fix(rule): index ORDER BY-only distance with WHERE (trimmed projection shape)

src/rule.rs

@@ -11,6 +11,19 @@
 //       Filter(predicate)                      ← WHERE clause absorbed
 //         TableScan(name)
 //
+// Patterns matched (TopDown, Projection node):
+//
+//   Projection([output cols])                  ← SELECT list without the distance
+//     Sort(l2_distance(vector, lit), fetch=k)  ← distance inline in ORDER BY
+//       [Projection([output cols + vector])]   ← optional; DataFusion materializes
+//         [Filter(predicate)]                     the vector only to feed the Sort
+//           TableScan(name)
+//
+// In the Projection-anchored shape, producibility is judged on the OUTER
+// projection (the query's real output): `SELECT id … ORDER BY l2_distance(…)`
+// rewrites, while `SELECT *` / `SELECT id, vector` still fall back because the
+// node cannot produce the vector column (issue #508).
+//
 // When a Filter node is present its predicate is stored in USearchNode.filters.
 // The physical planner then runs adaptive filtered search:
 //   - high selectivity → usearch::Index::filtered_search (in-graph filtering)
@@ -82,15 +95,35 @@ impl USearchRule {
                 let LogicalPlan::Sort(sort) = outer.input.as_ref() else {
                     return None;
                 };
-                // Only the passthrough shape; the remap shape (projection *below*
-                // the Sort) is handled when we visit the Sort above.
-                if !matches!(
-                    sort.input.as_ref(),
-                    LogicalPlan::TableScan(_) | LogicalPlan::Filter(_)
-                ) {
-                    return None;
+                match sort.input.as_ref() {
+                    // Passthrough shape: Sort rests directly on the scan.
+                    LogicalPlan::TableScan(_) | LogicalPlan::Filter(_) => {
+                        self.build_rewrite(sort, &outer.expr, sort.input.as_ref())
+                    }
+                    // Trimmed shape: `SELECT id … ORDER BY l2_distance(vec, …)`
+                    // with the distance NOT in the SELECT list. DataFusion
+                    // materializes the raw vector column in an intermediate
+                    // projection purely to feed the Sort, then trims it with
+                    // this outer projection. Producibility must be judged on
+                    // the OUTER (real output) columns — the inner projection's
+                    // vector column never reaches the user. The Sort visit
+                    // would wrongly decline this shape (it sees the vector
+                    // among the inner projection's outputs and the node cannot
+                    // produce it). When the distance is instead aliased inside
+                    // the inner projection (`SELECT …, l2_distance(…) AS d …
+                    // ORDER BY d`), `find_distance_info` finds no distance in
+                    // the outer exprs, `build_rewrite` declines here, and the
+                    // Sort visit handles it exactly as before.
+                    LogicalPlan::Projection(inner)
+                        if matches!(
+                            inner.input.as_ref(),
+                            LogicalPlan::TableScan(_) | LogicalPlan::Filter(_)
+                        ) =>
+                    {
+                        self.build_rewrite(sort, &outer.expr, inner.input.as_ref())
+                    }
+                    _ => None,
                 }
-                self.build_rewrite(sort, &outer.expr, sort.input.as_ref())
             }
 
             _ => None,

tests/orderby_distance_trimmed.rs

@@ -0,0 +1,232 @@
+// tests/orderby_distance_trimmed.rs — Regression tests for the "trimmed"
+// k-NN shape: `ORDER BY distance_fn(vector, lit)` where the distance is NOT in
+// the SELECT list and the query has a WHERE clause.
+//
+// With a Filter present, DataFusion materializes the raw vector column in an
+// intermediate projection between the Sort and the Filter (it is needed to
+// evaluate the inline ORDER BY expression), then trims it with an outer
+// projection:
+//
+//   Projection: id                      ← real output (no vector)
+//     Sort: l2_distance(vector, lit)
+//       Projection: id, vector          ← vector materialized only for the Sort
+//         Filter: label = '…'
+//           TableScan
+//
+// The Sort-anchored match judges producibility on the INNER projection — which
+// contains the vector the node cannot produce — and would wrongly fall back.
+// The Projection-anchored match must recognize this shape and judge
+// producibility on the OUTER projection instead (issue: ORDER-BY-only distance
+// silently losing the index whenever a WHERE clause is present).
+//
+// Unlike tests/vector_col_projection.rs, the fixtures here use a ducklake-style
+// addressing key — `rowid: Int64` — rather than the parquet-style
+// `_key: UInt64`, so the key-column-agnostic path is covered too.
+
+use std::sync::Arc;
+
+use arrow_schema::{DataType, Field, Schema};
+use datafusion::logical_expr::LogicalPlan;
+use datafusion::prelude::SessionContext;
+use usearch::{Index, IndexOptions, MetricKind, ScalarKind};
+
+use datafusion_vector_search_ext::{HashKeyProvider, USearchNode, USearchRegistry, register_all};
+
+/// The user-visible table: addressing key absent, vector column present.
+fn table_schema() -> Arc<Schema> {
+    Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int32, false),
+        Field::new("label", DataType::Utf8, true),
+        Field::new(
+            "embedding",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
+            false,
+        ),
+    ]))
+}
+
+/// Sidecar/lookup schema: ducklake-style `rowid` key + non-vector columns.
+/// The vector column is excluded — exactly as the SQLite sidecar stores it.
+fn lookup_schema() -> Arc<Schema> {
+    Arc::new(Schema::new(vec![
+        Field::new("rowid", DataType::Int64, false),
+        Field::new("id", DataType::Int32, false),
+        Field::new("label", DataType::Utf8, true),
+    ]))
+}
+
+/// Scan-provider schema: full column set including the vector, with the
+/// `rowid` key — mirrors the snapshot-pinned DuckLake scan provider.
+fn scan_schema() -> Arc<Schema> {
+    Arc::new(Schema::new(vec![
+        Field::new("id", DataType::Int32, false),
+        Field::new("label", DataType::Utf8, true),
+        Field::new(
+            "embedding",
+            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
+            false,
+        ),
+        Field::new("rowid", DataType::Int64, false),
+    ]))
+}
+
+fn make_index() -> Arc<Index> {
+    let options = IndexOptions {
+        dimensions: 4,
+        metric: MetricKind::L2sq,
+        quantization: ScalarKind::F32,
+        ..Default::default()
+    };
+    Arc::new(Index::new(&options).expect("usearch Index::new failed"))
+}
+
+async fn make_ctx() -> SessionContext {
+    let scan_provider = Arc::new(
+        HashKeyProvider::try_new(scan_schema(), vec![], "rowid")
+            .expect("scan HashKeyProvider::try_new failed"),
+    );
+    let lookup_provider = Arc::new(
+        HashKeyProvider::try_new(lookup_schema(), vec![], "rowid")
+            .expect("lookup HashKeyProvider::try_new failed"),
+    );
+
+    let reg = USearchRegistry::new();
+    reg.add(
+        "items::embedding",
+        make_index(),
+        scan_provider,
+        lookup_provider,
+        "rowid",
+        MetricKind::L2sq,
+        ScalarKind::F32,
+    )
+    .expect("USearchRegistry::add failed");
+    let registry = reg.into_arc();
+
+    let ctx = SessionContext::default();
+    register_all(&ctx, registry).expect("register_all failed");
+
+    let table = Arc::new(
+        HashKeyProvider::try_new(table_schema(), vec![], "id")
+            .expect("table HashKeyProvider::try_new failed"),
+    );
+    ctx.register_table("items", table)
+        .expect("register_table failed");
+    ctx
+}
+
+fn contains_usearch_node(plan: &LogicalPlan) -> bool {
+    if let LogicalPlan::Extension(ext) = plan
+        && ext.node.as_any().downcast_ref::<USearchNode>().is_some()
+    {
+        return true;
+    }
+    plan.inputs().iter().any(|c| contains_usearch_node(c))
+}
+
+const Q: &str = "ARRAY[0.1, 0.2, 0.3, 0.4]";
+
+/// The shape this file exists for: distance only in ORDER BY, WHERE present.
+/// The vector appears in DataFusion's intermediate projection but not in the
+/// query output — the rule must use the index.
+#[tokio::test]
+async fn test_bare_orderby_with_where_rewrites() {
+    let ctx = make_ctx().await;
+    let sql = format!(
+        "SELECT id FROM items WHERE label = 'x' \
+         ORDER BY l2_distance(embedding, {Q}) ASC LIMIT 2"
+    );
+    let plan = ctx
+        .sql(&sql)
+        .await
+        .expect("SQL analysis failed")
+        .into_optimized_plan()
+        .expect("optimization must not error");
+    assert!(
+        contains_usearch_node(&plan),
+        "vector not in output → rule must use the index despite the WHERE-induced \
+         intermediate projection\nPlan: {plan:?}"
+    );
+}
+
+/// Multiple non-vector output columns, same shape.
+#[tokio::test]
+async fn test_bare_orderby_with_where_multiple_columns_rewrites() {
+    let ctx = make_ctx().await;
+    let sql = format!(
+        "SELECT id, label FROM items WHERE label LIKE 'x%' \
+         ORDER BY l2_distance(embedding, {Q}) ASC LIMIT 5"
+    );
+    let plan = ctx
+        .sql(&sql)
+        .await
+        .expect("SQL analysis failed")
+        .into_optimized_plan()
+        .expect("optimization must not error");
+    assert!(
+        contains_usearch_node(&plan),
+        "all output columns producible → rule must use the index\nPlan: {plan:?}"
+    );
+}
+
+/// `SELECT *` with WHERE: output includes the vector → must still fall back.
+#[tokio::test]
+async fn test_select_star_with_where_still_falls_back() {
+    let ctx = make_ctx().await;
+    let sql = format!(
+        "SELECT * FROM items WHERE label = 'x' \
+         ORDER BY l2_distance(embedding, {Q}) ASC LIMIT 2"
+    );
+    let plan = ctx
+        .sql(&sql)
+        .await
+        .expect("SQL analysis failed")
+        .into_optimized_plan()
+        .expect("optimization must not error when the vector column is in the output");
+    assert!(
+        !contains_usearch_node(&plan),
+        "vector column in output → rule must fall back, WHERE or not\nPlan: {plan:?}"
+    );
+}
+
+/// Explicit vector column in the output with WHERE: must still fall back.
+#[tokio::test]
+async fn test_select_vector_with_where_still_falls_back() {
+    let ctx = make_ctx().await;
+    let sql = format!(
+        "SELECT id, embedding FROM items WHERE label = 'x' \
+         ORDER BY l2_distance(embedding, {Q}) ASC LIMIT 2"
+    );
+    let plan = ctx
+        .sql(&sql)
+        .await
+        .expect("SQL analysis failed")
+        .into_optimized_plan()
+        .expect("optimization must not error when the vector column is in the output");
+    assert!(
+        !contains_usearch_node(&plan),
+        "vector column in output → rule must fall back, WHERE or not\nPlan: {plan:?}"
+    );
+}
+
+/// The canonical aliased-distance shape with WHERE keeps rewriting via the
+/// Sort-anchored match (regression guard: the new Projection-anchored arm must
+/// decline it cleanly and leave it to the Sort visit).
+#[tokio::test]
+async fn test_aliased_distance_with_where_still_rewrites() {
+    let ctx = make_ctx().await;
+    let sql = format!(
+        "SELECT id, l2_distance(embedding, {Q}) AS dist FROM items \
+         WHERE label = 'x' ORDER BY dist ASC LIMIT 2"
+    );
+    let plan = ctx
+        .sql(&sql)
+        .await
+        .expect("SQL analysis failed")
+        .into_optimized_plan()
+        .expect("optimization failed");
+    assert!(
+        contains_usearch_node(&plan),
+        "aliased-distance shape must keep rewriting\nPlan: {plan:?}"
+    );
+}