3559: feat: Cast numeric (non int) to timestamp

native/spark-expr/Cargo.toml

@@ -103,3 +103,7 @@ path = "tests/spark_expr_reg.rs"
 [[bench]]
 name = "cast_from_boolean"
 harness = false
+
+[[bench]]
+name = "cast_non_int_numeric_timestamp"
+harness = false

native/spark-expr/benches/cast_non_int_numeric_timestamp.rs

@@ -0,0 +1,143 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+use arrow::array::builder::{BooleanBuilder, Decimal128Builder, Float32Builder, Float64Builder};
+use arrow::array::RecordBatch;
+use arrow::datatypes::{DataType, Field, Schema, TimeUnit};
+use criterion::{criterion_group, criterion_main, Criterion};
+use datafusion::physical_expr::{expressions::Column, PhysicalExpr};
+use datafusion_comet_spark_expr::{Cast, EvalMode, SparkCastOptions};
+use std::sync::Arc;
+
+const BATCH_SIZE: usize = 8192;
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let spark_cast_options = SparkCastOptions::new(EvalMode::Legacy, "UTC", false);
+    let timestamp_type = DataType::Timestamp(TimeUnit::Microsecond, Some("UTC".into()));
+
+    let mut group = c.benchmark_group("cast_non_int_numeric_to_timestamp");
+
+    // Float32 -> Timestamp
+    let batch_f32 = create_float32_batch();
+    let expr_f32 = Arc::new(Column::new("a", 0));
+    let cast_f32_to_ts = Cast::new(expr_f32, timestamp_type.clone(), spark_cast_options.clone());
+    group.bench_function("cast_f32_to_timestamp", |b| {
+        b.iter(|| cast_f32_to_ts.evaluate(&batch_f32).unwrap());
+    });
+
+    // Float64 -> Timestamp
+    let batch_f64 = create_float64_batch();
+    let expr_f64 = Arc::new(Column::new("a", 0));
+    let cast_f64_to_ts = Cast::new(expr_f64, timestamp_type.clone(), spark_cast_options.clone());
+    group.bench_function("cast_f64_to_timestamp", |b| {
+        b.iter(|| cast_f64_to_ts.evaluate(&batch_f64).unwrap());
+    });
+
+    // Boolean -> Timestamp
+    let batch_bool = create_boolean_batch();
+    let expr_bool = Arc::new(Column::new("a", 0));
+    let cast_bool_to_ts = Cast::new(
+        expr_bool,
+        timestamp_type.clone(),
+        spark_cast_options.clone(),
+    );
+    group.bench_function("cast_bool_to_timestamp", |b| {
+        b.iter(|| cast_bool_to_ts.evaluate(&batch_bool).unwrap());
+    });
+
+    // Decimal128 -> Timestamp
+    let batch_decimal = create_decimal128_batch();
+    let expr_decimal = Arc::new(Column::new("a", 0));
+    let cast_decimal_to_ts = Cast::new(
+        expr_decimal,
+        timestamp_type.clone(),
+        spark_cast_options.clone(),
+    );
+    group.bench_function("cast_decimal_to_timestamp", |b| {
+        b.iter(|| cast_decimal_to_ts.evaluate(&batch_decimal).unwrap());
+    });
+
+    group.finish();
+}
+
+fn create_float32_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Float32, true)]));
+    let mut b = Float32Builder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<f32>());
+        }
+    }
+    RecordBatch::try_new(schema, vec![Arc::new(b.finish())]).unwrap()
+}
+
+fn create_float64_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Float64, true)]));
+    let mut b = Float64Builder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<f64>());
+        }
+    }
+    RecordBatch::try_new(schema, vec![Arc::new(b.finish())]).unwrap()
+}
+
+fn create_boolean_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Boolean, true)]));
+    let mut b = BooleanBuilder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(rand::random::<bool>());
+        }
+    }
+    RecordBatch::try_new(schema, vec![Arc::new(b.finish())]).unwrap()
+}
+
+fn create_decimal128_batch() -> RecordBatch {
+    let schema = Arc::new(Schema::new(vec![Field::new(
+        "a",
+        DataType::Decimal128(18, 6),
+        true,
+    )]));
+    let mut b = Decimal128Builder::with_capacity(BATCH_SIZE);
+    for i in 0..BATCH_SIZE {
+        if i % 10 == 0 {
+            b.append_null();
+        } else {
+            b.append_value(i as i128 * 1_000_000);
+        }
+    }
+    let array = b.finish().with_precision_and_scale(18, 6).unwrap();
+    RecordBatch::try_new(schema, vec![Arc::new(array)]).unwrap()
+}
+
+fn config() -> Criterion {
+    Criterion::default()
+}
+
+criterion_group! {
+    name = benches;
+    config = config();
+    targets = criterion_benchmark
+}
+criterion_main!(benches);

native/spark-expr/src/conversion_funcs/boolean.rs

@@ -16,7 +16,7 @@
 // under the License.
 
 use crate::SparkResult;
-use arrow::array::{ArrayRef, AsArray, Decimal128Array};
+use arrow::array::{Array, ArrayRef, AsArray, Decimal128Array, TimestampMicrosecondBuilder};
 use arrow::datatypes::DataType;
 use std::sync::Arc;
 
@@ -28,7 +28,6 @@ pub fn is_df_cast_from_bool_spark_compatible(to_type: &DataType) -> bool {
     )
 }
 
-// only DF incompatible boolean cast
 pub fn cast_boolean_to_decimal(
     array: &ArrayRef,
     precision: u8,
@@ -43,6 +42,25 @@ pub fn cast_boolean_to_decimal(
     Ok(Arc::new(result.with_precision_and_scale(precision, scale)?))
 }
 
+pub(crate) fn cast_boolean_to_timestamp(
+    array_ref: &ArrayRef,
+    target_tz: &Option<Arc<str>>,
+) -> SparkResult<ArrayRef> {
+    let bool_array = array_ref.as_boolean();
+    let mut builder = TimestampMicrosecondBuilder::with_capacity(bool_array.len());
+
+    for i in 0..bool_array.len() {
+        if bool_array.is_null(i) {
+            builder.append_null();
+        } else {
+            let micros = if bool_array.value(i) { 1 } else { 0 };
+            builder.append_value(micros);
+        }
+    }
+
+    Ok(Arc::new(builder.finish().with_timezone_opt(target_tz.clone())) as ArrayRef)
+}
+
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -53,6 +71,7 @@ mod tests {
         Int64Array, Int8Array, StringArray,
     };
     use arrow::datatypes::DataType::Decimal128;
+    use arrow::datatypes::TimestampMicrosecondType;
     use std::sync::Arc;
 
     fn test_input_bool_array() -> ArrayRef {
@@ -193,4 +212,26 @@ mod tests {
         assert_eq!(arr.value(1), expected_arr.value(1));
         assert!(arr.is_null(2));
     }
+
+    #[test]
+    fn test_cast_boolean_to_timestamp() {
+        let timezones: [Option<Arc<str>>; 3] = [
+            Some(Arc::from("UTC")),
+            Some(Arc::from("America/Los_Angeles")),
+            None,
+        ];
+
+        for tz in &timezones {
+            let bool_array: ArrayRef =
+                Arc::new(BooleanArray::from(vec![Some(true), Some(false), None]));
+
+            let result = cast_boolean_to_timestamp(&bool_array, tz).unwrap();
+            let ts_array = result.as_primitive::<TimestampMicrosecondType>();
+
+            assert_eq!(ts_array.value(0), 1); // true -> 1 microsecond
+            assert_eq!(ts_array.value(1), 0); // false -> 0 (epoch)
+            assert!(ts_array.is_null(2));
+            assert_eq!(ts_array.timezone(), tz.as_ref().map(|s| s.as_ref()));
+        }
+    }
 }

native/spark-expr/src/conversion_funcs/cast.rs

@@ -16,14 +16,15 @@
 // under the License.
 
 use crate::conversion_funcs::boolean::{
-    cast_boolean_to_decimal, is_df_cast_from_bool_spark_compatible,
+    cast_boolean_to_decimal, cast_boolean_to_timestamp, is_df_cast_from_bool_spark_compatible,
 };
 use crate::conversion_funcs::numeric::{
-    cast_float32_to_decimal128, cast_float64_to_decimal128, cast_int_to_decimal128,
-    cast_int_to_timestamp, is_df_cast_from_decimal_spark_compatible,
-    is_df_cast_from_float_spark_compatible, is_df_cast_from_int_spark_compatible,
-    spark_cast_decimal_to_boolean, spark_cast_float32_to_utf8, spark_cast_float64_to_utf8,
-    spark_cast_int_to_int, spark_cast_nonintegral_numeric_to_integral,
+    cast_decimal_to_timestamp, cast_float32_to_decimal128, cast_float64_to_decimal128,
+    cast_float_to_timestamp, cast_int_to_decimal128, cast_int_to_timestamp,
+    is_df_cast_from_decimal_spark_compatible, is_df_cast_from_float_spark_compatible,
+    is_df_cast_from_int_spark_compatible, spark_cast_decimal_to_boolean,
+    spark_cast_float32_to_utf8, spark_cast_float64_to_utf8, spark_cast_int_to_int,
+    spark_cast_nonintegral_numeric_to_integral,
 };
 use crate::conversion_funcs::string::{
     cast_string_to_date, cast_string_to_decimal, cast_string_to_float, cast_string_to_int,
@@ -384,6 +385,9 @@ pub(crate) fn cast_array(
             cast_boolean_to_decimal(&array, *precision, *scale)
         }
         (Int8 | Int16 | Int32 | Int64, Timestamp(_, tz)) => cast_int_to_timestamp(&array, tz),
+        (Float32 | Float64, Timestamp(_, tz)) => cast_float_to_timestamp(&array, tz, eval_mode),
+        (Boolean, Timestamp(_, tz)) => cast_boolean_to_timestamp(&array, tz),
+        (Decimal128(_, scale), Timestamp(_, tz)) => cast_decimal_to_timestamp(&array, tz, *scale),
         _ if cast_options.is_adapting_schema
             || is_datafusion_spark_compatible(&from_type, to_type) =>
         {