refactor(sqlite-provider): switch from pack_key to monotonic integer keys

CLAUDE.md

@@ -0,0 +1,17 @@
+# CLAUDE.md
+
+## Project Overview
+
+A DataFusion extension crate for vector similarity search (ANN) using USearch HNSW indices. Provides optimizer rules, UDFs (l2_distance, cosine_distance, negative_dot), and pluggable lookup providers (Parquet, SQLite) for retrieving non-embedding columns by key.
+
+## Git Commits
+
+Use conventional format: `<type>(<scope>): <subject>` where type = feat|fix|docs|style|refactor|test|chore|perf. Subject: 50 chars max, imperative mood ("add" not "added"), no period.
+
+## Pre-Push Checklist
+
+Always run these before pushing:
+
+1. `cargo fmt --check` — fix any formatting issues
+2. `cargo clippy -- -D warnings` — no warnings allowed
+3. `cargo test --features sqlite-provider` — all tests must pass (sqlite-provider feature needed for SQLite provider tests)

src/sqlite_provider.rs

@@ -35,7 +35,6 @@ use datafusion::physical_plan::{
 use rusqlite::{Connection, types::Value as SqlValue};
 use tokio::sync::Semaphore;
 
-use crate::keys::{DatasetLayout, pack_key};
 use crate::lookup::PointLookupProvider;
 
 // ── Provider ──────────────────────────────────────────────────────────────────
@@ -106,15 +105,15 @@ impl SqliteLookupProvider {
     /// parquet files on first run.  Opens a pool of `pool_size` read
     /// connections (WAL allows N concurrent readers).
     ///
-    /// `local_parquet_files`, `layout`, `schema`, and `parquet_col_indices`
-    /// are only used if the table does not yet exist.
-    #[allow(clippy::too_many_arguments)]
+    /// `local_parquet_files`, `schema`, and `parquet_col_indices`
+    /// are only used if the table does not yet exist.  Row keys are assigned
+    /// as monotonic integers (0, 1, 2, …) in file-iteration order; any
+    /// USearch index used alongside this provider must use the same scheme.
     pub fn open_or_build(
         db_path: &str,
         table_name: &str,
         pool_size: usize,
         local_parquet_files: &[String],
-        layout: &DatasetLayout,
         schema: SchemaRef,
         parquet_col_indices: &[usize],
     ) -> DFResult<Self> {
@@ -156,7 +155,6 @@ impl SqliteLookupProvider {
                 &conn,
                 table_name,
                 local_parquet_files,
-                layout,
                 &schema,
                 parquet_col_indices,
             )?;
@@ -581,7 +579,6 @@ fn build_table(
     conn: &Connection,
     table_name: &str,
     parquet_files: &[String],
-    layout: &DatasetLayout,
     schema: &SchemaRef,
     parquet_col_indices: &[usize],
 ) -> DFResult<()> {
@@ -628,7 +625,9 @@ fn build_table(
             .prepare(&insert_sql)
             .map_err(|e| DataFusionError::Execution(e.to_string()))?;
 
-        for (file_idx, file_path) in parquet_files.iter().enumerate() {
+        let mut global_row_idx: u64 = 0;
+
+        for file_path in parquet_files {
             let f = std::fs::File::open(file_path)
                 .map_err(|e| DataFusionError::Execution(format!("open {file_path}: {e}")))?;
             let builder = parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder::try_new(f)
@@ -637,20 +636,17 @@ fn build_table(
                 .with_batch_size(2048)
                 .build()
                 .map_err(|e| DataFusionError::Execution(e.to_string()))?;
-            let mut file_row: u64 = 0;
 
             for batch_result in reader {
                 let batch = batch_result.map_err(|e| DataFusionError::Execution(e.to_string()))?;
                 let n = batch.num_rows();
 
                 for row_i in 0..n {
-                    let r = file_row + row_i as u64;
-                    let rg = layout.rg_cum_rows[file_idx].partition_point(|&s| s <= r) - 1;
-                    let lo = (r - layout.rg_cum_rows[file_idx][rg]) as usize;
-                    let packed_key = pack_key(file_idx, rg, lo);
+                    let key = global_row_idx;
+                    global_row_idx += 1;
 
                     let mut params: Vec<SqlValue> = Vec::with_capacity(schema.fields().len());
-                    params.push(SqlValue::Integer(packed_key as i64));
+                    params.push(SqlValue::Integer(key as i64));
 
                     for &ci in parquet_col_indices {
                         params.push(arrow_cell_to_sql(batch.column(ci), row_i));
@@ -659,7 +655,6 @@ fn build_table(
                     stmt.execute(rusqlite::params_from_iter(params.iter()))
                         .map_err(|e| DataFusionError::Execution(e.to_string()))?;
                 }
-                file_row += n as u64;
             }
         }
     }

tests/sqlite_provider_test.rs

@@ -6,9 +6,7 @@ use arrow_array::{Array, RecordBatch, StringArray, UInt64Array};
 use arrow_schema::{DataType, Field, Schema};
 use datafusion::catalog::TableProvider;
 use datafusion::prelude::SessionContext;
-use datafusion_vector_search_ext::{
-    DatasetLayout, PointLookupProvider, SqliteLookupProvider, pack_key,
-};
+use datafusion_vector_search_ext::{PointLookupProvider, SqliteLookupProvider};
 use parquet::arrow::ArrowWriter;
 use tempfile::tempdir;
 
@@ -38,13 +36,6 @@ fn make_provider(dir: &tempfile::TempDir) -> SqliteLookupProvider {
     writer.write(&batch).unwrap();
     writer.close().unwrap();
 
-    // Build a minimal DatasetLayout for 1 file with 1 row group of 3 rows.
-    let layout = DatasetLayout {
-        file_keys: vec!["parquet/test.parquet".to_string()],
-        file_cum_rows: vec![0, 3],
-        rg_cum_rows: vec![vec![0, 3]],
-    };
-
     let db_path = dir.path().join("test.db");
     let parquet_files = vec![parquet_path.to_str().unwrap().to_string()];
 
@@ -53,7 +44,6 @@ fn make_provider(dir: &tempfile::TempDir) -> SqliteLookupProvider {
         "models",
         4,
         &parquet_files,
-        &layout,
         provider_schema,
         &[0], // parquet col 0 (name) → provider col 1
     )
@@ -65,10 +55,8 @@ async fn test_fetch_existing_keys() {
     let dir = tempdir().unwrap();
     let provider = make_provider(&dir);
 
-    let key0 = pack_key(0, 0, 0);
-    let key2 = pack_key(0, 0, 2);
     let batches = provider
-        .fetch_by_keys(&[key0, key2], "row_idx", None)
+        .fetch_by_keys(&[0, 2], "row_idx", None)
         .await
         .unwrap();
 
@@ -98,10 +86,9 @@ async fn test_projection() {
     let dir = tempdir().unwrap();
     let provider = make_provider(&dir);
 
-    let key1 = pack_key(0, 0, 1);
     // Project only row_idx (index 0).
     let batches = provider
-        .fetch_by_keys(&[key1], "row_idx", Some(&[0]))
+        .fetch_by_keys(&[1], "row_idx", Some(&[0]))
         .await
         .unwrap();
 
@@ -114,17 +101,16 @@ async fn test_projection() {
         .as_any()
         .downcast_ref::<UInt64Array>()
         .unwrap();
-    assert_eq!(row_idx_col.value(0), key1);
+    assert_eq!(row_idx_col.value(0), 1);
 }
 
 #[tokio::test]
 async fn test_missing_keys_return_empty() {
     let dir = tempdir().unwrap();
     let provider = make_provider(&dir);
 
-    let missing = pack_key(0, 0, 99); // offset 99 doesn't exist
     let batches = provider
-        .fetch_by_keys(&[missing], "row_idx", None)
+        .fetch_by_keys(&[99], "row_idx", None)
         .await
         .unwrap();
     let total_rows: usize = batches.iter().map(|b| b.num_rows()).sum();
@@ -203,29 +189,18 @@ async fn test_table_name_with_spaces() {
     writer.write(&batch).unwrap();
     writer.close().unwrap();
 
-    let layout = DatasetLayout {
-        file_keys: vec!["parquet/test.parquet".to_string()],
-        file_cum_rows: vec![0, 1],
-        rg_cum_rows: vec![vec![0, 1]],
-    };
-
     let db_path = dir.path().join("test.db");
     // Table name with spaces — previously this would have produced a SQL syntax error.
     let provider = SqliteLookupProvider::open_or_build(
         db_path.to_str().unwrap(),
         "my models",
         2,
         &[parquet_path.to_str().unwrap().to_string()],
-        &layout,
         provider_schema,
         &[0],
     )
     .unwrap();
 
-    let key0 = pack_key(0, 0, 0);
-    let batches = provider
-        .fetch_by_keys(&[key0], "row_idx", None)
-        .await
-        .unwrap();
+    let batches = provider.fetch_by_keys(&[0], "row_idx", None).await.unwrap();
     assert_eq!(batches.iter().map(|b| b.num_rows()).sum::<usize>(), 1);
 }