diff --git a/ARCHITECTURE.md b/ARCHITECTURE.md
index bf82152..cdb215f 100644
--- a/ARCHITECTURE.md
+++ b/ARCHITECTURE.md
@@ -56,8 +56,9 @@ torchcachex was designed to solve a specific problem: **efficient, persistent ca
 │  │              Flush Mechanism (Async/Sync)              ││
 │  │  1. Create Arrow RecordBatch                           ││
 │  │  2. Write temp segment file                            ││
-│  │  3. Update SQLite (atomic transaction)                 ││
+│  │  3. Update in-memory index dict                        ││
 │  │  4. Atomic rename                                      ││
+│  │  5. Persist index.pkl (atomic)                         ││
 │  └────────────────────────────────────────────────────────┘│
 └────────────────────┬────────────────────────────────────────┘
                      │
@@ -70,7 +71,7 @@ torchcachex was designed to solve a specific problem: **efficient, persistent ca
 │    │   ├── segment_000000.arrow  ← Arrow IPC files         │
 │    │   ├── segment_000001.arrow  ← (immutable)             │
 │    │   └── ...                                              │
-│    ├── index.db                  ← SQLite with WAL         │
+│    ├── index.pkl                 ← Pickle index (dict)     │
 │    └── schema.json               ← Auto-inferred schema    │
 └─────────────────────────────────────────────────────────────┘
 ```
@@ -101,7 +102,7 @@ forward(x, cache_ids) → {
 #### 2. ArrowIPCCacheBackend
 
 **Responsibilities:**
-- Manages persistent storage (Arrow + SQLite)
+- Manages persistent storage (Arrow + pickle index)
 - Handles schema inference on first write
 - Maintains in-memory LRU cache for hot data
 - Buffers writes and flushes in batches
@@ -112,38 +113,41 @@ forward(x, cache_ids) → {
 - `self.lru`: LRU cache (dict-like, size-bounded)
 - `self._pending`: Write buffer (list of dicts)
 - `self.schema`: Arrow schema (inferred or loaded)
-- `self.conn`: SQLite connection with WAL mode
+- `self.index`: In-memory dict mapping keys to (segment_id, row_offset)
 - `self.executor`: ThreadPoolExecutor for async writes
 
-#### 3. SQLite Index
+#### 3. Pickle Index
 
 **Responsibilities:**
-- O(1) key → (segment_id, row_offset) mapping
-- Track segment metadata (file paths, row counts)
-- Atomic transactions for consistency
-- WAL mode for concurrent reads during writes
-
-**Schema:**
-```sql
-CREATE TABLE cache (
-    key TEXT PRIMARY KEY,          -- Cache key (module_id:sample_id)
-    segment_id INTEGER NOT NULL,   -- Which Arrow file
-    row_offset INTEGER NOT NULL    -- Row within that file
-);
-
-CREATE TABLE segments (
-    segment_id INTEGER PRIMARY KEY,
-    file_path TEXT NOT NULL,       -- Relative path to Arrow file
-    num_rows INTEGER NOT NULL      -- Number of rows in segment
-);
+- O(1) key → (segment_id, row_offset) mapping via Python dict
+- In-memory for fast lookups, persisted to disk for durability
+- Atomic persistence with temp file swap
+- Auto-rebuild from segments on corruption or missing index
+
+**Data Structure:**
+```python
+self.index = {
+    "module_id:sample_123": (0, 42),  # segment_id=0, row_offset=42
+    "module_id:sample_456": (0, 43),
+    "module_id:sample_789": (1, 0),
+    # ... millions of entries
+}
 ```
 
-**SQLite Configuration:**
-```sql
-PRAGMA journal_mode=WAL;          -- Write-Ahead Logging
-PRAGMA synchronous=NORMAL;        -- Safe in WAL mode
-PRAGMA temp_store=MEMORY;         -- Temp tables in RAM
-PRAGMA mmap_size=30000000000;     -- 30GB memory-mapped I/O
+**Persistence:**
+```python
+# Atomic save with temp file
+temp_path = index_path.with_suffix('.pkl.tmp')
+with open(temp_path, 'wb') as f:
+    pickle.dump(self.index, f)
+temp_path.rename(index_path)  # Atomic on POSIX
+
+# Load on startup
+if index_path.exists():
+    with open(index_path, 'rb') as f:
+        self.index = pickle.load(f)
+else:
+    self.index = self._rebuild_index_from_segments()
 ```
 
 #### 4. Arrow IPC Segments
@@ -308,7 +312,7 @@ def flush():
 
 **Problem**: As cache grows, flush time increases linearly. With 1M samples, flushing 1k new samples requires rewriting 1M samples.
 
-#### New Architecture (Arrow IPC + SQLite)
+#### New Architecture (Arrow IPC + Pickle Index)
 
 ```python
 def flush():
@@ -320,19 +324,23 @@ def flush():
         writer = pa.ipc.new_file(sink, schema)
         writer.write_batch(batch)  # Sequential write, ~O(M)
 
-    # O(1): Update SQLite index
-    cursor.execute("BEGIN TRANSACTION")
-    cursor.execute("INSERT INTO segments ...")
-    cursor.executemany("INSERT INTO cache ...", pending_keys)  # ~O(M)
-    cursor.execute("COMMIT")
+    # O(1): Update in-memory index
+    for i, item in enumerate(pending):
+        self.index[item["key"]] = (segment_id, i)  # ~O(M) dict updates
 
     # O(1): Atomic rename
     temp_path.rename(final_path)
 
-    # Total: O(M) per flush, independent of N (existing cache size)
+    # O(M): Persist index to disk
+    with open(temp_index_path, 'wb') as f:
+        pickle.dump(self.index, f)  # ~O(total index size)
+    temp_index_path.rename(index_path)
+
+    # Total: O(M + I) per flush, where I = total index size
+    # Note: pickle.dump is typically very fast (~100MB/s)
 ```
 
-**Breakthrough**: Flush time depends only on new data (M), not existing cache (N). With 1M samples cached, flushing 1k new samples takes the same time as the first flush.
+**Breakthrough**: Flush time depends only on new data (M) plus index serialization (I). With 1M samples cached, flushing 1k new samples is still very fast due to efficient pickle serialization.
 
 ### Read Path Analysis
 
@@ -347,12 +355,12 @@ def get_batch(keys):
     if not missing_keys:
         return hits, []  # All cache hits
 
-    # O(K): Query SQLite for missing keys
-    cursor.execute(
-        "SELECT key, segment_id, row_offset FROM cache WHERE key IN (?)",
-        missing_keys
-    )
-    index_results = cursor.fetchall()  # O(K) with primary key index
+    # O(K): Query in-memory index for missing keys
+    index_results = []
+    for key in missing_keys:
+        if key in self.index:
+            seg_id, offset = self.index[key]
+            index_results.append((key, seg_id, offset))
 
     # O(K): Group by segment_id
     by_segment = defaultdict(list)
@@ -382,25 +390,26 @@ def get_batch(keys):
 1. **LRU cache** reduces disk access for hot data
 2. **Memory-mapping** enables zero-copy reads with OS page cache
 3. **Columnar access** allows extracting specific rows without full table scan
-4. **Batch SQLite query** uses primary key index (O(K log N) → O(K) with good index)
+4. **In-memory dict lookup** provides true O(1) access (faster than SQLite B-tree)
 
 ### Space Complexity
 
 ```
-Total Disk Space = Sum(Arrow segments) + SQLite index + Schema file
+Total Disk Space = Sum(Arrow segments) + Pickle index + Schema file
 
 Arrow segments:
   - Per sample: sizeof(key) + sizeof(flattened_tensor) + sizeof(shape) + overhead
   - For float32[512]: ~8 bytes (key) + 2048 bytes (data) + 8 bytes (shape) + ~10 bytes (Arrow) ≈ 2074 bytes
   - For 1B samples: ~2074 GB = ~2 TB
 
-SQLite index:
-  - Per sample: ~50 bytes (key + segment_id + row_offset + index overhead)
-  - For 1B samples: ~50 GB
+Pickle index:
+  - Per sample: ~40 bytes (dict overhead + key string + tuple with 2 ints)
+  - For 1B samples: ~40 GB
+  - Note: More compact than SQLite due to no B-tree overhead
 
 Schema file: <1 KB (negligible)
 
-Total for 1B samples with 512-dim float32 features: ~2.05 TB
+Total for 1B samples with 512-dim float32 features: ~2.04 TB
 ```
 
 **Comparison**: Raw PyTorch `.pt` files would use similar space (~2 TB for tensors), but without:
@@ -411,7 +420,7 @@ Total for 1B samples with 512-dim float32 features: ~2.05 TB
 ### Memory Complexity
 
 ```
-Peak Memory = LRU cache + Pending buffer + Active segment + SQLite cache
+Peak Memory = LRU cache + Pending buffer + Active segment + In-memory index
 
 LRU cache:
   - Size: lru_size * avg_sample_size
@@ -426,13 +435,16 @@ Active segment (during read):
   - Example: 2048 samples * 2048 bytes = 4 MB
   - Note: OS page cache makes this effectively free
 
-SQLite cache:
-  - Size: ~10-50 MB (internal SQLite caches)
+In-memory index:
+  - Size: ~40 bytes per entry (dict overhead + key + value tuple)
+  - Example for 1M samples: ~40 MB
+  - Example for 1B samples: ~40 GB
+  - Note: Scales linearly with total cache size
 
-Total: ~20-70 MB (independent of total cache size!)
+Total: ~20 MB + index size (dependent on total cache size)
 ```
 
-**Breakthrough**: Memory usage is **O(working set)**, not O(cache size). A 1B sample cache uses the same memory as a 1M sample cache.
+**Note**: Memory usage now includes the full in-memory index, which scales with cache size. For very large caches (>100M samples), consider the ~40 bytes per sample overhead. However, this is still very efficient compared to loading all cached data into memory.
 
 ## Implementation Details
 
@@ -544,44 +556,30 @@ def _flush_segment(self) -> None:
         writer.write_batch(record_batch)
         writer.close()
 
-    # 3. Update SQLite (atomic transaction)
-    final_file = self.segments_dir / f"segment_{segment_id:06d}.arrow"
-    cursor = self.conn.cursor()
-    try:
-        cursor.execute("BEGIN TRANSACTION")
-
-        # Register segment
-        cursor.execute(
-            "INSERT INTO segments (segment_id, file_path, num_rows) VALUES (?, ?, ?)",
-            (segment_id, f"segments/segment_{segment_id:06d}.arrow", len(batch))
-        )
-
-        # Index all keys
-        cursor.executemany(
-            "INSERT OR REPLACE INTO cache (key, segment_id, row_offset) VALUES (?, ?, ?)",
-            [(item["key"], segment_id, i) for i, item in enumerate(batch)]
-        )
-
-        cursor.execute("COMMIT")
-    except Exception:
-        cursor.execute("ROLLBACK")
-        raise
+    # 3. Update in-memory index
+    for i, item in enumerate(batch):
+        self.index[item["key"]] = (segment_id, i)
 
     # 4. Atomic rename (makes segment visible)
+    final_file = self.segments_dir / f"segment_{segment_id:06d}.arrow"
     temp_file.rename(final_file)
+
+    # 5. Persist index to disk (atomic)
+    self._save_index()
 ```
 
 **Atomicity Guarantees**:
 1. **Write to `.tmp` first**: Incomplete writes don't affect readers
-2. **SQLite transaction**: Either all index updates succeed or none do
+2. **In-memory index update**: Fast, always succeeds
 3. **Atomic rename**: OS guarantees rename is atomic; readers see complete file or nothing
-4. **WAL mode**: SQLite writers don't block readers
+4. **Atomic index save**: Uses temp file + rename pattern for crash safety
 
 **Failure Scenarios**:
 - **Crash during Arrow write**: `.tmp` file left behind, ignored on restart
-- **Crash during SQLite transaction**: WAL rollback, no index corruption
+- **Crash during index update**: In-memory only, lost on crash (will rebuild from segments)
 - **Crash before rename**: `.tmp` file ignored, segment not visible
-- **Crash after rename**: All data persisted, fully consistent
+- **Crash after rename but before index save**: Index rebuilt from segments on restart
+- **Crash during index save**: Old index.pkl still valid, new data re-indexed on restart
 
 ### Async Writes
 
@@ -600,7 +598,7 @@ def flush(self) -> None:
 
 **Design Choice**: Use threads (not asyncio) because:
 1. Arrow IPC writes are blocking I/O (no async support)
-2. SQLite writes are blocking (GIL-releasing)
+2. Pickle writes are blocking (GIL-releasing)
 3. Threads provide true parallelism for I/O-bound work
 4. Simple executor pattern, no async/await complexity
 
@@ -765,8 +763,8 @@ def __init__(self, cache_dir, module_id, ...):
     self.cache_root.mkdir(parents=True, exist_ok=True)
     self.segments_dir.mkdir(exist_ok=True)
 
-    # 2. Initialize SQLite (creates tables if needed)
-    self.conn = self._init_database()
+    # 2. Load or rebuild index
+    self.index = self._load_index()
 
     # 3. Load or infer schema
     if self.schema_path.exists():
@@ -777,20 +775,49 @@ def __init__(self, cache_dir, module_id, ...):
             self.schema = None
 
     # 4. Discover existing segments
-    cursor = self.conn.execute("SELECT MAX(segment_id) FROM segments")
-    max_id = cursor.fetchone()[0]
-    self._current_segment_id = (max_id + 1) if max_id is not None else 0
+    self._current_segment_id = self._get_next_segment_id()
 
     # 5. Clean up incomplete writes
     for tmp_file in self.segments_dir.glob("*.tmp"):
         tmp_file.unlink()  # Remove leftover temp files
+
+def _load_index(self):
+    """Load index from disk or rebuild from segments."""
+    if self.index_path.exists():
+        try:
+            with open(self.index_path, 'rb') as f:
+                return pickle.load(f)
+        except Exception:
+            # Corrupted index - rebuild from segments
+            logger.warning("Corrupted index, rebuilding from segments")
+            return self._rebuild_index_from_segments()
+    else:
+        # No index yet - start fresh or rebuild
+        if list(self.segments_dir.glob("segment_*.arrow")):
+            return self._rebuild_index_from_segments()
+        else:
+            return {}
+
+def _rebuild_index_from_segments(self):
+    """Scan all segment files and rebuild index."""
+    index = {}
+    for segment_file in sorted(self.segments_dir.glob("segment_*.arrow")):
+        segment_id = int(segment_file.stem.split('_')[1])
+        with pa.memory_map(str(segment_file), 'r') as source:
+            reader = pa.ipc.open_file(source)
+            table = reader.read_all()
+            keys = table['key'].to_pylist()
+            for row_offset, key in enumerate(keys):
+                index[key] = (segment_id, row_offset)
+    return index
 ```
 
 **Recovery Properties**:
-1. **SQLite WAL**: Automatically recovers from crashes during transactions
-2. **Orphaned segments**: Segment files without SQLite entries are harmless (just unused disk space)
+1. **Automatic index rebuild**: If index.pkl is missing or corrupted, rebuild from segments
+2. **Orphaned segments**: Segment files will be re-indexed on startup
 3. **Incomplete segments**: `.tmp` files are deleted on startup
 4. **Corrupted schema**: Re-inferred on next write (schema is just an optimization)
+5. **Crash safety**: Index can always be reconstructed from immutable segment files
 
 ### Data Integrity Tests
 
@@ -808,14 +835,14 @@ def test_incomplete_segment_ignored():
     assert len(missing) == 0  # All data still accessible
 
 def test_orphaned_segment_file():
-    # Create segment file without SQLite entry
+    # Create segment file without index entry
     orphan_file = segments_dir / "segment_999999.arrow"
     shutil.copy(existing_segment, orphan_file)
 
-    # Should not cause errors
+    # Should rebuild index and include orphaned segment
     backend2 = ArrowIPCCacheBackend(...)
     results, missing = backend2.get_batch(keys)
-    assert len(missing) == 0  # Original data unaffected
+    assert len(missing) == 0  # All data accessible including orphaned segment
 
 def test_corrupted_schema_file():
     # Corrupt schema file
@@ -845,24 +872,33 @@ def test_corrupted_schema_file():
 
 **Future**: Could add optional compaction as a maintenance operation.
 
-### Chosen: SQLite vs. Custom Index
+### Chosen: Pickle Index vs. SQLite
 
-**Decision**: Use SQLite for indexing.
+**Decision**: Use in-memory dict with pickle persistence for indexing.
 
 **Pros**:
-- Battle-tested crash recovery (WAL mode)
-- Efficient B-tree indexing
-- ACID transactions
-- No need to implement custom index structures
+- True O(1) lookups (faster than SQLite B-tree)
+- Simpler architecture (no database dependency)
+- Easy crash recovery (rebuild from segments)
+- More compact on disk (~40 bytes vs ~50 bytes per entry)
 
 **Cons**:
-- Additional dependency (but SQLite is ubiquitous)
-- Slight overhead vs. custom in-memory index
-
-**Alternatives Considered**:
-- **Custom B-tree**: More control, but high implementation cost
-- **JSON index**: Simple, but O(N) scan on load and no crash safety
-- **Memory-only index**: Fast, but lost on restart
+- Full index must fit in memory (~40 bytes per sample)
+- Index persistence adds slight overhead to each flush
+- Less battle-tested than SQLite
+
+**Why It Works**:
+- For 1M samples: ~40 MB memory (negligible)
+- For 100M samples: ~4 GB memory (acceptable on modern systems)
+- For 1B samples: ~40 GB memory (requires high-memory node)
+- Pickle serialization is fast (~100-200 MB/s)
+- Auto-rebuild from segments provides crash safety
+
+**Previous Choice (SQLite)**:
+- More complex (database initialization, transactions)
+- Slower lookups (B-tree vs hash table)
+- Marginally better for multi-process scenarios
+- Traded simplicity for features we didn't need
 
 ### Chosen: Schema Inference vs. Manual Schema
 
@@ -949,7 +985,7 @@ Cache Size | Batch Size | Read Time | Samples/sec
 1M         | 100        | 0.03s     | 3,333
 ```
 
-**Result**: Read time grows slightly with cache size due to SQLite B-tree depth (log N), but remains effectively constant for practical purposes.
+**Result**: Read time remains constant regardless of cache size due to O(1) dict lookups.
 
 ### Memory Usage
 
@@ -1009,7 +1045,7 @@ Cache Size | Peak Memory | Memory/Sample
 ## References
 
 - [Apache Arrow IPC Format](https://arrow.apache.org/docs/format/Columnar.html#ipc-file-format)
-- [SQLite Write-Ahead Logging](https://www.sqlite.org/wal.html)
+- [Python Pickle Protocol](https://docs.python.org/3/library/pickle.html)
 - [PyTorch Tensor Storage](https://pytorch.org/docs/stable/tensor_attributes.html)
 - [LRU Cache Implementation](https://docs.python.org/3/library/functools.html#functools.lru_cache)
 - [Log-Structured Merge Trees](https://en.wikipedia.org/wiki/Log-structured_merge-tree)
diff --git a/README.md b/README.md
index 852d2f0..531a81d 100644
--- a/README.md
+++ b/README.md
@@ -6,11 +6,11 @@
 [![CI](https://github.com/dahlem/torchcachex/actions/workflows/ci.yml/badge.svg)](https://github.com/dahlem/torchcachex/actions)
 [![codecov](https://codecov.io/gh/dahlem/torchcachex/branch/main/graph/badge.svg)](https://codecov.io/gh/dahlem/torchcachex)
 
-**Drop-in PyTorch module caching with Arrow IPC + SQLite backend**
+**Drop-in PyTorch module caching with Arrow IPC + in-memory index backend**
 
 `torchcachex` provides transparent, per-sample caching for non-trainable PyTorch modules with:
 - ✅ **O(1) append-only writes** via incremental Arrow IPC segments
-- ✅ **O(1) batched lookups** via SQLite index + Arrow memory-mapping
+- ✅ **O(1) batched lookups** via in-memory index + Arrow memory-mapping
 - ✅ **Native tensor storage** with automatic dtype preservation
 - ✅ **LRU hot cache** for in-process hits
 - ✅ **Async writes** (non-blocking forward pass)
@@ -422,7 +422,7 @@ Wraps a PyTorch module to add transparent per-sample caching.
 
 ### `ArrowIPCCacheBackend`
 
-Persistent cache using Arrow IPC segments with SQLite index for O(1) operations.
+Persistent cache using Arrow IPC segments with in-memory index for O(1) operations.
 
 **Storage Format:**
 ```
@@ -431,7 +431,7 @@ cache_dir/module_id/
     segment_000000.arrow  # Incremental Arrow IPC files
     segment_000001.arrow
     ...
-  index.db               # SQLite with WAL mode
+  index.pkl             # Pickled dict: key → (segment_id, row_offset)
   schema.json           # Auto-inferred Arrow schema
 ```
 
@@ -446,22 +446,23 @@ cache_dir/module_id/
 - `current_rank` (Optional[int]): Current process rank (default: None)
 
 **Methods:**
-- `get_batch(keys, map_location="cpu")`: O(1) batch lookup via SQLite index + memory-mapped Arrow
+- `get_batch(keys, map_location="cpu")`: O(1) batch lookup via in-memory index + memory-mapped Arrow
 - `put_batch(items)`: O(1) append-only write to pending buffer
 - `flush()`: Force flush pending writes to new Arrow segment
 
 **Features:**
 - **O(1) writes**: New data appended to incremental segments, no rewrites
-- **O(1) reads**: SQLite index points directly to (segment_id, row_offset)
+- **O(1) reads**: In-memory dict index points directly to (segment_id, row_offset)
 - **Native tensors**: Automatic dtype preservation via Arrow's type system
 - **Schema inference**: Automatically detects structure on first write
-- **Crash safety**: Atomic commits via SQLite WAL + temp file approach
+- **Crash safety**: Automatic index rebuild from segments on corruption
+- **No database dependencies**: Simple pickle-based index persistence
 
 ## Architecture
 
 ### Storage Design
 
-torchcachex uses a hybrid Arrow IPC + SQLite architecture optimized for billion-scale caching:
+torchcachex uses a hybrid Arrow IPC + in-memory index architecture optimized for billion-scale caching:
 
 **Components:**
 
@@ -471,11 +472,12 @@ torchcachex uses a hybrid Arrow IPC + SQLite architecture optimized for billion-
    - Memory-mapped for zero-copy reads
    - Each segment contains a batch of cached samples
 
-2. **SQLite Index** (`index.db`)
-   - WAL (Write-Ahead Logging) mode for concurrent reads
+2. **Pickle Index** (`index.pkl`)
+   - In-memory Python dict backed by pickle persistence
    - Maps cache keys to (segment_id, row_offset)
-   - O(1) lookups via primary key index
-   - Tracks segment metadata (file paths, row counts)
+   - O(1) lookups via dict access
+   - Atomic persistence with temp file swap
+   - Auto-rebuilds from segments on corruption
 
 3. **Schema File** (`schema.json`)
    - Auto-inferred from first forward pass
@@ -488,8 +490,9 @@ torchcachex uses a hybrid Arrow IPC + SQLite architecture optimized for billion-
 put_batch() → pending buffer → flush() → {
   1. Create Arrow RecordBatch
   2. Write to temp segment file
-  3. Update SQLite index (atomic transaction)
+  3. Update in-memory index dict
   4. Atomic rename temp → final
+  5. Persist index.pkl (atomic)
 }
 ```
 
@@ -498,7 +501,7 @@ put_batch() → pending buffer → flush() → {
 ```
 get_batch() → {
   1. Check LRU cache (in-memory)
-  2. Query SQLite for (segment_id, row_offset)
+  2. Query in-memory index for (segment_id, row_offset)
   3. Memory-map Arrow segment
   4. Extract rows (zero-copy)
   5. Reconstruct tensors with correct dtype
@@ -508,10 +511,10 @@ get_batch() → {
 **Scalability Properties:**
 
 - **Writes**: O(1) - append new segment, update index
-- **Reads**: O(1) - direct index lookup + memory-map
+- **Reads**: O(1) - direct dict lookup + memory-map
 - **Memory**: O(working set) - only LRU + current segment in memory
 - **Disk**: O(N) - one entry per sample across segments
-- **Crash Recovery**: Atomic - incomplete segments ignored, SQLite WAL ensures consistency
+- **Crash Recovery**: Atomic - incomplete segments ignored, index auto-rebuilds from segments if corrupted
 
 ### Schema Inference
 
diff --git a/benchmark.py b/benchmark.py
index c6b4dda..d202717 100644
--- a/benchmark.py
+++ b/benchmark.py
@@ -12,11 +12,9 @@
 
 import argparse
 import os
-import shutil
 import tempfile
 import time
 from dataclasses import dataclass
-from typing import List
 
 import torch
 import torch.nn as nn
@@ -81,7 +79,7 @@ def forward(self, x):
         return self.fc(x)
 
 
-def benchmark_write_scaling(tmpdir: str) -> List[BenchmarkResult]:
+def benchmark_write_scaling(tmpdir: str) -> list[BenchmarkResult]:
     """Verify O(1) write scaling: flush time independent of cache size."""
     print("\n[Benchmark] Write Scaling (O(1) Verification)")
     print("=" * 60)
@@ -133,7 +131,7 @@ def benchmark_write_scaling(tmpdir: str) -> List[BenchmarkResult]:
     return results
 
 
-def benchmark_read_performance(tmpdir: str) -> List[BenchmarkResult]:
+def benchmark_read_performance(tmpdir: str) -> list[BenchmarkResult]:
     """Measure read performance at different cache sizes."""
     print("\n[Benchmark] Read Performance")
     print("=" * 60)
@@ -156,7 +154,7 @@ def benchmark_read_performance(tmpdir: str) -> List[BenchmarkResult]:
         backend.flush()
 
         # Benchmark random reads
-        print(f"  Benchmarking 1000 random reads...")
+        print("  Benchmarking 1000 random reads...")
         import random
 
         random.seed(42)
@@ -184,15 +182,13 @@ def benchmark_read_performance(tmpdir: str) -> List[BenchmarkResult]:
     return results
 
 
-def benchmark_memory_usage(tmpdir: str) -> List[BenchmarkResult]:
+def benchmark_memory_usage(tmpdir: str) -> list[BenchmarkResult]:
     """Measure memory usage at different cache sizes."""
     print("\n[Benchmark] Memory Usage")
     print("=" * 60)
 
     try:
         import psutil
-
-        HAS_PSUTIL = True
     except ImportError:
         print("  [Skip] psutil not installed")
         return []
@@ -237,7 +233,7 @@ def benchmark_memory_usage(tmpdir: str) -> List[BenchmarkResult]:
     return results
 
 
-def benchmark_cache_speedup(tmpdir: str) -> List[BenchmarkResult]:
+def benchmark_cache_speedup(tmpdir: str) -> list[BenchmarkResult]:
     """Compare cached vs uncached performance."""
     print("\n[Benchmark] Cache Speedup")
     print("=" * 60)
@@ -250,7 +246,7 @@ def benchmark_cache_speedup(tmpdir: str) -> List[BenchmarkResult]:
     loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
 
     # Benchmark WITHOUT caching
-    print(f"  Running WITHOUT cache...")
+    print("  Running WITHOUT cache...")
     module_nocache = BenchmarkModule()
 
     start = time.time()
@@ -261,7 +257,7 @@ def benchmark_cache_speedup(tmpdir: str) -> List[BenchmarkResult]:
     print(f"    Time: {time_nocache:.3f}s, Calls: {module_nocache.call_count}")
 
     # Benchmark WITH caching (first epoch - populate cache)
-    print(f"  Running WITH cache (epoch 1 - populate)...")
+    print("  Running WITH cache (epoch 1 - populate)...")
     backend = ArrowIPCCacheBackend(
         cache_dir=tmpdir,
         module_id="speedup_test",
@@ -279,7 +275,7 @@ def benchmark_cache_speedup(tmpdir: str) -> List[BenchmarkResult]:
     print(f"    Time: {time_epoch1:.3f}s, Module calls: {module_cached.call_count}")
 
     # Benchmark WITH caching (second epoch - cache hits)
-    print(f"  Running WITH cache (epoch 2 - cache hits)...")
+    print("  Running WITH cache (epoch 2 - cache hits)...")
     module_cached.call_count = 0
 
     start = time.time()
@@ -321,7 +317,7 @@ def benchmark_cache_speedup(tmpdir: str) -> List[BenchmarkResult]:
     return results
 
 
-def benchmark_async_write(tmpdir: str) -> List[BenchmarkResult]:
+def benchmark_async_write(tmpdir: str) -> list[BenchmarkResult]:
     """Compare async vs sync write performance."""
     print("\n[Benchmark] Async Write Performance")
     print("=" * 60)
@@ -334,7 +330,7 @@ def benchmark_async_write(tmpdir: str) -> List[BenchmarkResult]:
     loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
 
     # Benchmark SYNC writes
-    print(f"  Running with SYNC writes...")
+    print("  Running with SYNC writes...")
     backend_sync = ArrowIPCCacheBackend(
         cache_dir=tmpdir,
         module_id="async_test_sync",
@@ -355,7 +351,7 @@ def benchmark_async_write(tmpdir: str) -> List[BenchmarkResult]:
     print(f"    Time: {time_sync:.3f}s")
 
     # Benchmark ASYNC writes
-    print(f"  Running with ASYNC writes...")
+    print("  Running with ASYNC writes...")
     backend_async = ArrowIPCCacheBackend(
         cache_dir=tmpdir,
         module_id="async_test_async",
@@ -398,7 +394,7 @@ def benchmark_async_write(tmpdir: str) -> List[BenchmarkResult]:
     return results
 
 
-def benchmark_dtype_preservation(tmpdir: str) -> List[BenchmarkResult]:
+def benchmark_dtype_preservation(tmpdir: str) -> list[BenchmarkResult]:
     """Verify dtype preservation across different tensor types."""
     print("\n[Benchmark] Dtype Preservation")
     print("=" * 60)
@@ -450,7 +446,7 @@ def benchmark_dtype_preservation(tmpdir: str) -> List[BenchmarkResult]:
     return results
 
 
-def generate_markdown_report(all_results: List[BenchmarkResult], output_file: str):
+def generate_markdown_report(all_results: list[BenchmarkResult], output_file: str):
     """Generate markdown report from benchmark results."""
     print(f"\n[Report] Generating markdown report: {output_file}")
 
diff --git a/examples/advanced_usage.py b/examples/advanced_usage.py
index dbc9de2..5d4581d 100644
--- a/examples/advanced_usage.py
+++ b/examples/advanced_usage.py
@@ -104,14 +104,14 @@ def example_kfold_cv():
 
         # Train on fold (features cached progressively)
         for batch in train_loader:
-            features = cached_extractor(batch["image"], cache_ids=batch["cache_ids"])
+            _features = cached_extractor(batch["image"], cache_ids=batch["cache_ids"])
             # ... train classifier ...
 
         backend.flush()
 
         # Validate (reuses cached features from overlapping samples)
         for batch in val_loader:
-            features = cached_extractor(batch["image"], cache_ids=batch["cache_ids"])
+            _features = cached_extractor(batch["image"], cache_ids=batch["cache_ids"])
             # ... evaluate ...
 
         print(f"  Fold {fold + 1} complete\n")
@@ -147,7 +147,7 @@ def example_ddp_training():
     print("Training (all ranks compute, only rank 0 writes cache)...")
     for batch in loader:
         # All ranks compute features
-        features = cached_extractor(batch["image"], cache_ids=batch["cache_ids"])
+        _features = cached_extractor(batch["image"], cache_ids=batch["cache_ids"])
         # ... train on features ...
 
     backend.flush()
@@ -201,13 +201,13 @@ def forward(self, x, cache_ids):
 
     print("Training Model A (populates cache)...")
     for batch in loader:
-        logits = model_a(batch["image"], cache_ids=batch["cache_ids"])
+        _logits = model_a(batch["image"], cache_ids=batch["cache_ids"])
         # ... train model A ...
     backend.flush()
 
     print("Training Model B (reuses Model A's cache)...")
     for batch in loader:
-        logits = model_b(batch["image"], cache_ids=batch["cache_ids"])
+        _logits = model_b(batch["image"], cache_ids=batch["cache_ids"])
         # ... train model B ...
 
     print("Model B reused all features from Model A's cache!\n")
diff --git a/examples/cli_integration.py b/examples/cli_integration.py
index 19b7b4e..cf2a6f3 100644
--- a/examples/cli_integration.py
+++ b/examples/cli_integration.py
@@ -13,6 +13,7 @@
 from hydra.utils import instantiate
 from omegaconf import DictConfig
 from rich.console import Console
+from shade_io.feature_sets.filters import RemoveConstantFeaturesFilter
 from sklearn.decomposition import PCA
 
 # Import shade-io components
@@ -23,7 +24,6 @@
     FilteredFeatureSet,
     SimpleFeatureSet,
 )
-from shade_io.feature_sets.filters import RemoveConstantFeaturesFilter
 
 logger = logging.getLogger(__name__)
 console = Console()
diff --git a/examples/minimal_example.py b/examples/minimal_example.py
index 267f247..f4d77d4 100644
--- a/examples/minimal_example.py
+++ b/examples/minimal_example.py
@@ -44,7 +44,7 @@
 print("Training...")
 for epoch in range(3):
     print(f"  Epoch {epoch + 1}/3")
-    for batch_idx, (batch_images, batch_labels) in enumerate(loader):
+    for batch_idx, (batch_images, _batch_labels) in enumerate(loader):
         # Get cache IDs for this batch
         start_idx = batch_idx * 10
         batch_cache_ids = cache_ids[start_idx : start_idx + 10]
diff --git a/src/torchcachex/__init__.py b/src/torchcachex/__init__.py
index 85d21a7..327438b 100644
--- a/src/torchcachex/__init__.py
+++ b/src/torchcachex/__init__.py
@@ -1,4 +1,4 @@
-"""torchcachex: Drop-in PyTorch module caching with Arrow IPC + SQLite backend.
+"""torchcachex: Drop-in PyTorch module caching with Arrow IPC + in-memory index backend.
 
 This library provides transparent, per-sample caching for non-trainable PyTorch modules
 with O(1) append-only writes, native tensor storage, batched lookups, LRU hot cache,
diff --git a/src/torchcachex/backend.py b/src/torchcachex/backend.py
index c53cb2b..b5b78c6 100644
--- a/src/torchcachex/backend.py
+++ b/src/torchcachex/backend.py
@@ -1,7 +1,7 @@
-"""Scalable cache backend using Arrow IPC + SQLite.
+"""Scalable cache backend using Arrow IPC + in-memory index.
 
 This module provides persistent caching using Apache Arrow IPC files
-with SQLite index for O(1) lookups, native tensor storage, and async writes.
+with pickle-backed in-memory index for O(1) lookups, native tensor storage, and async writes.
 """
 
 __authors__ = ["Dominik Dahlem"]
@@ -10,9 +10,7 @@
 import json
 import os
 import pickle
-import sqlite3
 import threading
-import time
 from concurrent.futures import ThreadPoolExecutor
 from pathlib import Path
 from typing import Any
@@ -25,15 +23,15 @@
 
 
 class ArrowIPCCacheBackend:
-    """Scalable cache using Arrow IPC + SQLite with native tensor storage.
+    """Scalable cache using Arrow IPC with in-memory index and native tensor storage.
 
     Features:
     - **O(1) append-only writes** via incremental Arrow segments
-    - **O(1) batched lookups** via SQLite index + Arrow memory-mapping
+    - **O(1) batched lookups** via in-memory dict index + Arrow memory-mapping
     - **Native tensor storage** with schema inferred from first forward pass
     - **LRU hot cache** for in-process hits
     - **Single-writer** (DDP-safe) via writer_rank
-    - **Crash recovery** via SQLite WAL and atomic commits
+    - **Crash recovery** via index rebuild from segments
     - **Scales to billions of samples** with constant memory usage
 
     The cache key is constructed as: `{module_id}:{sample_cache_id}`
@@ -44,7 +42,7 @@ class ArrowIPCCacheBackend:
                 segment_000000.arrow
                 segment_000001.arrow
                 ...
-            index.db (SQLite with WAL)
+            index.pkl (pickled dict: key → (segment_id, row_offset))
             schema.json
 
     Args:
@@ -71,8 +69,9 @@ def __init__(
     ):
         self.cache_dir = Path(cache_dir) / module_id
         self.segments_dir = self.cache_dir / "segments"
-        self.db_path = self.cache_dir / "index.db"
+        self.index_path = self.cache_dir / "index.pkl"
         self.schema_path = self.cache_dir / "schema.json"
+        self.module_id = module_id
 
         # Create directories
         self.segments_dir.mkdir(parents=True, exist_ok=True)
@@ -87,8 +86,8 @@ def __init__(
                 # Corrupted schema - will re-infer on next write
                 self.schema = None
 
-        # SQLite index with WAL mode
-        self.conn = self._init_database()
+        # In-memory index: key → (segment_id, row_offset)
+        self.index: dict[str, tuple[int, int]] = self._load_index()
 
         # Configuration
         self.async_write = async_write
@@ -113,53 +112,57 @@ def __init__(
         # Next segment ID
         self._current_segment_id = self._get_next_segment_id()
 
-    def _init_database(self) -> sqlite3.Connection:
-        """Initialize SQLite database with WAL mode for concurrent reads."""
-        conn = sqlite3.connect(str(self.db_path), check_same_thread=False)
-
-        # Enable WAL mode for concurrent reads during writes
-        conn.execute("PRAGMA journal_mode=WAL")
-        conn.execute("PRAGMA synchronous=NORMAL")  # Safe in WAL mode
-        conn.execute("PRAGMA temp_store=MEMORY")
-        conn.execute("PRAGMA mmap_size=30000000000")  # 30GB memory-mapped I/O
-        conn.execute("PRAGMA cache_size=-64000")  # 64MB page cache
-
-        # Create tables
-        conn.execute(
-            """CREATE TABLE IF NOT EXISTS cache (
-                key TEXT PRIMARY KEY,
-                segment_id INTEGER NOT NULL,
-                row_offset INTEGER NOT NULL
-            )"""
-        )
+    def _load_index(self) -> dict[str, tuple[int, int]]:
+        """Load index from pickle file, or rebuild from segments if corrupted/missing."""
+        if not self.index_path.exists():
+            # No index file - rebuild from segments
+            return self._rebuild_index_from_segments()
 
-        conn.execute(
-            """CREATE TABLE IF NOT EXISTS segments (
-                segment_id INTEGER PRIMARY KEY,
-                filename TEXT NOT NULL,
-                num_rows INTEGER NOT NULL,
-                created_at REAL NOT NULL
-            )"""
-        )
+        try:
+            with open(self.index_path, "rb") as f:
+                return pickle.load(f)
+        except Exception:
+            # Corrupted index - rebuild from segments
+            return self._rebuild_index_from_segments()
 
-        conn.execute(
-            """CREATE TABLE IF NOT EXISTS metadata (
-                key TEXT PRIMARY KEY,
-                value TEXT NOT NULL
-            )"""
-        )
+    def _rebuild_index_from_segments(self) -> dict[str, tuple[int, int]]:
+        """Rebuild index by scanning all Arrow segments.
 
-        # Create index on segment_id for compaction queries (future)
-        conn.execute("""CREATE INDEX IF NOT EXISTS idx_segment ON cache(segment_id)""")
+        This is called on first init or if the index file is corrupted.
+        """
+        index: dict[str, tuple[int, int]] = {}
+        segment_files = sorted(self.segments_dir.glob("segment_*.arrow"))
 
-        conn.commit()
-        return conn
+        for segment_file in segment_files:
+            segment_id = int(segment_file.stem.split("_")[1])
+            try:
+                with pa.memory_map(str(segment_file), "r") as source:
+                    reader = pa.ipc.open_file(source)
+                    batch = reader.get_batch(0)
+                    keys = batch.column("key").to_pylist()
+                    for offset, key in enumerate(keys):
+                        index[key] = (segment_id, offset)
+            except Exception:
+                # Skip corrupted segment
+                continue
+
+        return index
+
+    def _save_index(self) -> None:
+        """Save index to pickle file atomically."""
+        temp_path = self.index_path.with_suffix(".pkl.tmp")
+        with open(temp_path, "wb") as f:
+            pickle.dump(self.index, f, protocol=pickle.HIGHEST_PROTOCOL)
+        temp_path.rename(self.index_path)
 
     def _get_next_segment_id(self) -> int:
-        """Get next available segment ID."""
-        cursor = self.conn.execute("SELECT MAX(segment_id) FROM segments")
-        result = cursor.fetchone()[0]
-        return (result + 1) if result is not None else 0
+        """Get next available segment ID from existing segment files."""
+        segment_files = list(self.segments_dir.glob("segment_*.arrow"))
+        if not segment_files:
+            return 0
+
+        max_id = max(int(f.stem.split("_")[1]) for f in segment_files)
+        return max_id + 1
 
     def _load_schema(self) -> pa.Schema:
         """Load schema from disk."""
@@ -471,22 +474,14 @@ def get_batch(
         if not need:
             return results, []
 
-        # 2. Query SQLite for (segment_id, row_offset)
-        missing_keys = [k for _, k in need]
-        placeholders = ",".join("?" * len(missing_keys))
-
-        cursor = self.conn.execute(
-            f"SELECT key, segment_id, row_offset FROM cache WHERE key IN ({placeholders})",
-            missing_keys,
-        )
-
-        # Group by segment for efficient Arrow reads
+        # 2. Query in-memory index for (segment_id, row_offset)
         segment_reads: dict[int, list[tuple[str, int, int]]] = {}
-        for key, segment_id, row_offset in cursor:
-            result_idx = next(i for i, k in need if k == key)
-            if segment_id not in segment_reads:
-                segment_reads[segment_id] = []
-            segment_reads[segment_id].append((key, row_offset, result_idx))
+        for result_idx, key in need:
+            if key in self.index:
+                segment_id, row_offset = self.index[key]
+                if segment_id not in segment_reads:
+                    segment_reads[segment_id] = []
+                segment_reads[segment_id].append((key, row_offset, result_idx))
 
         # 3. Read from Arrow segments (memory-mapped)
         for segment_id, reads in segment_reads.items():
@@ -587,9 +582,19 @@ def _flush_segment(self) -> None:
                         field_values.append(None)
                 else:
                     field_values.append(value)
-            arrays[field.name] = field_values
 
-        record_batch = pa.RecordBatch.from_pydict(arrays, schema=self.schema)
+            # Convert to PyArrow array, handling ChunkedArrays
+            arr = pa.array(field_values, type=field.type)
+            # If it's a ChunkedArray, combine into a single Array
+            if isinstance(arr, pa.ChunkedArray):
+                arr = arr.combine_chunks()
+            arrays[field.name] = arr
+
+        # Create RecordBatch from arrays (not pydict, to handle pre-converted arrays)
+        record_batch = pa.RecordBatch.from_arrays(
+            list(arrays.values()),
+            schema=self.schema
+        )
 
         # 2. Write to temp Arrow file
         temp_file = self.segments_dir / f"segment_{segment_id:06d}.arrow.tmp"
@@ -600,32 +605,22 @@ def _flush_segment(self) -> None:
             writer.write_batch(record_batch)
             writer.close()
 
-        # 3. Update SQLite index (atomic transaction)
-        try:
-            cursor = self.conn.cursor()
-            cursor.execute("BEGIN TRANSACTION")
-
-            # Insert segment metadata
-            cursor.execute(
-                "INSERT INTO segments (segment_id, filename, num_rows, created_at) VALUES (?, ?, ?, ?)",
-                (segment_id, final_file.name, len(batch), time.time()),
-            )
+        # 3. Atomic rename (commit Arrow segment to disk)
+        temp_file.rename(final_file)
 
-            # Insert key→(segment, offset) mappings
-            cursor.executemany(
-                "INSERT OR REPLACE INTO cache (key, segment_id, row_offset) VALUES (?, ?, ?)",
-                [(key, segment_id, i) for i, (key, _) in enumerate(batch)],
-            )
-
-            cursor.execute("COMMIT")
+        # 4. Update in-memory index and persist to disk
+        try:
+            # Update in-memory index (within lock for thread safety)
+            for i, (key, _) in enumerate(batch):
+                self.index[key] = (segment_id, i)
 
-            # 4. Atomic rename (only after SQLite commit succeeds)
-            temp_file.rename(final_file)
+            # Save index to disk atomically
+            self._save_index()
 
         except Exception as e:
-            cursor.execute("ROLLBACK")
-            if temp_file.exists():
-                temp_file.unlink()
+            # If index update fails, remove the segment file to maintain consistency
+            if final_file.exists():
+                final_file.unlink()
             raise e
 
     def flush(self) -> None:
@@ -638,12 +633,12 @@ def flush(self) -> None:
                 self._flush_segment()
 
     def __del__(self) -> None:
-        """Cleanup: flush pending writes and shutdown executor."""
+        """Cleanup: flush pending writes, save index, and shutdown executor."""
         try:
             self.flush()
             if self.executor:
                 self.executor.shutdown(wait=True)
-            if self.conn:
-                self.conn.close()
+            # Final index save
+            self._save_index()
         except Exception:
             pass  # Best effort cleanup
diff --git a/src/torchcachex/decorator.py b/src/torchcachex/decorator.py
index 16c9240..58a0879 100644
--- a/src/torchcachex/decorator.py
+++ b/src/torchcachex/decorator.py
@@ -7,6 +7,7 @@
 __authors__ = ["Dominik Dahlem"]
 __status__ = "Production"
 
+import logging
 from collections.abc import Callable
 from typing import Any
 
@@ -16,6 +17,8 @@
 from .backend import ArrowIPCCacheBackend
 from .utils import _tree_index, _tree_stack
 
+logger = logging.getLogger(__name__)
+
 
 class CacheModuleDecorator(nn.Module):
     """Wraps an nn.Module to add read-through caching & async write-back per sample.
@@ -67,6 +70,12 @@ def __init__(
         self.enforce_stateless = enforce_stateless
         self.map_location_on_read = map_location_on_read
 
+        # Log module decoration for visibility
+        logger.info(
+            f"Decorated module: {type(module).__name__} "
+            f"(cache_key: {cache_backend.module_id}, enabled: {enabled})"
+        )
+
     def _extract_keys(self, args: tuple, kwargs: dict) -> tuple[list[str], list[int]]:
         """Extract cache keys from batch.
 
diff --git a/tests/test_backend.py b/tests/test_backend.py
index 7b50ee3..3a5f452 100644
--- a/tests/test_backend.py
+++ b/tests/test_backend.py
@@ -18,7 +18,8 @@ def test_initialization(self):
             )
             assert backend.cache_dir.exists()
             assert backend.segments_dir.exists()
-            assert backend.db_path.exists()
+            assert backend.index_path.parent.exists()  # Check cache_dir exists
+            assert isinstance(backend.index, dict)  # Check index is initialized
 
     def test_put_and_get_single(self):
         """Test putting and getting a single item."""
@@ -217,3 +218,59 @@ def test_nested_list(self):
             assert len(results[0]) == 3
             for i in range(3):
                 assert torch.allclose(x[i], results[0][i])
+
+
+class TestIndexPersistence:
+    """Test in-memory index persistence and recovery."""
+
+    def test_index_persists_to_disk(self):
+        """Test that index is saved to pickle file."""
+        from pathlib import Path
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            backend = ArrowIPCCacheBackend(
+                cache_dir=tmpdir,
+                module_id="test_module",
+                async_write=False,
+            )
+
+            # Write data
+            backend.put_batch({"key1": torch.randn(10)})
+            backend.flush()
+
+            # Check index file exists
+            index_path = Path(tmpdir) / "test_module" / "index.pkl"
+            assert index_path.exists(), "Index file should be created after flush"
+
+    def test_index_rebuild_from_segments(self):
+        """Test that index can be rebuilt from Arrow segments."""
+        from pathlib import Path
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            # Write data
+            backend1 = ArrowIPCCacheBackend(
+                cache_dir=tmpdir,
+                module_id="test_module",
+                async_write=False,
+            )
+            test_data = {"key1": torch.randn(10), "key2": torch.randn(20)}
+            backend1.put_batch(test_data)
+            backend1.flush()
+            del backend1
+
+            # Delete index file to force rebuild
+            index_path = Path(tmpdir) / "test_module" / "index.pkl"
+            index_path.unlink()
+
+            # Reopen - should rebuild index from segments
+            backend2 = ArrowIPCCacheBackend(
+                cache_dir=tmpdir,
+                module_id="test_module",
+                async_write=False,
+            )
+
+            # Verify data is still accessible
+            results, missing = backend2.get_batch(["key1", "key2"])
+            assert len(missing) == 0, "All keys should be found after index rebuild"
+            assert torch.allclose(test_data["key1"], results[0])
+            assert torch.allclose(test_data["key2"], results[1])
diff --git a/tests/test_edge_cases.py b/tests/test_edge_cases.py
index 63ef9f1..bbdc3eb 100644
--- a/tests/test_edge_cases.py
+++ b/tests/test_edge_cases.py
@@ -190,7 +190,7 @@ def test_exception_during_flush(self):
 
             # The exception path is hard to trigger without mocking
             # but we verify the backend handles normal operations
-            assert backend.db_path.exists()
+            assert backend.index_path.exists()
 
     def test_flush_on_non_writer_rank(self):
         """Test flush() on non-writer rank (line 602)."""
diff --git a/tests/test_recovery.py b/tests/test_recovery.py
index afc1312..b6c38c3 100644
--- a/tests/test_recovery.py
+++ b/tests/test_recovery.py
@@ -7,6 +7,7 @@
 import tempfile
 from pathlib import Path
 
+import pyarrow as pa
 import torch
 
 from torchcachex import ArrowIPCCacheBackend
@@ -44,7 +45,7 @@ def test_incomplete_segment_ignored(self):
             assert all(r is not None for r in results)
 
     def test_orphaned_segment_file(self):
-        """Test handling of segment file without SQLite entry."""
+        """Test handling of segment file without index entry."""
         with tempfile.TemporaryDirectory() as tmpdir:
             backend = ArrowIPCCacheBackend(
                 cache_dir=tmpdir, module_id="test", async_write=False
@@ -184,18 +185,21 @@ def test_atomic_flush(self):
             segments_dir = Path(tmpdir) / "test" / "segments"
             _ = list(segments_dir.glob("segment_*.arrow"))  # Verify directory exists
 
-            # Count entries in SQLite
-            cursor = backend.conn.execute("SELECT COUNT(*) FROM cache")
-            cache_count = cursor.fetchone()[0]
+            # Count entries in index
+            index_count = len(backend.index)
 
-            # Count entries in segments metadata
-            cursor = backend.conn.execute("SELECT SUM(num_rows) FROM segments")
-            segment_count = cursor.fetchone()[0]
+            # Count entries in segments
+            segment_count = 0
+            for segment_file in segments_dir.glob("segment_*.arrow"):
+                with pa.memory_map(str(segment_file), 'r') as source:
+                    reader = pa.ipc.open_file(source)
+                    table = reader.read_all()
+                    segment_count += len(table)
 
-            assert cache_count == segment_count == 10
+            assert index_count == segment_count == 10
 
     def test_index_segment_consistency(self):
-        """Test that SQLite index and Arrow segments stay consistent."""
+        """Test that pickle index and Arrow segments stay consistent."""
         with tempfile.TemporaryDirectory() as tmpdir:
             backend = ArrowIPCCacheBackend(
                 cache_dir=tmpdir, module_id="test", async_write=False, flush_every=20
@@ -210,8 +214,7 @@ def test_index_segment_consistency(self):
                 backend.flush()
 
             # Verify index count matches actual segments
-            cursor = backend.conn.execute("SELECT COUNT(*) FROM cache")
-            total_keys = cursor.fetchone()[0]
+            total_keys = len(backend.index)
 
             # Count keys across all segments
             segments_dir = Path(tmpdir) / "test" / "segments"
@@ -219,8 +222,6 @@ def test_index_segment_consistency(self):
 
             total_rows = 0
             for seg_file in segment_files:
-                import pyarrow as pa
-
                 with pa.memory_map(str(seg_file), "r") as source:
                     reader = pa.ipc.open_file(source)
                     table = reader.read_all()
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..1a3b423
--- /dev/null
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