From c08f3a4472b9b5bd728f5b73f7a03b14da4ebda7 Mon Sep 17 00:00:00 2001
From: dennis zhuang <killme2008@gmail.com>
Date: Wed, 4 Feb 2026 11:54:47 +0800
Subject: [PATCH] test: adds sqlness test for vector index (#7634)

* test: adds sqlness test for vector index

Signed-off-by: Dennis Zhuang <killme2008@gmail.com>

* fix: CI

Signed-off-by: Dennis Zhuang <killme2008@gmail.com>

* test: redacted flat map and size

Signed-off-by: Dennis Zhuang <killme2008@gmail.com>

* test: simplify the replace rules

Signed-off-by: Dennis Zhuang <killme2008@gmail.com>

* chore: update comments and tests

Signed-off-by: Dennis Zhuang <killme2008@gmail.com>

---------

Signed-off-by: Dennis Zhuang <killme2008@gmail.com>
---
 .github/workflows/develop.yml                 |   2 +-
 src/mito2/src/read/scan_region.rs             |   4 +
 src/query/src/optimizer/scan_hint.rs          |  55 +-
 .../src/optimizer/scan_hint/vector_search.rs  |  86 ++-
 .../function/vector/vector_index.result       | 602 ++++++++++++++++++
 .../common/function/vector/vector_index.sql   | 341 ++++++++++
 .../vector/vector_index_explain.result        | 270 ++++++++
 .../function/vector/vector_index_explain.sql  | 155 +++++
 .../function/vector/vector_index_join.result  | 240 +++++++
 .../function/vector/vector_index_join.sql     | 159 +++++
 tests/runner/src/env/bare.rs                  |   2 +-
 11 files changed, 1901 insertions(+), 15 deletions(-)
 create mode 100644 tests/cases/standalone/common/function/vector/vector_index.result
 create mode 100644 tests/cases/standalone/common/function/vector/vector_index.sql
 create mode 100644 tests/cases/standalone/common/function/vector/vector_index_explain.result
 create mode 100644 tests/cases/standalone/common/function/vector/vector_index_explain.sql
 create mode 100644 tests/cases/standalone/common/function/vector/vector_index_join.result
 create mode 100644 tests/cases/standalone/common/function/vector/vector_index_join.sql

diff --git a/.github/workflows/develop.yml b/.github/workflows/develop.yml
index 1b35761318..8b39acd99b 100644
--- a/.github/workflows/develop.yml
+++ b/.github/workflows/develop.yml
@@ -119,7 +119,7 @@ jobs:
       - name: Build greptime binaries
         shell: bash
         # `cargo gc` will invoke `cargo build` with specified args
-        run: cargo gc -- --bin greptime --bin sqlness-runner --features "pg_kvbackend,mysql_kvbackend"
+        run: cargo gc -- --bin greptime --bin sqlness-runner --features "pg_kvbackend,mysql_kvbackend,vector_index"
       - name: Pack greptime binaries
         shell: bash
         run: |
diff --git a/src/mito2/src/read/scan_region.rs b/src/mito2/src/read/scan_region.rs
index 86d73991b0..bc8bb987d7 100644
--- a/src/mito2/src/read/scan_region.rs
+++ b/src/mito2/src/read/scan_region.rs
@@ -1576,6 +1576,10 @@ impl StreamContext {
                     let exprs: Vec<_> = predicate.exprs().iter().map(|e| e.to_string()).collect();
                     write!(f, ", \"filters\": {:?}", exprs)?;
                 }
+                #[cfg(feature = "vector_index")]
+                if let Some(vector_index_k) = self.input.vector_index_k {
+                    write!(f, ", \"vector_index_k\": {}", vector_index_k)?;
+                }
                 if !self.input.files.is_empty() {
                     write!(f, ", \"files\": ")?;
                     f.debug_list()
diff --git a/src/query/src/optimizer/scan_hint.rs b/src/query/src/optimizer/scan_hint.rs
index c06c6b7812..da70813404 100644
--- a/src/query/src/optimizer/scan_hint.rs
+++ b/src/query/src/optimizer/scan_hint.rs
@@ -323,14 +323,18 @@ impl TreeNodeVisitor<'_> for ScanHintVisitor {
         }
 
         // Avoid carrying vector hints across branching inputs (join/subquery) to prevent
-        // pruning results before global ordering is applied.
-        let is_branching = matches!(node, LogicalPlan::Subquery(_)) || node.inputs().len() > 1;
-        if is_branching && self.ts_row_selector.is_some() {
+        // pruning results before global ordering is applied. Only treat a subquery as a
+        // barrier when it contains non-inlineable operators.
+        let is_branching_for_ts = matches!(
+            node,
+            LogicalPlan::Subquery(_) | LogicalPlan::SubqueryAlias(_)
+        ) || node.inputs().len() > 1;
+        if is_branching_for_ts && self.ts_row_selector.is_some() {
             // clean previous time series selector hint when encounter subqueries or join
             self.ts_row_selector = None;
         }
         #[cfg(feature = "vector_index")]
-        if is_branching {
+        if is_branching_for_vector(node) {
             self.vector_search.on_branching_enter();
         }
 
@@ -371,8 +375,10 @@ impl TreeNodeVisitor<'_> for ScanHintVisitor {
             LogicalPlan::Filter(_) => {
                 self.vector_search.on_filter_exit();
             }
-            LogicalPlan::Subquery(_) => {
-                self.vector_search.on_branching_exit();
+            LogicalPlan::Subquery(_) | LogicalPlan::SubqueryAlias(_) => {
+                if is_branching_for_vector(_node) {
+                    self.vector_search.on_branching_exit();
+                }
             }
             _ if _node.inputs().len() > 1 => {
                 self.vector_search.on_branching_exit();
@@ -398,6 +404,43 @@ impl ScanHintVisitor {
     }
 }
 
+#[cfg(feature = "vector_index")]
+fn is_branching_for_vector(node: &LogicalPlan) -> bool {
+    if node.inputs().len() > 1 {
+        return true;
+    }
+
+    match node {
+        LogicalPlan::Subquery(subquery) => has_non_inlineable_ops(subquery.subquery.as_ref()),
+        LogicalPlan::SubqueryAlias(alias) => has_non_inlineable_ops(alias.input.as_ref()),
+        _ => false,
+    }
+}
+
+#[cfg(feature = "vector_index")]
+fn has_non_inlineable_ops(plan: &LogicalPlan) -> bool {
+    if matches!(
+        plan,
+        LogicalPlan::Limit(_)
+            | LogicalPlan::Sort(_)
+            | LogicalPlan::Distinct(_)
+            | LogicalPlan::Aggregate(_)
+            | LogicalPlan::Window(_)
+            | LogicalPlan::Union(_)
+            | LogicalPlan::Join(_)
+    ) {
+        return true;
+    }
+
+    for input in plan.inputs() {
+        if has_non_inlineable_ops(input) {
+            return true;
+        }
+    }
+
+    false
+}
+
 #[cfg(test)]
 mod test {
     use std::sync::Arc;
diff --git a/src/query/src/optimizer/scan_hint/vector_search.rs b/src/query/src/optimizer/scan_hint/vector_search.rs
index 44f76066a1..7f97ad5ac1 100644
--- a/src/query/src/optimizer/scan_hint/vector_search.rs
+++ b/src/query/src/optimizer/scan_hint/vector_search.rs
@@ -35,6 +35,16 @@ use crate::dummy_catalog::DummyTableProvider;
 /// - A LIMIT (or Sort.fetch) is present to derive k.
 /// - The hint stays within a single input chain (not across join/subquery branches).
 /// - The target column is non-nullable, or an explicit IS NOT NULL filter exists.
+///
+/// Known limitations:
+/// - Dynamic overfetching is not implemented yet. When filters exist or ORDER BY includes
+///   additional tie-breaker columns (e.g., ORDER BY distance, id), the current fixed k may
+///   return incorrect results. A future improvement should dynamically adjust k based on
+///   filter selectivity and secondary sort requirements.
+/// - Hints only block at subquery boundaries when the subquery contains non-inlineable
+///   operators (Limit/Sort/Distinct/Aggregate/Window). Simple subqueries without these
+///   operators allow hints to propagate through. In distributed mode, the dist analyzer
+///   may inline subqueries before this rule runs, further reducing isolation.
 #[derive(Default)]
 pub(crate) struct VectorSearchState {
     current_distance: Option<VectorDistanceInfo>,
@@ -241,23 +251,47 @@ impl VectorSearchState {
     fn extract_distance_from_sort(
         sort: &datafusion_expr::logical_plan::Sort,
     ) -> Option<VectorDistanceInfo> {
-        if sort.expr.len() != 1 {
-            debug!(
-                "Skip vector hint: Sort has {} expressions, expected 1",
-                sort.expr.len()
-            );
+        if sort.expr.is_empty() {
+            debug!("Skip vector hint: Sort has no expressions");
             return None;
         }
         let sort_expr: &SortExpr = &sort.expr[0];
         let info = Self::extract_distance_info(&sort_expr.expr)?;
         let expected_asc = info.metric != VectorDistanceMetric::InnerProduct;
-        if sort_expr.asc == expected_asc {
+        if sort_expr.asc != expected_asc {
+            return None;
+        }
+
+        if Self::tie_breakers_allowed(&sort.expr[1..], &info) {
             Some(info)
         } else {
+            if sort.expr.len() > 1 {
+                debug!(
+                    "Skip vector hint: Sort has unsupported tie-breakers ({} expressions)",
+                    sort.expr.len()
+                );
+            }
             None
         }
     }
 
+    fn tie_breakers_allowed(sort_exprs: &[SortExpr], distance_info: &VectorDistanceInfo) -> bool {
+        if sort_exprs.is_empty() {
+            return true;
+        }
+
+        sort_exprs.iter().all(|sort_expr| {
+            let Expr::Column(col) = &sort_expr.expr else {
+                return false;
+            };
+
+            match &distance_info.table_reference {
+                Some(table) => col.relation.as_ref() == Some(table),
+                None => col.relation.is_none(),
+            }
+        })
+    }
+
     fn extract_limit_info(limit: &datafusion_expr::logical_plan::Limit) -> Option<VectorLimitInfo> {
         let fetch = match limit.get_fetch_type().ok()? {
             FetchType::Literal(fetch) => fetch?,
@@ -767,8 +801,10 @@ mod tests {
         assert!(t2_provider.get_vector_search_hint().is_none());
     }
 
+    // Simple subqueries (without non-inlineable ops like Limit/Sort/Distinct/Aggregate/Window)
+    // allow hints to propagate through. See known limitations in VectorSearchState docs.
     #[test]
-    fn test_no_vector_hint_above_subquery() {
+    fn test_simple_subquery_allows_hint_propagation() {
         let provider = build_dummy_provider(10);
         let table_source = Arc::new(DefaultTableSource::new(provider.clone()));
         let scan_plan = LogicalPlanBuilder::scan_with_filters("t", table_source, None, vec![])
@@ -794,6 +830,42 @@ mod tests {
         let context = OptimizerContext::default();
         let _ = ScanHintRule.rewrite(plan, &context).unwrap();
 
+        // Hint propagates through simple subquery
+        let hint = provider.get_vector_search_hint().unwrap();
+        assert_eq!(hint.k, 5);
+    }
+
+    // Subqueries with non-inlineable ops (Limit/Sort/Distinct/Aggregate/Window) block hint propagation.
+    #[test]
+    fn test_subquery_with_limit_blocks_hint() {
+        let provider = build_dummy_provider(10);
+        let table_source = Arc::new(DefaultTableSource::new(provider.clone()));
+        let scan_plan = LogicalPlanBuilder::scan_with_filters("t", table_source, None, vec![])
+            .unwrap()
+            .limit(0, Some(100)) // non-inlineable op inside subquery
+            .unwrap()
+            .build()
+            .unwrap();
+
+        let subquery = LogicalPlan::Subquery(Subquery {
+            subquery: Arc::new(scan_plan),
+            outer_ref_columns: vec![],
+            spans: Default::default(),
+        });
+
+        let expr = vec_distance_expr(VEC_L2SQ_DISTANCE);
+        let plan = LogicalPlanBuilder::from(subquery)
+            .sort(vec![expr.sort(true, false)])
+            .unwrap()
+            .limit(0, Some(5))
+            .unwrap()
+            .build()
+            .unwrap();
+
+        let context = OptimizerContext::default();
+        let _ = ScanHintRule.rewrite(plan, &context).unwrap();
+
+        // Hint does NOT propagate through subquery with non-inlineable ops
         assert!(provider.get_vector_search_hint().is_none());
     }
 
diff --git a/tests/cases/standalone/common/function/vector/vector_index.result b/tests/cases/standalone/common/function/vector/vector_index.result
new file mode 100644
index 0000000000..ace2205f23
--- /dev/null
+++ b/tests/cases/standalone/common/function/vector/vector_index.result
@@ -0,0 +1,602 @@
+-- Test vector index creation and KNN search
+-- ============================================
+-- Part 1: Basic L2 squared distance tests
+-- ============================================
+-- Create a table with vector column and L2sq vector index
+CREATE TABLE vectors_l2sq (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+-- Insert test vectors
+INSERT INTO vectors_l2sq VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.0, 1.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 0.0, 1.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.0, 0.0, 1.0]'),
+    (5, '2024-01-01 00:00:04', '[1.0, 1.0, 0.0, 0.0]'),
+    (6, '2024-01-01 00:00:05', '[0.0, 1.0, 1.0, 0.0]'),
+    (7, '2024-01-01 00:00:06', '[0.0, 0.0, 1.0, 1.0]'),
+    (8, '2024-01-01 00:00:07', '[1.0, 0.0, 0.0, 1.0]');
+
+Affected Rows: 8
+
+-- Query BEFORE flush (memtable search)
+-- Expected: vec_id=1 (distance=0), vec_id=5 (distance=1), vec_id=8 (distance=1)
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_l2sq
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
++--------+----------+
+| vec_id | distance |
++--------+----------+
+| 1      | 0.0      |
+| 5      | 1.0      |
+| 8      | 1.0      |
++--------+----------+
+
+-- Flush to create SST files with vector index
+ADMIN FLUSH_TABLE('vectors_l2sq');
+
++-----------------------------------+
+| ADMIN FLUSH_TABLE('vectors_l2sq') |
++-----------------------------------+
+| 0                                 |
++-----------------------------------+
+
+-- Query AFTER flush (SST index search)
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_l2sq
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
++--------+----------+
+| vec_id | distance |
++--------+----------+
+| 1      | 0.0      |
+| 5      | 1.0      |
+| 8      | 1.0      |
++--------+----------+
+
+-- Query with different target vector
+-- Expected: vec_id=6 (distance=0), vec_id=2 (distance=1), vec_id=3 (distance=1)
+SELECT vec_id, vec_l2sq_distance(embedding, '[0.0, 1.0, 1.0, 0.0]') as distance
+FROM vectors_l2sq
+ORDER BY vec_l2sq_distance(embedding, '[0.0, 1.0, 1.0, 0.0]'), vec_id
+LIMIT 3;
+
++--------+----------+
+| vec_id | distance |
++--------+----------+
+| 6      | 0.0      |
+| 2      | 1.0      |
+| 3      | 1.0      |
++--------+----------+
+
+DROP TABLE vectors_l2sq;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 2: Cosine distance tests
+-- ============================================
+CREATE TABLE vectors_cosine (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'cosine'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+-- Insert vectors with different magnitudes but same/different directions
+INSERT INTO vectors_cosine VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[2.0, 0.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0, 0.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', '[1.0, 1.0, 0.0, 0.0]'),
+    (5, '2024-01-01 00:00:04', '[-1.0, 0.0, 0.0, 0.0]');
+
+Affected Rows: 5
+
+-- Memtable search with cosine distance
+-- vec_id=1 and vec_id=2 should have same cosine distance (0) since they point same direction
+SELECT vec_id, vec_cos_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_cosine
+ORDER BY vec_cos_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
++--------+------------+
+| vec_id | distance   |
++--------+------------+
+| 1      | 0.0        |
+| 2      | 0.0        |
+| 4      | 0.29289323 |
++--------+------------+
+
+ADMIN FLUSH_TABLE('vectors_cosine');
+
++-------------------------------------+
+| ADMIN FLUSH_TABLE('vectors_cosine') |
++-------------------------------------+
+| 0                                   |
++-------------------------------------+
+
+-- SST index search with cosine distance
+SELECT vec_id, vec_cos_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_cosine
+ORDER BY vec_cos_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
++--------+------------+
+| vec_id | distance   |
++--------+------------+
+| 1      | 0.0        |
+| 2      | 0.0        |
+| 4      | 0.29289323 |
++--------+------------+
+
+DROP TABLE vectors_cosine;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 3: Dot product (inner product) tests
+-- ============================================
+CREATE TABLE vectors_dot (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'dot'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_dot VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[2.0, 0.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0, 0.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', '[1.0, 1.0, 0.0, 0.0]'),
+    (5, '2024-01-01 00:00:04', '[-1.0, 0.0, 0.0, 0.0]');
+
+Affected Rows: 5
+
+-- Memtable search with dot product
+-- Larger dot product means more similar, so we use negative for ordering
+-- vec_id=2 should be best (dot=2), vec_id=1 and vec_id=4 have dot=1
+SELECT vec_id, vec_dot_product(embedding, '[1.0, 0.0, 0.0, 0.0]') as dot_product
+FROM vectors_dot
+ORDER BY vec_dot_product(embedding, '[1.0, 0.0, 0.0, 0.0]') DESC, vec_id
+LIMIT 3;
+
++--------+-------------+
+| vec_id | dot_product |
++--------+-------------+
+| 2      | 2.0         |
+| 1      | 1.0         |
+| 4      | 1.0         |
++--------+-------------+
+
+ADMIN FLUSH_TABLE('vectors_dot');
+
++----------------------------------+
+| ADMIN FLUSH_TABLE('vectors_dot') |
++----------------------------------+
+| 0                                |
++----------------------------------+
+
+-- SST index search with dot product
+SELECT vec_id, vec_dot_product(embedding, '[1.0, 0.0, 0.0, 0.0]') as dot_product
+FROM vectors_dot
+ORDER BY vec_dot_product(embedding, '[1.0, 0.0, 0.0, 0.0]') DESC, vec_id
+LIMIT 3;
+
++--------+-------------+
+| vec_id | dot_product |
++--------+-------------+
+| 2      | 2.0         |
+| 1      | 1.0         |
+| 4      | 1.0         |
++--------+-------------+
+
+DROP TABLE vectors_dot;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 4: NULL vector handling tests
+-- ============================================
+CREATE TABLE vectors_null (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+-- Insert vectors with some NULLs
+INSERT INTO vectors_null VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', NULL),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0, 0.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', NULL),
+    (5, '2024-01-01 00:00:04', '[0.0, 0.0, 1.0, 0.0]');
+
+Affected Rows: 5
+
+-- Memtable search should skip NULL vectors
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_null
+WHERE embedding IS NOT NULL
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
++--------+----------+
+| vec_id | distance |
++--------+----------+
+| 1      | 0.0      |
+| 3      | 2.0      |
+| 5      | 2.0      |
++--------+----------+
+
+ADMIN FLUSH_TABLE('vectors_null');
+
++-----------------------------------+
+| ADMIN FLUSH_TABLE('vectors_null') |
++-----------------------------------+
+| 0                                 |
++-----------------------------------+
+
+-- SST index search should also skip NULL vectors
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_null
+WHERE embedding IS NOT NULL
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
++--------+----------+
+| vec_id | distance |
++--------+----------+
+| 1      | 0.0      |
+| 3      | 2.0      |
+| 5      | 2.0      |
++--------+----------+
+
+DROP TABLE vectors_null;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 5: Mixed memtable + SST search tests
+-- ============================================
+CREATE TABLE vectors_mixed (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+-- Insert first batch and flush to SST
+INSERT INTO vectors_mixed VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.0, 1.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 0.0, 1.0, 0.0]');
+
+Affected Rows: 3
+
+ADMIN FLUSH_TABLE('vectors_mixed');
+
++------------------------------------+
+| ADMIN FLUSH_TABLE('vectors_mixed') |
++------------------------------------+
+| 0                                  |
++------------------------------------+
+
+-- Insert second batch (stays in memtable)
+INSERT INTO vectors_mixed VALUES
+    (4, '2024-01-01 00:00:03', '[0.5, 0.5, 0.0, 0.0]'),
+    (5, '2024-01-01 00:00:04', '[0.9, 0.1, 0.0, 0.0]');
+
+Affected Rows: 2
+
+-- Query should search both SST (vec_id 1,2,3) and memtable (vec_id 4,5)
+-- Target: [1.0, 0.0, 0.0, 0.0]
+-- Expected: vec_id=1 (dist=0), vec_id=5 (dist=0.02), vec_id=4 (dist=0.5)
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_mixed
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
++--------+-------------+
+| vec_id | distance    |
++--------+-------------+
+| 1      | 0.0         |
+| 5      | 0.020000005 |
+| 4      | 0.5         |
++--------+-------------+
+
+DROP TABLE vectors_mixed;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 6: KNN with WHERE clause tests
+-- ============================================
+CREATE TABLE vectors_filter (
+    vec_id INT,
+    category STRING,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_filter VALUES
+    (1, 'A', '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, 'B', '2024-01-01 00:00:01', '[0.9, 0.1, 0.0, 0.0]'),
+    (3, 'A', '2024-01-01 00:00:02', '[0.0, 1.0, 0.0, 0.0]'),
+    (4, 'B', '2024-01-01 00:00:03', '[0.1, 0.9, 0.0, 0.0]'),
+    (5, 'A', '2024-01-01 00:00:04', '[0.5, 0.5, 0.0, 0.0]');
+
+Affected Rows: 5
+
+-- Memtable search with filter
+SELECT vec_id, category, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_filter
+WHERE category = 'A'
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
++--------+----------+----------+
+| vec_id | category | distance |
++--------+----------+----------+
+| 1      | A        | 0.0      |
+| 5      | A        | 0.5      |
++--------+----------+----------+
+
+ADMIN FLUSH_TABLE('vectors_filter');
+
++-------------------------------------+
+| ADMIN FLUSH_TABLE('vectors_filter') |
++-------------------------------------+
+| 0                                   |
++-------------------------------------+
+
+-- SST index search with filter
+SELECT vec_id, category, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_filter
+WHERE category = 'A'
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
++--------+----------+----------+
+| vec_id | category | distance |
++--------+----------+----------+
+| 1      | A        | 0.0      |
+| 5      | A        | 0.5      |
++--------+----------+----------+
+
+-- Filter with time range
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_filter
+WHERE ts >= '2024-01-01 00:00:02' AND ts <= '2024-01-01 00:00:04'
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
++--------+-----------+
+| vec_id | distance  |
++--------+-----------+
+| 5      | 0.5       |
+| 4      | 1.6199999 |
++--------+-----------+
+
+DROP TABLE vectors_filter;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 7: Higher dimension vectors
+-- ============================================
+CREATE TABLE vectors_high_dim (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(128) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+-- Insert high-dimensional vectors (simplified: first few elements differ)
+INSERT INTO vectors_high_dim VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]');
+
+Affected Rows: 3
+
+-- Memtable search
+SELECT vec_id FROM vectors_high_dim
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
++--------+
+| vec_id |
++--------+
+| 1      |
+| 3      |
++--------+
+
+ADMIN FLUSH_TABLE('vectors_high_dim');
+
++---------------------------------------+
+| ADMIN FLUSH_TABLE('vectors_high_dim') |
++---------------------------------------+
+| 0                                     |
++---------------------------------------+
+
+-- SST index search
+SELECT vec_id FROM vectors_high_dim
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
++--------+
+| vec_id |
++--------+
+| 1      |
+| 3      |
++--------+
+
+DROP TABLE vectors_high_dim;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 8: Different k values (LIMIT)
+-- ============================================
+CREATE TABLE vectors_k (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_k VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.1, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.8, 0.2, 0.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', '[0.7, 0.3, 0.0, 0.0]'),
+    (5, '2024-01-01 00:00:04', '[0.6, 0.4, 0.0, 0.0]'),
+    (6, '2024-01-01 00:00:05', '[0.5, 0.5, 0.0, 0.0]'),
+    (7, '2024-01-01 00:00:06', '[0.4, 0.6, 0.0, 0.0]'),
+    (8, '2024-01-01 00:00:07', '[0.3, 0.7, 0.0, 0.0]'),
+    (9, '2024-01-01 00:00:08', '[0.2, 0.8, 0.0, 0.0]'),
+    (10, '2024-01-01 00:00:09', '[0.1, 0.9, 0.0, 0.0]');
+
+Affected Rows: 10
+
+ADMIN FLUSH_TABLE('vectors_k');
+
++--------------------------------+
+| ADMIN FLUSH_TABLE('vectors_k') |
++--------------------------------+
+| 0                              |
++--------------------------------+
+
+-- k=1
+SELECT vec_id FROM vectors_k
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 1;
+
++--------+
+| vec_id |
++--------+
+| 1      |
++--------+
+
+-- k=5
+SELECT vec_id FROM vectors_k
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 5;
+
++--------+
+| vec_id |
++--------+
+| 1      |
+| 2      |
+| 3      |
+| 4      |
+| 5      |
++--------+
+
+-- k=10 (all vectors)
+SELECT vec_id FROM vectors_k
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 10;
+
++--------+
+| vec_id |
++--------+
+| 1      |
+| 2      |
+| 3      |
+| 4      |
+| 5      |
+| 6      |
+| 7      |
+| 8      |
+| 9      |
+| 10     |
++--------+
+
+DROP TABLE vectors_k;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 9: Engine parameter tests
+-- ============================================
+-- Create table with explicit engine parameter (usearch is default)
+CREATE TABLE vectors_engine (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (engine = 'usearch', metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+-- Insert test vectors
+INSERT INTO vectors_engine VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.0, 1.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.5, 0.5, 0.0, 0.0]');
+
+Affected Rows: 3
+
+-- Memtable search
+SELECT vec_id FROM vectors_engine
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
++--------+
+| vec_id |
++--------+
+| 1      |
+| 3      |
++--------+
+
+ADMIN FLUSH_TABLE('vectors_engine');
+
++-------------------------------------+
+| ADMIN FLUSH_TABLE('vectors_engine') |
++-------------------------------------+
+| 0                                   |
++-------------------------------------+
+
+-- SST index search with usearch engine
+SELECT vec_id FROM vectors_engine
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
++--------+
+| vec_id |
++--------+
+| 1      |
+| 3      |
++--------+
+
+DROP TABLE vectors_engine;
+
+Affected Rows: 0
+
diff --git a/tests/cases/standalone/common/function/vector/vector_index.sql b/tests/cases/standalone/common/function/vector/vector_index.sql
new file mode 100644
index 0000000000..ecfcd586b4
--- /dev/null
+++ b/tests/cases/standalone/common/function/vector/vector_index.sql
@@ -0,0 +1,341 @@
+-- Test vector index creation and KNN search
+
+-- ============================================
+-- Part 1: Basic L2 squared distance tests
+-- ============================================
+
+-- Create a table with vector column and L2sq vector index
+CREATE TABLE vectors_l2sq (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+-- Insert test vectors
+INSERT INTO vectors_l2sq VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.0, 1.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 0.0, 1.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.0, 0.0, 1.0]'),
+    (5, '2024-01-01 00:00:04', '[1.0, 1.0, 0.0, 0.0]'),
+    (6, '2024-01-01 00:00:05', '[0.0, 1.0, 1.0, 0.0]'),
+    (7, '2024-01-01 00:00:06', '[0.0, 0.0, 1.0, 1.0]'),
+    (8, '2024-01-01 00:00:07', '[1.0, 0.0, 0.0, 1.0]');
+
+-- Query BEFORE flush (memtable search)
+-- Expected: vec_id=1 (distance=0), vec_id=5 (distance=1), vec_id=8 (distance=1)
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_l2sq
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
+-- Flush to create SST files with vector index
+ADMIN FLUSH_TABLE('vectors_l2sq');
+
+-- Query AFTER flush (SST index search)
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_l2sq
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
+-- Query with different target vector
+-- Expected: vec_id=6 (distance=0), vec_id=2 (distance=1), vec_id=3 (distance=1)
+SELECT vec_id, vec_l2sq_distance(embedding, '[0.0, 1.0, 1.0, 0.0]') as distance
+FROM vectors_l2sq
+ORDER BY vec_l2sq_distance(embedding, '[0.0, 1.0, 1.0, 0.0]'), vec_id
+LIMIT 3;
+
+DROP TABLE vectors_l2sq;
+
+-- ============================================
+-- Part 2: Cosine distance tests
+-- ============================================
+
+CREATE TABLE vectors_cosine (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'cosine'),
+    PRIMARY KEY (vec_id)
+);
+
+-- Insert vectors with different magnitudes but same/different directions
+INSERT INTO vectors_cosine VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[2.0, 0.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0, 0.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', '[1.0, 1.0, 0.0, 0.0]'),
+    (5, '2024-01-01 00:00:04', '[-1.0, 0.0, 0.0, 0.0]');
+
+-- Memtable search with cosine distance
+-- vec_id=1 and vec_id=2 should have same cosine distance (0) since they point same direction
+SELECT vec_id, vec_cos_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_cosine
+ORDER BY vec_cos_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
+ADMIN FLUSH_TABLE('vectors_cosine');
+
+-- SST index search with cosine distance
+SELECT vec_id, vec_cos_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_cosine
+ORDER BY vec_cos_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
+DROP TABLE vectors_cosine;
+
+-- ============================================
+-- Part 3: Dot product (inner product) tests
+-- ============================================
+
+CREATE TABLE vectors_dot (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'dot'),
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_dot VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[2.0, 0.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0, 0.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', '[1.0, 1.0, 0.0, 0.0]'),
+    (5, '2024-01-01 00:00:04', '[-1.0, 0.0, 0.0, 0.0]');
+
+-- Memtable search with dot product
+-- Larger dot product means more similar, so we use negative for ordering
+-- vec_id=2 should be best (dot=2), vec_id=1 and vec_id=4 have dot=1
+SELECT vec_id, vec_dot_product(embedding, '[1.0, 0.0, 0.0, 0.0]') as dot_product
+FROM vectors_dot
+ORDER BY vec_dot_product(embedding, '[1.0, 0.0, 0.0, 0.0]') DESC, vec_id
+LIMIT 3;
+
+ADMIN FLUSH_TABLE('vectors_dot');
+
+-- SST index search with dot product
+SELECT vec_id, vec_dot_product(embedding, '[1.0, 0.0, 0.0, 0.0]') as dot_product
+FROM vectors_dot
+ORDER BY vec_dot_product(embedding, '[1.0, 0.0, 0.0, 0.0]') DESC, vec_id
+LIMIT 3;
+
+DROP TABLE vectors_dot;
+
+-- ============================================
+-- Part 4: NULL vector handling tests
+-- ============================================
+
+CREATE TABLE vectors_null (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+-- Insert vectors with some NULLs
+INSERT INTO vectors_null VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', NULL),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0, 0.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', NULL),
+    (5, '2024-01-01 00:00:04', '[0.0, 0.0, 1.0, 0.0]');
+
+-- Memtable search should skip NULL vectors
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_null
+WHERE embedding IS NOT NULL
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
+ADMIN FLUSH_TABLE('vectors_null');
+
+-- SST index search should also skip NULL vectors
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_null
+WHERE embedding IS NOT NULL
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
+DROP TABLE vectors_null;
+
+-- ============================================
+-- Part 5: Mixed memtable + SST search tests
+-- ============================================
+
+CREATE TABLE vectors_mixed (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+-- Insert first batch and flush to SST
+INSERT INTO vectors_mixed VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.0, 1.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 0.0, 1.0, 0.0]');
+
+ADMIN FLUSH_TABLE('vectors_mixed');
+
+-- Insert second batch (stays in memtable)
+INSERT INTO vectors_mixed VALUES
+    (4, '2024-01-01 00:00:03', '[0.5, 0.5, 0.0, 0.0]'),
+    (5, '2024-01-01 00:00:04', '[0.9, 0.1, 0.0, 0.0]');
+
+-- Query should search both SST (vec_id 1,2,3) and memtable (vec_id 4,5)
+-- Target: [1.0, 0.0, 0.0, 0.0]
+-- Expected: vec_id=1 (dist=0), vec_id=5 (dist=0.02), vec_id=4 (dist=0.5)
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_mixed
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 3;
+
+DROP TABLE vectors_mixed;
+
+-- ============================================
+-- Part 6: KNN with WHERE clause tests
+-- ============================================
+
+CREATE TABLE vectors_filter (
+    vec_id INT,
+    category STRING,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_filter VALUES
+    (1, 'A', '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, 'B', '2024-01-01 00:00:01', '[0.9, 0.1, 0.0, 0.0]'),
+    (3, 'A', '2024-01-01 00:00:02', '[0.0, 1.0, 0.0, 0.0]'),
+    (4, 'B', '2024-01-01 00:00:03', '[0.1, 0.9, 0.0, 0.0]'),
+    (5, 'A', '2024-01-01 00:00:04', '[0.5, 0.5, 0.0, 0.0]');
+
+-- Memtable search with filter
+SELECT vec_id, category, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_filter
+WHERE category = 'A'
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
+ADMIN FLUSH_TABLE('vectors_filter');
+
+-- SST index search with filter
+SELECT vec_id, category, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_filter
+WHERE category = 'A'
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
+-- Filter with time range
+SELECT vec_id, vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]') as distance
+FROM vectors_filter
+WHERE ts >= '2024-01-01 00:00:02' AND ts <= '2024-01-01 00:00:04'
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
+DROP TABLE vectors_filter;
+
+-- ============================================
+-- Part 7: Higher dimension vectors
+-- ============================================
+
+CREATE TABLE vectors_high_dim (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(128) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+-- Insert high-dimensional vectors (simplified: first few elements differ)
+INSERT INTO vectors_high_dim VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]');
+
+-- Memtable search
+SELECT vec_id FROM vectors_high_dim
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
+ADMIN FLUSH_TABLE('vectors_high_dim');
+
+-- SST index search
+SELECT vec_id FROM vectors_high_dim
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
+DROP TABLE vectors_high_dim;
+
+-- ============================================
+-- Part 8: Different k values (LIMIT)
+-- ============================================
+
+CREATE TABLE vectors_k (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_k VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.1, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.8, 0.2, 0.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', '[0.7, 0.3, 0.0, 0.0]'),
+    (5, '2024-01-01 00:00:04', '[0.6, 0.4, 0.0, 0.0]'),
+    (6, '2024-01-01 00:00:05', '[0.5, 0.5, 0.0, 0.0]'),
+    (7, '2024-01-01 00:00:06', '[0.4, 0.6, 0.0, 0.0]'),
+    (8, '2024-01-01 00:00:07', '[0.3, 0.7, 0.0, 0.0]'),
+    (9, '2024-01-01 00:00:08', '[0.2, 0.8, 0.0, 0.0]'),
+    (10, '2024-01-01 00:00:09', '[0.1, 0.9, 0.0, 0.0]');
+
+ADMIN FLUSH_TABLE('vectors_k');
+
+-- k=1
+SELECT vec_id FROM vectors_k
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 1;
+
+-- k=5
+SELECT vec_id FROM vectors_k
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 5;
+
+-- k=10 (all vectors)
+SELECT vec_id FROM vectors_k
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 10;
+
+DROP TABLE vectors_k;
+
+-- ============================================
+-- Part 9: Engine parameter tests
+-- ============================================
+
+-- Create table with explicit engine parameter (usearch is default)
+CREATE TABLE vectors_engine (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(4) NOT NULL VECTOR INDEX WITH (engine = 'usearch', metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+-- Insert test vectors
+INSERT INTO vectors_engine VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0, 0.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.0, 1.0, 0.0, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.5, 0.5, 0.0, 0.0]');
+
+-- Memtable search
+SELECT vec_id FROM vectors_engine
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
+ADMIN FLUSH_TABLE('vectors_engine');
+
+-- SST index search with usearch engine
+SELECT vec_id FROM vectors_engine
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0, 0.0, 0.0]'), vec_id
+LIMIT 2;
+
+DROP TABLE vectors_engine;
diff --git a/tests/cases/standalone/common/function/vector/vector_index_explain.result b/tests/cases/standalone/common/function/vector/vector_index_explain.result
new file mode 100644
index 0000000000..111267ae74
--- /dev/null
+++ b/tests/cases/standalone/common/function/vector/vector_index_explain.result
@@ -0,0 +1,270 @@
+-- Vector index explain analyze coverage
+-- ============================================
+-- Part 1: Single table KNN explain
+-- ============================================
+CREATE TABLE vectors_explain (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_explain VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+Affected Rows: 4
+
+ADMIN FLUSH_TABLE('vectors_explain');
+
++--------------------------------------+
+| ADMIN FLUSH_TABLE('vectors_explain') |
++--------------------------------------+
+| 0                                    |
++--------------------------------------+
+
+-- SQLNESS REPLACE ("metrics_per_partition":\s*.*metrics=) "metrics_per_partition": REDACTED metrics=
+-- SQLNESS REPLACE (metrics=\{.*\}) metrics=REDACTED
+-- SQLNESS REPLACE (metrics=\[[^\]]*\]) metrics=REDACTED
+-- SQLNESS REPLACE (RoundRobinBatch.*) REDACTED
+-- SQLNESS REPLACE Hash\(\[vec_id@0\],.* Hash([vec_id@0],REDACTED
+-- SQLNESS REPLACE (-+) -
+-- SQLNESS REPLACE (\s\s+) _
+-- SQLNESS REPLACE "(file_id|time_range_start|time_range_end)":"[^"]+" "$1":"REDACTED"
+-- SQLNESS REPLACE ("[a-z_]+":"[0-9\.]+(ns|us|µs|ms|s)") "DURATION": REDACTED
+-- SQLNESS REPLACE "(size|flat_format)":\s*(\d+|true|false) "$1":REDACTED
+-- SQLNESS REPLACE ,\s*filter=.*?metrics=  metrics=
+-- SQLNESS REPLACE Total\s+rows:\s+\d+ Total rows: REDACTED
+-- SQLNESS REPLACE (peers.*) REDACTED
+-- SQLNESS REPLACE region=\d+\(\d+,\s+\d+\) region=REDACTED
+EXPLAIN ANALYZE VERBOSE
+SELECT vec_id
+FROM vectors_explain
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0]'), vec_id
+LIMIT 2;
+
++-+-+-+
+| stage | node | plan_|
++-+-+-+
+| 0_| 0_|_CooperativeExec metrics=REDACTED_|
+|_|_|_MergeScanExec: REDACTED
+|_|_|_|
+| 1_| 0_|_ProjectionExec: expr=[vec_id@0 as vec_id] metrics=REDACTED_|
+|_|_|_SortPreservingMergeExec: [vec_l2sq_distance(embedding@1, [1.0, 0.0]) ASC NULLS LAST, vec_id@0 ASC NULLS LAST], fetch=2 metrics=REDACTED_|
+|_|_|_SortExec: TopK(fetch=2), expr=[vec_l2sq_distance(embedding@1, [1.0, 0.0]) ASC NULLS LAST, vec_id@0 ASC NULLS LAST], preserve_partitioning=[true] metrics=REDACTED_|
+|_|_|_CooperativeExec metrics=REDACTED_|
+|_|_|_SeqScan: region=REDACTED, {"partition_count":{"count":1, "mem_ranges":0, "files":1, "file_ranges":1}, "projection": ["vec_id", "embedding"], "vector_index_k": 2, "files": [{"file_id":"REDACTED","time_range_start":"REDACTED","time_range_end":"REDACTED","rows":4,"size":REDACTED,"index_size":902}], "flat_format":REDACTED, "metrics_per_partition": REDACTED metrics=REDACTED |
+|_|_|_|
+|_|_| Total rows: REDACTED_|
++-+-+-+
+
+DROP TABLE vectors_explain;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 2: Join with vector order/limit
+-- ============================================
+CREATE TABLE vectors_explain_left (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+CREATE TABLE vectors_explain_right (
+    vec_id INT,
+    note STRING,
+    ts TIMESTAMP TIME INDEX,
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_explain_left VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+Affected Rows: 4
+
+INSERT INTO vectors_explain_right VALUES
+    (3, 'keep', '2024-01-01 00:00:02'),
+    (4, 'keep', '2024-01-01 00:00:03');
+
+Affected Rows: 2
+
+ADMIN FLUSH_TABLE('vectors_explain_left');
+
++-------------------------------------------+
+| ADMIN FLUSH_TABLE('vectors_explain_left') |
++-------------------------------------------+
+| 0                                         |
++-------------------------------------------+
+
+-- SQLNESS REPLACE ("metrics_per_partition":\s*.*metrics=) "metrics_per_partition": REDACTED metrics=
+-- SQLNESS REPLACE (metrics=\{.*\}) metrics=REDACTED
+-- SQLNESS REPLACE (metrics=\[[^\]]*\]) metrics=REDACTED
+-- SQLNESS REPLACE (RoundRobinBatch.*) REDACTED
+-- SQLNESS REPLACE Hash\(\[vec_id@0\],.* Hash([vec_id@0],REDACTED
+-- SQLNESS REPLACE (-+) -
+-- SQLNESS REPLACE (\s\s+) _
+-- SQLNESS REPLACE "(file_id|time_range_start|time_range_end)":"[^"]+" "$1":"REDACTED"
+-- SQLNESS REPLACE ("[a-z_]+":"[0-9\.]+(ns|us|µs|ms|s)") "DURATION": REDACTED
+-- SQLNESS REPLACE "(size|flat_format)":\s*(\d+|true|false) "$1":REDACTED
+-- SQLNESS REPLACE ,\s*filter=.*?metrics=  metrics=
+-- SQLNESS REPLACE Total\s+rows:\s+\d+ Total rows: REDACTED
+-- SQLNESS REPLACE (peers.*) REDACTED
+-- SQLNESS REPLACE region=\d+\(\d+,\s+\d+\) region=REDACTED
+EXPLAIN ANALYZE VERBOSE
+SELECT l.vec_id
+FROM vectors_explain_left l
+JOIN vectors_explain_right r ON l.vec_id = r.vec_id
+ORDER BY vec_l2sq_distance(l.embedding, '[1.0, 0.0]'), l.vec_id
+LIMIT 1;
+
++-+-+-+
+| stage | node | plan_|
++-+-+-+
+| 0_| 0_|_ProjectionExec: expr=[vec_id@0 as vec_id] metrics=REDACTED_|
+|_|_|_SortPreservingMergeExec: [vec_l2sq_distance(embedding@1, [1.0, 0.0]) ASC NULLS LAST, vec_id@0 ASC NULLS LAST], fetch=1 metrics=REDACTED_|
+|_|_|_SortExec: TopK(fetch=1), expr=[vec_l2sq_distance(embedding@1, [1.0, 0.0]) ASC NULLS LAST, vec_id@0 ASC NULLS LAST], preserve_partitioning=[true] metrics=REDACTED_|
+|_|_|_CoalesceBatchesExec: target_batch_size=8192 metrics=REDACTED_|
+|_|_|_HashJoinExec: mode=Partitioned, join_type=Inner, on=[(vec_id@0, vec_id@0)], projection=[vec_id@0, embedding@1] metrics=REDACTED_|
+|_|_|_CoalesceBatchesExec: target_batch_size=8192 metrics=REDACTED_|
+|_|_|_RepartitionExec: partitioning=Hash([vec_id@0],REDACTED
+|_|_|_ProjectionExec: expr=[vec_id@0 as vec_id, embedding@2 as embedding] metrics=REDACTED_|
+|_|_|_CooperativeExec metrics=REDACTED_|
+|_|_|_MergeScanExec: REDACTED
+|_|_|_CoalesceBatchesExec: target_batch_size=8192 metrics=REDACTED_|
+|_|_|_RepartitionExec: partitioning=Hash([vec_id@0],REDACTED
+|_|_|_ProjectionExec: expr=[vec_id@0 as vec_id] metrics=REDACTED_|
+|_|_|_CooperativeExec metrics=REDACTED_|
+|_|_|_MergeScanExec: REDACTED
+|_|_|_|
+| 1_| 0_|_CooperativeExec metrics=REDACTED_|
+|_|_|_SeqScan: region=REDACTED, {"partition_count":{"count":1, "mem_ranges":0, "files":1, "file_ranges":1}, "projection": ["vec_id", "ts", "embedding"], "files": [{"file_id":"REDACTED","time_range_start":"REDACTED","time_range_end":"REDACTED","rows":4,"size":REDACTED,"index_size":902}], "flat_format":REDACTED, "metrics_per_partition": REDACTED metrics=REDACTED |
+|_|_|_|
+| 1_| 0_|_CooperativeExec metrics=REDACTED_|
+|_|_|_SeqScan: region=REDACTED, {"partition_count":{"count":1, "mem_ranges":1, "files":0, "file_ranges":0}, "projection": ["vec_id", "note", "ts"], "flat_format":REDACTED, "metrics_per_partition": REDACTED metrics=REDACTED_|
+|_|_|_|
+|_|_| Total rows: REDACTED_|
++-+-+-+
+
+DROP TABLE vectors_explain_left;
+
+Affected Rows: 0
+
+DROP TABLE vectors_explain_right;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 3: Cosine and dot explain coverage
+-- ============================================
+CREATE TABLE vectors_explain_metric (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'cosine'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_explain_metric VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.0, 1.0]'),
+    (3, '2024-01-01 00:00:02', '[-1.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, -1.0]');
+
+Affected Rows: 4
+
+ADMIN FLUSH_TABLE('vectors_explain_metric');
+
++---------------------------------------------+
+| ADMIN FLUSH_TABLE('vectors_explain_metric') |
++---------------------------------------------+
+| 0                                           |
++---------------------------------------------+
+
+-- SQLNESS REPLACE ("metrics_per_partition":\s*.*metrics=) "metrics_per_partition": REDACTED metrics=
+-- SQLNESS REPLACE (metrics=\{.*\}) metrics=REDACTED
+-- SQLNESS REPLACE (metrics=\[[^\]]*\]) metrics=REDACTED
+-- SQLNESS REPLACE (RoundRobinBatch.*) REDACTED
+-- SQLNESS REPLACE Hash\(\[vec_id@0\],.* Hash([vec_id@0],REDACTED
+-- SQLNESS REPLACE (-+) -
+-- SQLNESS REPLACE (\s\s+) _
+-- SQLNESS REPLACE "(file_id|time_range_start|time_range_end)":"[^"]+" "$1":"REDACTED"
+-- SQLNESS REPLACE ("[a-z_]+":"[0-9\.]+(ns|us|µs|ms|s)") "DURATION": REDACTED
+-- SQLNESS REPLACE "(size|flat_format)":\s*(\d+|true|false) "$1":REDACTED
+-- SQLNESS REPLACE ,\s*filter=.*?metrics=  metrics=
+-- SQLNESS REPLACE Total\s+rows:\s+\d+ Total rows: REDACTED
+-- SQLNESS REPLACE (peers.*) REDACTED
+-- SQLNESS REPLACE region=\d+\(\d+,\s+\d+\) region=REDACTED
+EXPLAIN ANALYZE VERBOSE
+SELECT vec_id
+FROM vectors_explain_metric
+ORDER BY vec_cos_distance(embedding, '[1.0, 0.0]'), vec_id
+LIMIT 2;
+
++-+-+-+
+| stage | node | plan_|
++-+-+-+
+| 0_| 0_|_CooperativeExec metrics=REDACTED_|
+|_|_|_MergeScanExec: REDACTED
+|_|_|_|
+| 1_| 0_|_ProjectionExec: expr=[vec_id@0 as vec_id] metrics=REDACTED_|
+|_|_|_SortPreservingMergeExec: [vec_cos_distance(embedding@1, [1.0, 0.0]) ASC NULLS LAST, vec_id@0 ASC NULLS LAST], fetch=2 metrics=REDACTED_|
+|_|_|_SortExec: TopK(fetch=2), expr=[vec_cos_distance(embedding@1, [1.0, 0.0]) ASC NULLS LAST, vec_id@0 ASC NULLS LAST], preserve_partitioning=[true] metrics=REDACTED_|
+|_|_|_CooperativeExec metrics=REDACTED_|
+|_|_|_SeqScan: region=REDACTED, {"partition_count":{"count":1, "mem_ranges":0, "files":1, "file_ranges":1}, "projection": ["vec_id", "embedding"], "vector_index_k": 2, "files": [{"file_id":"REDACTED","time_range_start":"REDACTED","time_range_end":"REDACTED","rows":4,"size":REDACTED,"index_size":902}], "flat_format":REDACTED, "metrics_per_partition": REDACTED metrics=REDACTED |
+|_|_|_|
+|_|_| Total rows: REDACTED_|
++-+-+-+
+
+-- SQLNESS REPLACE ("metrics_per_partition":\s*.*metrics=) "metrics_per_partition": REDACTED metrics=
+-- SQLNESS REPLACE (metrics=\{.*\}) metrics=REDACTED
+-- SQLNESS REPLACE (metrics=\[[^\]]*\]) metrics=REDACTED
+-- SQLNESS REPLACE (RoundRobinBatch.*) REDACTED
+-- SQLNESS REPLACE Hash\(\[vec_id@0\],.* Hash([vec_id@0],REDACTED
+-- SQLNESS REPLACE (-+) -
+-- SQLNESS REPLACE (\s\s+) _
+-- SQLNESS REPLACE "(file_id|time_range_start|time_range_end)":"[^"]+" "$1":"REDACTED"
+-- SQLNESS REPLACE ("[a-z_]+":"[0-9\.]+(ns|us|µs|ms|s)") "DURATION": REDACTED
+-- SQLNESS REPLACE "(size|flat_format)":\s*(\d+|true|false) "$1":REDACTED
+-- SQLNESS REPLACE ,\s*filter=.*?metrics=  metrics=
+-- SQLNESS REPLACE Total\s+rows:\s+\d+ Total rows: REDACTED
+-- SQLNESS REPLACE (peers.*) REDACTED
+-- SQLNESS REPLACE region=\d+\(\d+,\s+\d+\) region=REDACTED
+EXPLAIN ANALYZE VERBOSE
+SELECT vec_id
+FROM vectors_explain_metric
+ORDER BY vec_dot_product(embedding, '[1.0, 0.0]') DESC, vec_id
+LIMIT 2;
+
++-+-+-+
+| stage | node | plan_|
++-+-+-+
+| 0_| 0_|_CooperativeExec metrics=REDACTED_|
+|_|_|_MergeScanExec: REDACTED
+|_|_|_|
+| 1_| 0_|_ProjectionExec: expr=[vec_id@0 as vec_id] metrics=REDACTED_|
+|_|_|_SortPreservingMergeExec: [vec_dot_product(embedding@1, [1.0, 0.0]) DESC, vec_id@0 ASC NULLS LAST], fetch=2 metrics=REDACTED_|
+|_|_|_SortExec: TopK(fetch=2), expr=[vec_dot_product(embedding@1, [1.0, 0.0]) DESC, vec_id@0 ASC NULLS LAST], preserve_partitioning=[true] metrics=REDACTED_|
+|_|_|_CooperativeExec metrics=REDACTED_|
+|_|_|_SeqScan: region=REDACTED, {"partition_count":{"count":1, "mem_ranges":0, "files":1, "file_ranges":1}, "projection": ["vec_id", "embedding"], "vector_index_k": 2, "files": [{"file_id":"REDACTED","time_range_start":"REDACTED","time_range_end":"REDACTED","rows":4,"size":REDACTED,"index_size":902}], "flat_format":REDACTED, "metrics_per_partition": REDACTED metrics=REDACTED |
+|_|_|_|
+|_|_| Total rows: REDACTED_|
++-+-+-+
+
+DROP TABLE vectors_explain_metric;
+
+Affected Rows: 0
+
diff --git a/tests/cases/standalone/common/function/vector/vector_index_explain.sql b/tests/cases/standalone/common/function/vector/vector_index_explain.sql
new file mode 100644
index 0000000000..50bf3cdbaa
--- /dev/null
+++ b/tests/cases/standalone/common/function/vector/vector_index_explain.sql
@@ -0,0 +1,155 @@
+-- Vector index explain analyze coverage
+
+
+-- ============================================
+-- Part 1: Single table KNN explain
+-- ============================================
+CREATE TABLE vectors_explain (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_explain VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+ADMIN FLUSH_TABLE('vectors_explain');
+
+-- SQLNESS REPLACE ("metrics_per_partition":\s*.*metrics=) "metrics_per_partition": REDACTED metrics=
+-- SQLNESS REPLACE (metrics=\{.*\}) metrics=REDACTED
+-- SQLNESS REPLACE (metrics=\[[^\]]*\]) metrics=REDACTED
+-- SQLNESS REPLACE (RoundRobinBatch.*) REDACTED
+-- SQLNESS REPLACE Hash\(\[vec_id@0\],.* Hash([vec_id@0],REDACTED
+-- SQLNESS REPLACE (-+) -
+-- SQLNESS REPLACE (\s\s+) _
+-- SQLNESS REPLACE "(file_id|time_range_start|time_range_end)":"[^"]+" "$1":"REDACTED"
+-- SQLNESS REPLACE ("[a-z_]+":"[0-9\.]+(ns|us|µs|ms|s)") "DURATION": REDACTED
+-- SQLNESS REPLACE "(size|flat_format)":\s*(\d+|true|false) "$1":REDACTED
+-- SQLNESS REPLACE ,\s*filter=.*?metrics=  metrics=
+-- SQLNESS REPLACE Total\s+rows:\s+\d+ Total rows: REDACTED
+-- SQLNESS REPLACE (peers.*) REDACTED
+-- SQLNESS REPLACE region=\d+\(\d+,\s+\d+\) region=REDACTED
+EXPLAIN ANALYZE VERBOSE
+SELECT vec_id
+FROM vectors_explain
+ORDER BY vec_l2sq_distance(embedding, '[1.0, 0.0]'), vec_id
+LIMIT 2;
+
+DROP TABLE vectors_explain;
+
+-- ============================================
+-- Part 2: Join with vector order/limit
+-- ============================================
+CREATE TABLE vectors_explain_left (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+CREATE TABLE vectors_explain_right (
+    vec_id INT,
+    note STRING,
+    ts TIMESTAMP TIME INDEX,
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_explain_left VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+INSERT INTO vectors_explain_right VALUES
+    (3, 'keep', '2024-01-01 00:00:02'),
+    (4, 'keep', '2024-01-01 00:00:03');
+
+ADMIN FLUSH_TABLE('vectors_explain_left');
+
+-- SQLNESS REPLACE ("metrics_per_partition":\s*.*metrics=) "metrics_per_partition": REDACTED metrics=
+-- SQLNESS REPLACE (metrics=\{.*\}) metrics=REDACTED
+-- SQLNESS REPLACE (metrics=\[[^\]]*\]) metrics=REDACTED
+-- SQLNESS REPLACE (RoundRobinBatch.*) REDACTED
+-- SQLNESS REPLACE Hash\(\[vec_id@0\],.* Hash([vec_id@0],REDACTED
+-- SQLNESS REPLACE (-+) -
+-- SQLNESS REPLACE (\s\s+) _
+-- SQLNESS REPLACE "(file_id|time_range_start|time_range_end)":"[^"]+" "$1":"REDACTED"
+-- SQLNESS REPLACE ("[a-z_]+":"[0-9\.]+(ns|us|µs|ms|s)") "DURATION": REDACTED
+-- SQLNESS REPLACE "(size|flat_format)":\s*(\d+|true|false) "$1":REDACTED
+-- SQLNESS REPLACE ,\s*filter=.*?metrics=  metrics=
+-- SQLNESS REPLACE Total\s+rows:\s+\d+ Total rows: REDACTED
+-- SQLNESS REPLACE (peers.*) REDACTED
+-- SQLNESS REPLACE region=\d+\(\d+,\s+\d+\) region=REDACTED
+EXPLAIN ANALYZE VERBOSE
+SELECT l.vec_id
+FROM vectors_explain_left l
+JOIN vectors_explain_right r ON l.vec_id = r.vec_id
+ORDER BY vec_l2sq_distance(l.embedding, '[1.0, 0.0]'), l.vec_id
+LIMIT 1;
+
+DROP TABLE vectors_explain_left;
+DROP TABLE vectors_explain_right;
+
+-- ============================================
+-- Part 3: Cosine and dot explain coverage
+-- ============================================
+CREATE TABLE vectors_explain_metric (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'cosine'),
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_explain_metric VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.0, 1.0]'),
+    (3, '2024-01-01 00:00:02', '[-1.0, 0.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, -1.0]');
+
+ADMIN FLUSH_TABLE('vectors_explain_metric');
+
+-- SQLNESS REPLACE ("metrics_per_partition":\s*.*metrics=) "metrics_per_partition": REDACTED metrics=
+-- SQLNESS REPLACE (metrics=\{.*\}) metrics=REDACTED
+-- SQLNESS REPLACE (metrics=\[[^\]]*\]) metrics=REDACTED
+-- SQLNESS REPLACE (RoundRobinBatch.*) REDACTED
+-- SQLNESS REPLACE Hash\(\[vec_id@0\],.* Hash([vec_id@0],REDACTED
+-- SQLNESS REPLACE (-+) -
+-- SQLNESS REPLACE (\s\s+) _
+-- SQLNESS REPLACE "(file_id|time_range_start|time_range_end)":"[^"]+" "$1":"REDACTED"
+-- SQLNESS REPLACE ("[a-z_]+":"[0-9\.]+(ns|us|µs|ms|s)") "DURATION": REDACTED
+-- SQLNESS REPLACE "(size|flat_format)":\s*(\d+|true|false) "$1":REDACTED
+-- SQLNESS REPLACE ,\s*filter=.*?metrics=  metrics=
+-- SQLNESS REPLACE Total\s+rows:\s+\d+ Total rows: REDACTED
+-- SQLNESS REPLACE (peers.*) REDACTED
+-- SQLNESS REPLACE region=\d+\(\d+,\s+\d+\) region=REDACTED
+EXPLAIN ANALYZE VERBOSE
+SELECT vec_id
+FROM vectors_explain_metric
+ORDER BY vec_cos_distance(embedding, '[1.0, 0.0]'), vec_id
+LIMIT 2;
+
+-- SQLNESS REPLACE ("metrics_per_partition":\s*.*metrics=) "metrics_per_partition": REDACTED metrics=
+-- SQLNESS REPLACE (metrics=\{.*\}) metrics=REDACTED
+-- SQLNESS REPLACE (metrics=\[[^\]]*\]) metrics=REDACTED
+-- SQLNESS REPLACE (RoundRobinBatch.*) REDACTED
+-- SQLNESS REPLACE Hash\(\[vec_id@0\],.* Hash([vec_id@0],REDACTED
+-- SQLNESS REPLACE (-+) -
+-- SQLNESS REPLACE (\s\s+) _
+-- SQLNESS REPLACE "(file_id|time_range_start|time_range_end)":"[^"]+" "$1":"REDACTED"
+-- SQLNESS REPLACE ("[a-z_]+":"[0-9\.]+(ns|us|µs|ms|s)") "DURATION": REDACTED
+-- SQLNESS REPLACE "(size|flat_format)":\s*(\d+|true|false) "$1":REDACTED
+-- SQLNESS REPLACE ,\s*filter=.*?metrics=  metrics=
+-- SQLNESS REPLACE Total\s+rows:\s+\d+ Total rows: REDACTED
+-- SQLNESS REPLACE (peers.*) REDACTED
+-- SQLNESS REPLACE region=\d+\(\d+,\s+\d+\) region=REDACTED
+EXPLAIN ANALYZE VERBOSE
+SELECT vec_id
+FROM vectors_explain_metric
+ORDER BY vec_dot_product(embedding, '[1.0, 0.0]') DESC, vec_id
+LIMIT 2;
+
+DROP TABLE vectors_explain_metric;
diff --git a/tests/cases/standalone/common/function/vector/vector_index_join.result b/tests/cases/standalone/common/function/vector/vector_index_join.result
new file mode 100644
index 0000000000..51fb393405
--- /dev/null
+++ b/tests/cases/standalone/common/function/vector/vector_index_join.result
@@ -0,0 +1,240 @@
+-- Vector index join/subquery coverage
+-- ============================================
+-- Part 1: Join should not pre-limit left table
+-- ============================================
+CREATE TABLE vectors_join_left (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+CREATE TABLE vectors_join_right (
+    vec_id INT,
+    note STRING,
+    ts TIMESTAMP TIME INDEX,
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_join_left VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+Affected Rows: 4
+
+INSERT INTO vectors_join_right VALUES
+    (3, 'keep', '2024-01-01 00:00:02'),
+    (4, 'keep', '2024-01-01 00:00:03');
+
+Affected Rows: 2
+
+SELECT l.vec_id, round(vec_l2sq_distance(l.embedding, '[1.0, 0.0]'), 2) AS dist
+FROM vectors_join_left l
+JOIN vectors_join_right r ON l.vec_id = r.vec_id
+ORDER BY dist, l.vec_id
+LIMIT 1;
+
++--------+------+
+| vec_id | dist |
++--------+------+
+| 4      | 1.81 |
++--------+------+
+
+DROP TABLE vectors_join_left;
+
+Affected Rows: 0
+
+DROP TABLE vectors_join_right;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 2: Subquery should be a barrier
+-- ============================================
+CREATE TABLE vectors_subquery (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_subquery VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+Affected Rows: 4
+
+SELECT s.vec_id, round(vec_l2sq_distance(s.embedding, '[1.0, 0.0]'), 2) AS dist
+FROM (
+    SELECT * FROM vectors_subquery WHERE vec_id >= 3
+) s
+ORDER BY dist, s.vec_id
+LIMIT 1;
+
++--------+------+
+| vec_id | dist |
++--------+------+
+| 4      | 1.81 |
++--------+------+
+
+DROP TABLE vectors_subquery;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 3: LEFT JOIN should not pre-limit
+-- ============================================
+CREATE TABLE vectors_left_join (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+CREATE TABLE vectors_left_join_filter (
+    vec_id INT,
+    keep BOOLEAN,
+    ts TIMESTAMP TIME INDEX,
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_left_join VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+Affected Rows: 4
+
+-- Only vec_id 3,4 have matching rows in filter table
+INSERT INTO vectors_left_join_filter VALUES
+    (3, true, '2024-01-01 00:00:02'),
+    (4, true, '2024-01-01 00:00:03');
+
+Affected Rows: 2
+
+-- LEFT JOIN then filter by IS NOT NULL
+-- Should return vec_id=4 (dist=1.81), not vec_id=1 or 2
+SELECT l.vec_id, round(vec_l2sq_distance(l.embedding, '[1.0, 0.0]'), 2) AS dist
+FROM vectors_left_join l
+LEFT JOIN vectors_left_join_filter r ON l.vec_id = r.vec_id
+WHERE r.vec_id IS NOT NULL
+ORDER BY dist, l.vec_id
+LIMIT 1;
+
++--------+------+
+| vec_id | dist |
++--------+------+
+| 4      | 1.81 |
++--------+------+
+
+DROP TABLE vectors_left_join;
+
+Affected Rows: 0
+
+DROP TABLE vectors_left_join_filter;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 4: Inlineable subquery should allow hint
+-- ============================================
+CREATE TABLE vectors_inline_subquery (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_inline_subquery VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+Affected Rows: 4
+
+ADMIN FLUSH_TABLE('vectors_inline_subquery');
+
++----------------------------------------------+
+| ADMIN FLUSH_TABLE('vectors_inline_subquery') |
++----------------------------------------------+
+| 0                                            |
++----------------------------------------------+
+
+-- Subquery without LIMIT/DISTINCT/aggregation can be inlined
+-- Vector hint should be able to push down
+SELECT s.vec_id, round(vec_l2sq_distance(s.embedding, '[1.0, 0.0]'), 2) AS dist
+FROM (
+    SELECT * FROM vectors_inline_subquery WHERE vec_id >= 1
+) s
+ORDER BY dist, s.vec_id
+LIMIT 2;
+
++--------+------+
+| vec_id | dist |
++--------+------+
+| 1      | 0.0  |
+| 2      | 0.01 |
++--------+------+
+
+DROP TABLE vectors_inline_subquery;
+
+Affected Rows: 0
+
+-- ============================================
+-- Part 5: CTE should be a barrier
+-- ============================================
+CREATE TABLE vectors_cte (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+Affected Rows: 0
+
+INSERT INTO vectors_cte VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+Affected Rows: 4
+
+-- CTE acts as optimization barrier
+-- Filter in CTE limits to vec_id >= 3, so result should be vec_id=4 (dist=1.81)
+WITH filtered AS (
+    SELECT * FROM vectors_cte WHERE vec_id >= 3
+)
+SELECT vec_id, round(vec_l2sq_distance(embedding, '[1.0, 0.0]'), 2) AS dist
+FROM filtered
+ORDER BY dist, vec_id
+LIMIT 1;
+
++--------+------+
+| vec_id | dist |
++--------+------+
+| 4      | 1.81 |
++--------+------+
+
+DROP TABLE vectors_cte;
+
+Affected Rows: 0
+
diff --git a/tests/cases/standalone/common/function/vector/vector_index_join.sql b/tests/cases/standalone/common/function/vector/vector_index_join.sql
new file mode 100644
index 0000000000..699942b300
--- /dev/null
+++ b/tests/cases/standalone/common/function/vector/vector_index_join.sql
@@ -0,0 +1,159 @@
+-- Vector index join/subquery coverage
+
+-- ============================================
+-- Part 1: Join should not pre-limit left table
+-- ============================================
+CREATE TABLE vectors_join_left (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+CREATE TABLE vectors_join_right (
+    vec_id INT,
+    note STRING,
+    ts TIMESTAMP TIME INDEX,
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_join_left VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+INSERT INTO vectors_join_right VALUES
+    (3, 'keep', '2024-01-01 00:00:02'),
+    (4, 'keep', '2024-01-01 00:00:03');
+
+SELECT l.vec_id, round(vec_l2sq_distance(l.embedding, '[1.0, 0.0]'), 2) AS dist
+FROM vectors_join_left l
+JOIN vectors_join_right r ON l.vec_id = r.vec_id
+ORDER BY dist, l.vec_id
+LIMIT 1;
+
+DROP TABLE vectors_join_left;
+DROP TABLE vectors_join_right;
+
+-- ============================================
+-- Part 2: Subquery should be a barrier
+-- ============================================
+CREATE TABLE vectors_subquery (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_subquery VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+SELECT s.vec_id, round(vec_l2sq_distance(s.embedding, '[1.0, 0.0]'), 2) AS dist
+FROM (
+    SELECT * FROM vectors_subquery WHERE vec_id >= 3
+) s
+ORDER BY dist, s.vec_id
+LIMIT 1;
+
+DROP TABLE vectors_subquery;
+
+-- ============================================
+-- Part 3: LEFT JOIN should not pre-limit
+-- ============================================
+CREATE TABLE vectors_left_join (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+CREATE TABLE vectors_left_join_filter (
+    vec_id INT,
+    keep BOOLEAN,
+    ts TIMESTAMP TIME INDEX,
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_left_join VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+-- Only vec_id 3,4 have matching rows in filter table
+INSERT INTO vectors_left_join_filter VALUES
+    (3, true, '2024-01-01 00:00:02'),
+    (4, true, '2024-01-01 00:00:03');
+
+-- LEFT JOIN then filter by IS NOT NULL
+-- Should return vec_id=4 (dist=1.81), not vec_id=1 or 2
+SELECT l.vec_id, round(vec_l2sq_distance(l.embedding, '[1.0, 0.0]'), 2) AS dist
+FROM vectors_left_join l
+LEFT JOIN vectors_left_join_filter r ON l.vec_id = r.vec_id
+WHERE r.vec_id IS NOT NULL
+ORDER BY dist, l.vec_id
+LIMIT 1;
+
+DROP TABLE vectors_left_join;
+DROP TABLE vectors_left_join_filter;
+
+-- ============================================
+-- Part 4: Inlineable subquery should allow hint
+-- ============================================
+CREATE TABLE vectors_inline_subquery (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_inline_subquery VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+ADMIN FLUSH_TABLE('vectors_inline_subquery');
+
+-- Subquery without LIMIT/DISTINCT/aggregation can be inlined
+-- Vector hint should be able to push down
+SELECT s.vec_id, round(vec_l2sq_distance(s.embedding, '[1.0, 0.0]'), 2) AS dist
+FROM (
+    SELECT * FROM vectors_inline_subquery WHERE vec_id >= 1
+) s
+ORDER BY dist, s.vec_id
+LIMIT 2;
+
+DROP TABLE vectors_inline_subquery;
+
+-- ============================================
+-- Part 5: CTE should be a barrier
+-- ============================================
+CREATE TABLE vectors_cte (
+    vec_id INT,
+    ts TIMESTAMP TIME INDEX,
+    embedding VECTOR(2) NOT NULL VECTOR INDEX WITH (metric = 'l2sq'),
+    PRIMARY KEY (vec_id)
+);
+
+INSERT INTO vectors_cte VALUES
+    (1, '2024-01-01 00:00:00', '[1.0, 0.0]'),
+    (2, '2024-01-01 00:00:01', '[0.9, 0.0]'),
+    (3, '2024-01-01 00:00:02', '[0.0, 1.0]'),
+    (4, '2024-01-01 00:00:03', '[0.0, 0.9]');
+
+-- CTE acts as optimization barrier
+-- Filter in CTE limits to vec_id >= 3, so result should be vec_id=4 (dist=1.81)
+WITH filtered AS (
+    SELECT * FROM vectors_cte WHERE vec_id >= 3
+)
+SELECT vec_id, round(vec_l2sq_distance(embedding, '[1.0, 0.0]'), 2) AS dist
+FROM filtered
+ORDER BY dist, vec_id
+LIMIT 1;
+
+DROP TABLE vectors_cte;
diff --git a/tests/runner/src/env/bare.rs b/tests/runner/src/env/bare.rs
index a0bf67dd97..1501d18512 100644
--- a/tests/runner/src/env/bare.rs
+++ b/tests/runner/src/env/bare.rs
@@ -528,7 +528,7 @@ impl Env {
                 "--bin",
                 "greptime",
                 "--features",
-                "pg_kvbackend,mysql_kvbackend",
+                "pg_kvbackend,mysql_kvbackend,vector_index",
             ])
             .output()
             .expect("Failed to start GreptimeDB");