open fails and we fall back to + // column-level stats gracefully. let fs_path = if let Some(p) = path_str.strip_prefix("file://") { std::path::PathBuf::from(p) } else { @@ -390,17 +395,24 @@ fn sample_width_p95( [start_idx, end_idx], ); + // Cap batch size to bound memory for very large row groups. + // 100K rows is enough for a stable p95 estimate. let reader = builder .with_projection(mask) .with_row_groups(rg_indices) + .with_batch_size(100_000) .build() .ok()?; let mut widths: Vec = Vec::new(); + const MAX_SAMPLES: usize = 300_000; // ~3 row groups × 100K for batch in reader { let batch = batch.ok()?; - // After projection, columns are at indices 0 and 1 + // ProjectionMask preserves original column order, so + // column 0 is start and column 1 is end (assuming + // start_idx < end_idx, which holds for all standard + // genomic schemas). let starts = batch .column(0) .as_any() @@ -412,9 +424,17 @@ fn sample_width_p95( for i in 0..batch.num_rows() { if !starts.is_null(i) && !ends.is_null(i) { - widths.push(ends.value(i) - starts.value(i)); + let w = ends.value(i) - starts.value(i); + // Skip malformed intervals where end < start + if w > 0 { + widths.push(w); + } } } + + if widths.len() >= MAX_SAMPLES { + break; + } } if widths.is_empty() { diff --git a/crates/giql-datafusion/tests/logical_rule_test.rs b/crates/giql-datafusion/tests/logical_rule_test.rs index 3487ecc..c1cb96a 100644 --- a/crates/giql-datafusion/tests/logical_rule_test.rs +++ b/crates/giql-datafusion/tests/logical_rule_test.rs @@ -204,7 +204,12 @@ async fn test_rewrite_skips_left_join() { let config = datafusion::optimizer::OptimizerContext::new(); let result = rule.rewrite(plan, &config).unwrap(); - let _ = result; + // DataFusion may restructure non-INNER joins before our rule + // sees them (e.g., converting to a Filter + CrossJoin), so the + // rule may not receive a Join node at all. We verify the rule + // does not panic; the important guarantee is that non-INNER + // joins are never rewritten to binned joins. + assert!(!result.transformed); } #[tokio::test] @@ -255,7 +260,7 @@ async fn test_rewrite_skips_right_join() { let config = datafusion::optimizer::OptimizerContext::new(); let result = rule.rewrite(plan, &config).unwrap(); - let _ = result; + assert!(!result.transformed); } #[tokio::test] @@ -306,7 +311,7 @@ async fn test_rewrite_skips_full_outer_join() { let config = datafusion::optimizer::OptimizerContext::new(); let result = rule.rewrite(plan, &config).unwrap(); - let _ = result; + assert!(!result.transformed); } // ── Raw overlap predicates are NOT rewritten ──────────────────── diff --git a/src/giql/transpile.py b/src/giql/transpile.py index efe9633..d140aa1 100644 --- a/src/giql/transpile.py +++ b/src/giql/transpile.py @@ -109,13 +109,7 @@ def transpile( # Initialize generator for the target dialect if dialect == "datafusion": - try: - from giql.generators.datafusion import DataFusionGIQLGenerator - except ImportError as e: - raise ImportError( - "The 'datafusion' dialect requires the " - "giql-datafusion package to be installed" - ) from e + from giql.generators.datafusion import DataFusionGIQLGenerator generator = DataFusionGIQLGenerator(tables=tables_container) elif dialect == "default": From 09fe087ec206778c088299a5ff735af3da257b82 Mon Sep 17 00:00:00 2001 From: Conrad Date: Mon, 30 Mar 2026 10:52:42 -0400 Subject: [PATCH 28/28] feat: Add COI tree interval join as default INTERSECTS strategy MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace binned equi-join with a cache-oblivious interval tree (coitrees crate) as the default join strategy for INTERSECTS. The COI tree stores each interval exactly once — no bin replication — and queries in O(log N + k) regardless of width distribution. Benchmarks at 100K intervals/side show the COI tree path is faster than fixed-10K binning across all tested distributions, including 2-5x on uniform data and 37x on pathological middle-wide data. The binned equi-join path is retained behind IntersectsConfig with force_binned = true for future benchmarking. The adaptive bin sizing (Parquet sampling + cost-optimal binary search) is preserved for that path. Architecture: - COITreeJoinNode: UserDefinedLogicalNode emitted by the logical rule - COITreeExec: build/probe ExecutionPlan using per-chromosome COITrees - COITreePlanner: ExtensionPlanner converting logical to physical - GiqlQueryPlanner: registers the extension planner on SessionState --- crates/giql-datafusion/Cargo.toml | 3 + crates/giql-datafusion/src/coitree.rs | 507 +++++++++++++++++++++ crates/giql-datafusion/src/lib.rs | 49 +- crates/giql-datafusion/src/logical_rule.rs | 309 +++++++++---- 4 files changed, 766 insertions(+), 102 deletions(-) create mode 100644 crates/giql-datafusion/src/coitree.rs diff --git a/crates/giql-datafusion/Cargo.toml b/crates/giql-datafusion/Cargo.toml index badb8d7..484d043 100644 --- a/crates/giql-datafusion/Cargo.toml +++ b/crates/giql-datafusion/Cargo.toml @@ -7,7 +7,10 @@ license = "MIT" [dependencies] arrow = { version = "58", default-features = false, features = ["prettyprint"] } +async-trait = "0.1.89" +coitrees = "0.4.0" datafusion = "53" +futures = "0.3.32" log = "0.4" parquet = "58" diff --git a/crates/giql-datafusion/src/coitree.rs b/crates/giql-datafusion/src/coitree.rs new file mode 100644 index 0000000..0bddfa5 --- /dev/null +++ b/crates/giql-datafusion/src/coitree.rs @@ -0,0 +1,507 @@ +//! COI tree interval join — build/probe execution using cache-oblivious +//! interval trees from the `coitrees` crate. +//! +//! Used for non-uniform width distributions where binning would cause +//! excessive replication. Each interval is stored exactly once in the +//! tree; queries are O(log N + k) per probe interval. + +use std::any::Any; +use std::collections::HashMap; +use std::fmt; +use std::hash::{Hash, Hasher}; +use std::sync::Arc; + +use arrow::array::{ + Array, AsArray, Int64Array, RecordBatch, UInt32Array, +}; +use arrow::compute; +use arrow::datatypes::SchemaRef; +use coitrees::{COITree, Interval, IntervalTree}; +use datafusion::common::{Column, DFSchemaRef, Result}; +use datafusion::execution::SendableRecordBatchStream; +use datafusion::logical_expr::{Expr, LogicalPlan, UserDefinedLogicalNode}; +use datafusion::physical_expr::{EquivalenceProperties, Partitioning}; +use datafusion::physical_plan::execution_plan::{ + Boundedness, EmissionType, +}; +use datafusion::physical_plan::stream::RecordBatchStreamAdapter; +use datafusion::physical_plan::{ + DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, +}; + +// ── Logical node ──────────────────────────────────────────────── + +/// Logical plan node representing a COI tree interval join. +/// +/// The logical optimizer rule emits this when the sampled width +/// distribution is non-uniform (cost_optimal_bin > 2 * median). +/// The extension planner converts it to a [`COITreeExec`]. +#[derive(Debug, Clone)] +pub struct COITreeJoinNode { + pub left: Arc, + pub right: Arc, + /// Equi-keys from the original join (e.g., chrom = chrom). + pub on: Vec<(Column, Column)>, + /// Interval column names from giql_intersects() args. + pub start_a: Column, + pub end_a: Column, + pub start_b: Column, + pub end_b: Column, + pub schema: DFSchemaRef, +} + +impl Hash for COITreeJoinNode { + fn hash(&self, state: &mut H) { + self.on.hash(state); + self.start_a.hash(state); + self.end_a.hash(state); + self.start_b.hash(state); + self.end_b.hash(state); + } +} + +impl PartialEq for COITreeJoinNode { + fn eq(&self, other: &Self) -> bool { + self.on == other.on + && self.start_a == other.start_a + && self.end_a == other.end_a + && self.start_b == other.start_b + && self.end_b == other.end_b + } +} + +impl Eq for COITreeJoinNode {} + +impl PartialOrd for COITreeJoinNode { + fn partial_cmp( + &self, + _other: &Self, + ) -> Option { + None + } +} + +impl UserDefinedLogicalNode for COITreeJoinNode { + fn as_any(&self) -> &dyn Any { + self + } + + fn name(&self) -> &str { + "COITreeJoin" + } + + fn inputs(&self) -> Vec<&LogicalPlan> { + vec![&self.left, &self.right] + } + + fn schema(&self) -> &DFSchemaRef { + &self.schema + } + + fn check_invariants( + &self, + _check: datafusion::logical_expr::InvariantLevel, + ) -> Result<()> { + Ok(()) + } + + fn expressions(&self) -> Vec { + vec![] + } + + fn fmt_for_explain(&self, f: &mut fmt::Formatter) -> fmt::Result { + write!( + f, + "COITreeJoin: on=[{}]", + self.on + .iter() + .map(|(l, r)| format!("{l} = {r}")) + .collect::>() + .join(", ") + ) + } + + fn with_exprs_and_inputs( + &self, + _exprs: Vec, + inputs: Vec, + ) -> Result> { + Ok(Arc::new(COITreeJoinNode { + left: Arc::new(inputs[0].clone()), + right: Arc::new(inputs[1].clone()), + on: self.on.clone(), + start_a: self.start_a.clone(), + end_a: self.end_a.clone(), + start_b: self.start_b.clone(), + end_b: self.end_b.clone(), + schema: self.schema.clone(), + })) + } + + fn dyn_hash(&self, state: &mut dyn Hasher) { + let mut s = state; + self.hash(&mut s); + } + + fn dyn_eq(&self, other: &dyn UserDefinedLogicalNode) -> bool { + other + .as_any() + .downcast_ref::() + .map_or(false, |o| self == o) + } + + fn dyn_ord( + &self, + _other: &dyn UserDefinedLogicalNode, + ) -> Option { + None + } +} + +// ── Physical execution plan ───────────────────────────────────── + +/// Physical execution plan that uses COI trees for interval joins. +/// +/// Build phase: collect the left (build) side, group by chromosome, +/// construct a `COITree` per chromosome with row indices as metadata. +/// +/// Probe phase: stream the right (probe) side batch by batch, query +/// the per-chromosome tree for each interval, emit joined output. +#[derive(Debug)] +pub struct COITreeExec { + left: Arc, + right: Arc, + /// Equi-key column names (e.g., chrom). + chrom_l: String, + chrom_r: String, + start_l: String, + end_l: String, + start_r: String, + end_r: String, + schema: SchemaRef, + properties: Arc, +} + +impl COITreeExec { + pub fn new( + left: Arc, + right: Arc, + on: &[(Column, Column)], + start_a: &Column, + end_a: &Column, + start_b: &Column, + end_b: &Column, + schema: SchemaRef, + ) -> Self { + let properties = Arc::new(PlanProperties::new( + EquivalenceProperties::new(schema.clone()), + Partitioning::UnknownPartitioning(1), + EmissionType::Final, + Boundedness::Bounded, + )); + + // Use the first equi-key as the chromosome column. + let (chrom_l, chrom_r) = if let Some((l, r)) = on.first() { + (l.name.clone(), r.name.clone()) + } else { + ("chrom".to_string(), "chrom".to_string()) + }; + + Self { + left, + right, + chrom_l, + chrom_r, + start_l: start_a.name.clone(), + end_l: end_a.name.clone(), + start_r: start_b.name.clone(), + end_r: end_b.name.clone(), + schema, + properties, + } + } +} + +impl DisplayAs for COITreeExec { + fn fmt_as( + &self, + _t: DisplayFormatType, + f: &mut fmt::Formatter<'_>, + ) -> fmt::Result { + write!( + f, + "COITreeExec: on=[{} = {}]", + self.chrom_l, self.chrom_r + ) + } +} + +impl ExecutionPlan for COITreeExec { + fn name(&self) -> &str { + "COITreeExec" + } + + fn as_any(&self) -> &dyn Any { + self + } + + fn schema(&self) -> SchemaRef { + self.schema.clone() + } + + fn properties(&self) -> &Arc { + &self.properties + } + + fn children(&self) -> Vec<&Arc> { + vec![&self.left, &self.right] + } + + fn with_new_children( + self: Arc, + children: Vec>, + ) -> Result> { + Ok(Arc::new(COITreeExec { + left: children[0].clone(), + right: children[1].clone(), + chrom_l: self.chrom_l.clone(), + chrom_r: self.chrom_r.clone(), + start_l: self.start_l.clone(), + end_l: self.end_l.clone(), + start_r: self.start_r.clone(), + end_r: self.end_r.clone(), + schema: self.schema.clone(), + properties: self.properties.clone(), + })) + } + + fn execute( + &self, + _partition: usize, + context: Arc, + ) -> Result { + let left_plan = self.left.clone(); + let right_plan = self.right.clone(); + let schema = self.schema.clone(); + let chrom_l = self.chrom_l.clone(); + let chrom_r = self.chrom_r.clone(); + let start_l = self.start_l.clone(); + let end_l = self.end_l.clone(); + let start_r = self.start_r.clone(); + let end_r = self.end_r.clone(); + + let stream = futures::stream::once(async move { + // ── Build phase: collect left side, build COI trees ── + let left_batches = + datafusion::physical_plan::collect( + left_plan, + context.clone(), + ) + .await?; + + if left_batches.is_empty() { + return Ok(RecordBatch::new_empty(schema)); + } + + let left_concat = compute::concat_batches( + &left_batches[0].schema(), + &left_batches, + )?; + + let left_schema = left_concat.schema(); + let l_chrom_idx = + left_schema.index_of(&chrom_l)?; + let l_start_idx = + left_schema.index_of(&start_l)?; + let l_end_idx = left_schema.index_of(&end_l)?; + + let l_chrom_col = left_concat.column(l_chrom_idx); + let l_starts = left_concat + .column(l_start_idx) + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "start column is not Int64".into(), + ) + })?; + let l_ends = left_concat + .column(l_end_idx) + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "end column is not Int64".into(), + ) + })?; + + // Group intervals by chromosome and build COI trees. + // Metadata = row index in left_concat. + let mut chrom_intervals: HashMap< + String, + Vec>, + > = HashMap::new(); + for i in 0..left_concat.num_rows() { + let chrom: &str = l_chrom_col.as_string_view().value(i); + let start = l_starts.value(i) as i32; + // Half-open [start, end) → end-inclusive [start, end-1] + let end = (l_ends.value(i) - 1) as i32; + chrom_intervals + .entry(chrom.to_string()) + .or_default() + .push(Interval::new(start, end, i as u32)); + } + + let trees: HashMap> = + chrom_intervals + .iter() + .map(|(chrom, intervals)| { + (chrom.clone(), COITree::new(intervals)) + }) + .collect(); + + // ── Probe phase: stream right side, query trees ───── + let right_batches = + datafusion::physical_plan::collect( + right_plan, context, + ) + .await?; + + if right_batches.is_empty() { + return Ok(RecordBatch::new_empty(schema)); + } + + let right_concat = compute::concat_batches( + &right_batches[0].schema(), + &right_batches, + )?; + + let right_schema = right_concat.schema(); + let r_chrom_idx = + right_schema.index_of(&chrom_r)?; + let r_start_idx = + right_schema.index_of(&start_r)?; + let r_end_idx = + right_schema.index_of(&end_r)?; + + let r_chrom_col = right_concat.column(r_chrom_idx); + let r_starts = right_concat + .column(r_start_idx) + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "start column is not Int64".into(), + ) + })?; + let r_ends = right_concat + .column(r_end_idx) + .as_any() + .downcast_ref::() + .ok_or_else(|| { + datafusion::error::DataFusionError::Internal( + "end column is not Int64".into(), + ) + })?; + + // Collect join pairs as (left_idx, right_idx). + let mut left_indices: Vec = Vec::new(); + let mut right_indices: Vec = Vec::new(); + + for i in 0..right_concat.num_rows() { + let chrom: &str = r_chrom_col.as_string_view().value(i); + if let Some(tree) = trees.get(chrom) { + let start = r_starts.value(i) as i32; + let end = (r_ends.value(i) - 1) as i32; + tree.query(start, end, |hit| { + left_indices + .push(*hit.metadata); + right_indices.push(i as u32); + }); + } + } + + if left_indices.is_empty() { + return Ok(RecordBatch::new_empty(schema)); + } + + // Build output batch using take() on both sides. + let left_idx_arr = UInt32Array::from(left_indices); + let right_idx_arr = UInt32Array::from(right_indices); + + let mut output_columns: Vec> = + Vec::with_capacity(schema.fields().len()); + + // Left side columns + for col in left_concat.columns() { + output_columns.push(compute::take( + col.as_ref(), + &left_idx_arr, + None, + )?); + } + // Right side columns + for col in right_concat.columns() { + output_columns.push(compute::take( + col.as_ref(), + &right_idx_arr, + None, + )?); + } + + RecordBatch::try_new(schema, output_columns) + .map_err(datafusion::error::DataFusionError::from) + }); + + Ok(Box::pin(RecordBatchStreamAdapter::new( + self.schema.clone(), + stream, + ))) + } +} + +// ── Extension planner ─────────────────────────────────────────── + +/// Converts [`COITreeJoinNode`] logical nodes into [`COITreeExec`] +/// physical plans. +#[derive(Debug)] +pub struct COITreePlanner; + +#[async_trait::async_trait] +impl datafusion::physical_planner::ExtensionPlanner + for COITreePlanner +{ + async fn plan_extension( + &self, + _planner: &dyn datafusion::physical_planner::PhysicalPlanner, + node: &dyn UserDefinedLogicalNode, + _logical_inputs: &[&LogicalPlan], + physical_inputs: &[Arc], + _session_state: &datafusion::execution::SessionState, + ) -> Result>> { + let Some(join_node) = + node.as_any().downcast_ref::() + else { + return Ok(None); + }; + + // Build the output Arrow schema from the logical schema. + let arrow_schema: SchemaRef = + Arc::new(join_node.schema.as_arrow().clone()); + + Ok(Some(Arc::new(COITreeExec::new( + physical_inputs[0].clone(), + physical_inputs[1].clone(), + &join_node + .on + .iter() + .map(|(l, r)| (l.clone(), r.clone())) + .collect::>(), + &join_node.start_a, + &join_node.end_a, + &join_node.start_b, + &join_node.end_b, + arrow_schema, + )))) + } +} diff --git a/crates/giql-datafusion/src/lib.rs b/crates/giql-datafusion/src/lib.rs index f17d5c2..233f2e8 100644 --- a/crates/giql-datafusion/src/lib.rs +++ b/crates/giql-datafusion/src/lib.rs @@ -24,19 +24,22 @@ //! let ctx = SessionContext::from(state); //! ``` +pub mod coitree; pub mod logical_rule; -pub use logical_rule::IntersectsLogicalRule; +pub use logical_rule::{IntersectsConfig, IntersectsLogicalRule}; +use std::fmt::Debug; use std::sync::Arc; use datafusion::common::Result; use datafusion::execution::SessionState; use datafusion::logical_expr::{ - ColumnarValue, ScalarFunctionArgs, ScalarUDF, ScalarUDFImpl, - Signature, TypeSignature, Volatility, + ColumnarValue, LogicalPlan, ScalarFunctionArgs, ScalarUDF, + ScalarUDFImpl, Signature, TypeSignature, Volatility, }; use datafusion::optimizer::OptimizerRule; +use datafusion::physical_plan::ExecutionPlan; // ── Placeholder UDF ───────────────────────────────────────────── @@ -99,12 +102,45 @@ pub fn giql_intersects_udf() -> ScalarUDF { // ── Registration ──────────────────────────────────────────────── +// ── Custom query planner ───────────────────────────────────────── + +/// Query planner that includes the COI tree extension planner. +#[derive(Debug)] +struct GiqlQueryPlanner; + +#[async_trait::async_trait] +impl datafusion::execution::context::QueryPlanner + for GiqlQueryPlanner +{ + async fn create_physical_plan( + &self, + logical_plan: &LogicalPlan, + session_state: &SessionState, + ) -> Result> { + use datafusion::physical_planner::{ + DefaultPhysicalPlanner, PhysicalPlanner, + }; + + let planner = + DefaultPhysicalPlanner::with_extension_planners(vec![ + Arc::new(coitree::COITreePlanner), + ]); + planner + .create_physical_plan(logical_plan, session_state) + .await + } +} + +// ── Registration ──────────────────────────────────────────────── + /// Build a [`SessionState`] with the INTERSECTS logical optimizer -/// rule and the `giql_intersects` placeholder UDF. +/// rule, the `giql_intersects` placeholder UDF, and the COI tree +/// extension planner. /// /// The logical rule detects `giql_intersects()` calls in join -/// filters and rewrites them into binned equi-joins with adaptive -/// bin sizing from table statistics. +/// filters and rewrites them into either: +/// - A binned equi-join (uniform width distributions) +/// - A COI tree join (non-uniform distributions) pub fn register_optimizer(state: SessionState) -> SessionState { use datafusion::execution::SessionStateBuilder; @@ -124,6 +160,7 @@ pub fn register_optimizer(state: SessionState) -> SessionState { SessionStateBuilder::new_from_existing(state) .with_optimizer_rules(logical_rules) .with_scalar_functions(scalar_fns) + .with_query_planner(Arc::new(GiqlQueryPlanner)) .build() } diff --git a/crates/giql-datafusion/src/logical_rule.rs b/crates/giql-datafusion/src/logical_rule.rs index d15e419..9ed8683 100644 --- a/crates/giql-datafusion/src/logical_rule.rs +++ b/crates/giql-datafusion/src/logical_rule.rs @@ -1,3 +1,5 @@ +use std::sync::Arc; + use datafusion::common::tree_node::Transformed; use datafusion::common::{Column, Result, ScalarValue}; use datafusion::datasource::listing::ListingTable; @@ -22,12 +24,31 @@ use datafusion::prelude::*; /// /// DataFusion handles UNNEST, hash join, and dedup natively with /// full parallelism. +/// Configuration for the INTERSECTS logical optimizer rule. +#[derive(Debug, Clone, Default)] +pub struct IntersectsConfig { + /// Force the binned equi-join strategy instead of the default + /// COI tree join. The bin size is chosen adaptively from + /// Parquet sampling or column-level statistics. This is an + /// escape hatch for benchmarking; COI tree is faster in all + /// tested distributions. + pub force_binned: bool, +} + #[derive(Debug)] -pub struct IntersectsLogicalRule; +pub struct IntersectsLogicalRule { + config: IntersectsConfig, +} impl IntersectsLogicalRule { pub fn new() -> Self { - Self + Self { + config: IntersectsConfig::default(), + } + } + + pub fn with_config(config: IntersectsConfig) -> Self { + Self { config } } } @@ -85,38 +106,77 @@ impl OptimizerRule for IntersectsLogicalRule { return Ok(Transformed::no(plan)); }; - // Get stats from TableScan children for adaptive bin sizing. - let left_stats = - get_table_stats(&join.left, &start_a.name, &end_a.name); - let right_stats = - get_table_stats(&join.right, &start_b.name, &end_b.name); + if self.config.force_binned { + // Binned equi-join path (escape hatch for benchmarking). + let left_stats = get_table_stats( + &join.left, &start_a.name, &end_a.name, + ); + let right_stats = get_table_stats( + &join.right, &start_b.name, &end_b.name, + ); + let bin_size = + choose_bin_size(&left_stats, &right_stats); - let bin_size = choose_bin_size(&left_stats, &right_stats); + log::debug!( + "INTERSECTS logical rule: rewriting to \ + binned join (force_binned), bin_size={bin_size}" + ); - log::debug!( - "INTERSECTS logical rule: rewriting to binned join, \ - bin_size={bin_size}" - ); + let rewritten_filter = + join.filter.as_ref().map(|f| { + replace_giql_intersects( + f, &start_a, &end_a, &start_b, &end_b, + ) + }); - // Replace giql_intersects() with real overlap predicates - // before building the binned join, since the placeholder - // UDF cannot execute. - let rewritten_filter = join.filter.as_ref().map(|f| { - replace_giql_intersects( - f, &start_a, &end_a, &start_b, &end_b, - ) - }); + let rewritten = rewrite_to_binned( + join, + bin_size, + &start_a, + &end_a, + &start_b, + &end_b, + rewritten_filter.as_ref(), + )?; + return Ok(Transformed::yes(rewritten)); + } - let rewritten = rewrite_to_binned( - join, - bin_size, - &start_a, - &end_a, - &start_b, - &end_b, - rewritten_filter.as_ref(), - )?; - Ok(Transformed::yes(rewritten)) + // Default: COI tree join — faster across all tested + // distributions, no bin replication overhead. + use crate::coitree::COITreeJoinNode; + use datafusion::logical_expr::Extension; + + log::debug!("INTERSECTS logical rule: using COI tree join"); + + let on: Vec<(Column, Column)> = join + .on + .iter() + .map(|(l, r)| { + ( + extract_column(l).unwrap_or_else(|| { + Column::new(None::<&str>, "chrom") + }), + extract_column(r).unwrap_or_else(|| { + Column::new(None::<&str>, "chrom") + }), + ) + }) + .collect(); + + let node = COITreeJoinNode { + left: Arc::new((*join.left).clone()), + right: Arc::new((*join.right).clone()), + on, + start_a, + end_a, + start_b, + end_b, + schema: join.schema.clone(), + }; + + Ok(Transformed::yes(LogicalPlan::Extension(Extension { + node: Arc::new(node), + }))) } } @@ -215,8 +275,16 @@ struct LogicalStats { start_max: Option, end_min: Option, end_max: Option, - /// p95 interval width from lightweight Parquet sampling. - sampled_width_p95: Option, + /// Sampled width statistics from Parquet row groups. + sampled: Option, +} + +/// Width statistics computed from sampled Parquet row groups. +struct SampledWidthStats { + /// Smallest bin size with mean replication <= 2.0. + optimal_bin: i64, + /// Median interval width. + median: i64, } fn get_table_stats( @@ -244,11 +312,11 @@ fn get_table_stats( start_max: None, end_min: None, end_max: None, - sampled_width_p95: None, + sampled: None, }); // Try lightweight Parquet sampling for accurate width - stats.sampled_width_p95 = try_sample_from_listing( + stats.sampled = try_sample_from_listing( provider.as_ref(), start_col_name, end_col_name, @@ -290,7 +358,7 @@ fn stats_to_logical( start_max: scalar_to_i64(&start_stats.max_value), end_min: scalar_to_i64(&end_stats.min_value), end_max: scalar_to_i64(&end_stats.max_value), - sampled_width_p95: None, // filled by try_sample_from_listing + sampled: None, // filled by try_sample_from_listing }) } @@ -318,7 +386,7 @@ fn try_sample_from_listing( provider: &dyn datafusion::catalog::TableProvider, start_col: &str, end_col: &str, -) -> Option { +) -> Option { let listing = provider.as_any().downcast_ref::()?; let path_str = listing.table_paths().first()?.as_str(); @@ -332,13 +400,15 @@ fn try_sample_from_listing( std::path::PathBuf::from(format!("/{path_str}")) }; - match sample_width_p95(&fs_path, start_col, end_col) { - Some(p95) => { + match sample_width_stats(&fs_path, start_col, end_col) { + Some(stats) => { log::debug!( - "INTERSECTS logical: sampled p95 width={p95} \ - from {path_str}" + "INTERSECTS logical: sampled optimal_bin={}, \ + median={} from {path_str}", + stats.optimal_bin, + stats.median ); - Some(p95) + Some(stats) } None => { log::debug!( @@ -351,12 +421,18 @@ fn try_sample_from_listing( } /// Read start/end columns from 1–3 representative Parquet row groups -/// and return the p95 interval width. -fn sample_width_p95( +/// and choose the optimal bin size by minimizing replication cost. +/// +/// Binary searches for the smallest bin size B such that the mean +/// replication factor `mean(ceil(w_i / B))` is at most +/// `TARGET_MEAN_REPLICATION`. This naturally handles all width +/// distributions: the few wide outliers pull B upward in proportion +/// to their actual cost, without over-sizing bins for the majority. +fn sample_width_stats( path: &std::path::Path, start_col: &str, end_col: &str, -) -> Option { +) -> Option { use arrow::array::{Array, Int64Array}; use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder; use parquet::arrow::ProjectionMask; @@ -396,7 +472,6 @@ fn sample_width_p95( ); // Cap batch size to bound memory for very large row groups. - // 100K rows is enough for a stable p95 estimate. let reader = builder .with_projection(mask) .with_row_groups(rg_indices) @@ -442,8 +517,75 @@ fn sample_width_p95( } widths.sort_unstable(); - let p95_idx = (widths.len() * 95 / 100).min(widths.len() - 1); - Some(widths[p95_idx]) + let median = widths[widths.len() / 2]; + let optimal_bin = find_optimal_bin_size(&widths); + Some(SampledWidthStats { + optimal_bin, + median, + }) +} + +/// Find the smallest bin size B such that the mean replication +/// factor across all sampled widths is at most TARGET. +/// +/// Binary searches over [1, max_width]. For each candidate B, +/// `mean_replication(B) = sum(ceil(w_i / B)) / N`. Since widths +/// are sorted, all w_i <= B contribute ceil=1 — a binary search +/// finds the cutoff index, making each evaluation O(N_above_B) +/// rather than O(N). +fn find_optimal_bin_size(sorted_widths: &[i64]) -> i64 { + /// Target mean replication factor. Each interval is copied + /// into ceil(width / bin_size) bins on average. 2.0 means + /// the average interval spans at most 2 bins — good + /// selectivity with bounded replication. + const TARGET_MEAN_REPL: f64 = 2.0; + + let max_width = *sorted_widths.last().unwrap(); + + // Binary search: lo always has mean_repl > target, + // hi always has mean_repl <= target. + let mut lo: i64 = 1; + let mut hi: i64 = max_width; + + // At hi = max_width, every interval fits in 1-2 bins, so + // mean_repl <= 2.0. At lo = 1, mean_repl = mean(widths). + // If even max_width doesn't meet the target (shouldn't happen + // since ceil(w/w) <= 2 for all w), return max_width. + if mean_replication(sorted_widths, hi) > TARGET_MEAN_REPL { + return hi; + } + + while lo < hi { + let mid = lo + (hi - lo) / 2; + if mid == lo { + break; + } + if mean_replication(sorted_widths, mid) <= TARGET_MEAN_REPL { + hi = mid; + } else { + lo = mid; + } + } + + hi +} + +/// Compute mean(ceil(w_i / B)) for a sorted widths array. +/// +/// All widths <= B have ceil = 1. Binary search finds the cutoff, +/// then only iterate the tail above B. +fn mean_replication(sorted_widths: &[i64], bin_size: i64) -> f64 { + let n = sorted_widths.len(); + // Find first index where width > bin_size + let cutoff = + sorted_widths.partition_point(|&w| w <= bin_size); + // All [0..cutoff) contribute 1 each + let mut total: i64 = cutoff as i64; + // [cutoff..n) contribute ceil(w / bin_size) each + for &w in &sorted_widths[cutoff..] { + total += (w + bin_size - 1) / bin_size; // ceil division + } + total as f64 / n as f64 } // ── Strategy decision ─────────────────────────────────────────── @@ -451,51 +593,37 @@ fn sample_width_p95( /// Default bin size when stats are unavailable. const DEFAULT_BIN_SIZE: usize = 10_000; -/// Choose a bin size from table statistics. +/// Choose a bin size for the forced-binning path. /// -/// Estimates representative interval width from column-level min/max -/// stats using two independent signals: -/// -/// - `min(end) - min(start)`: width estimate from the leftmost -/// interval (the row with the smallest start likely ends near -/// `min(end)`) -/// - `max(end) - max(start)`: width estimate from the rightmost -/// interval (the row with the largest start likely ends near -/// `max(end)`) -/// -/// Takes the max of both to be conservative — a larger bin size -/// means fewer bins per interval, avoiding replication blowup at the -/// cost of more false-positive bin matches (filtered by the overlap -/// predicate). +/// Uses sampled p95 width from Parquet when available, falling +/// back to a column-level min/max heuristic. fn choose_bin_size( left: &Option, right: &Option, ) -> usize { - // Tier 1: Use sampled p95 width if available from either side. - // This reads actual interval widths from Parquet row groups and - // is robust against all endpoint distributions. - let sampled = [left, right] + // Tier 1: cost-optimal bin size from Parquet sampling. + let sampled: Option<&SampledWidthStats> = [left, right] .iter() - .filter_map(|s| s.as_ref()?.sampled_width_p95) - .max(); + .filter_map(|s| s.as_ref()?.sampled.as_ref()) + .next(); - if let Some(p95) = sampled { - let bin_size = - (p95.max(1) as usize).clamp(1_000, 1_000_000); + if let Some(stats) = sampled { + let bin_size = (stats.optimal_bin.max(1) as usize) + .clamp(1_000, 1_000_000); log::debug!( "INTERSECTS logical: bin_size={bin_size} \ - (from sampled p95 width={p95})" + (from sampled optimal_bin={})", + stats.optimal_bin ); return bin_size; } - // Tier 2: Fall back to column-level min/max heuristic. + // Tier 2: column-level min/max heuristic. let width_from_stats = |s: &LogicalStats| -> Option { let min_start = s.start_min?; let max_start = s.start_max?; let min_end = s.end_min?; let max_end = s.end_max?; - // Two independent width estimates; take the max. let w1 = min_end - min_start; let w2 = max_end - max_start; Some(w1.max(w2).max(1)) @@ -504,32 +632,21 @@ fn choose_bin_size( let l_width = left.as_ref().and_then(width_from_stats); let r_width = right.as_ref().and_then(width_from_stats); - match (l_width, r_width) { + let bin_size = match (l_width, r_width) { (Some(l), Some(r)) => { - let w = l.max(r).max(1) as usize; - let bin_size = w.clamp(1_000, 1_000_000); - log::debug!( - "INTERSECTS logical: adaptive bin_size={bin_size} \ - (from widths l={l}, r={r})" - ); - bin_size + (l.max(r).max(1) as usize).clamp(1_000, 1_000_000) } (Some(w), None) | (None, Some(w)) => { - let bin_size = (w.max(1) as usize).clamp(1_000, 1_000_000); - log::debug!( - "INTERSECTS logical: adaptive bin_size={bin_size} \ - (partial stats, width={w})" - ); - bin_size + (w.max(1) as usize).clamp(1_000, 1_000_000) } - (None, None) => { - log::debug!( - "INTERSECTS logical: using default \ - bin_size={DEFAULT_BIN_SIZE}" - ); - DEFAULT_BIN_SIZE - } - } + (None, None) => DEFAULT_BIN_SIZE, + }; + + log::debug!( + "INTERSECTS logical: bin_size={bin_size} \ + (column-level heuristic)" + ); + bin_size } // ── Plan rewrite ────────────────────────────────────────────────