Merge fix/hnsw-deleted-topk: live-only search results, batched parallel index build

Co-Authored-By: Claude Fable 5.1 <[email protected]>

BENCHMARKS.md

@@ -243,6 +243,28 @@ results.
 | 10000 | 104 | 1487 | 33.8 | 13.7 | 13.9 | 0.49 | 0.51 | 2020.9 |
 | 100000 | 1376 | 20285 | 728.9 | 353.1 | 142.2 | 4.65 | 4.78 | 214.7 |
 
+### After: batched bulk build (optionally parallel); deletions handled in search
+
+Profiling showed **90% of a build's distance evaluations are in back-link
+pruning**. The bulk build now inserts in batches: plan each node's neighbours
+against the graph as it stood at the start of the batch, link, then prune every
+overflowing list once. That is less work even single-threaded (a node gaining
+several back-links in a batch is pruned once), and with the `parallel` feature
+planning and pruning run on a thread pool. The graph is deterministic and the
+same with or without the feature. Parallelising *within* one insert was tried
+first and gave only 1.45x on 16 cores (tasks too small).
+
+| build | 1K | 10K | 100K |
+|---|---:|---:|---:|
+| v2.4.0 | 116 ms | 1676 ms | ~21 s |
+| batched | 83 ms | 1074 ms | 19.2 s |
+| batched + `parallel` (16 cores) | 34 ms | 388 ms | 5.9 s |
+
+Recall on clustered data is unchanged or slightly better (100K, `ef = 64`:
+0.984 -> 0.9945). On uniform random data it dips slightly (10K, `ef = 64`:
+0.474 -> 0.444), the cost of batch members not seeing each other while
+planning; batches are capped at 1/16 of the graph and 512 nodes.
+
 ## Vector Search Latency
 
 Brute-force cosine similarity over 384-dimensional embeddings (OpenAI text-embedding-3-small size).

CHANGELOG.md

@@ -1,5 +1,24 @@
 # Changelog
 
+## Unreleased
+
+### Search
+- `clawhdf5-ann`: **faster index builds.** Back-link pruning is 90% of a
+  build's distance evaluations; the bulk build now inserts in batches and
+  prunes each overflowing neighbour list once per batch (10K: 1676 -> 1074 ms).
+  With the `parallel` feature, planning and pruning run on a thread pool (10K:
+  388 ms, 100K: ~21 s -> 5.9 s on 16 cores). The graph is deterministic and
+  identical with or without the feature. `clawhdf5-agent`'s `parallel` feature
+  enables it for the agent's index and is now **on by default** (adds `rayon`
+  to the default dependency set; build with `--no-default-features --features
+  float16,hnsw` to opt out).
+- `clawhdf5-ann`: `HnswIndex::search` returned fewer than `k` results — often
+  none — when the records nearest the query had been deleted: it collected `ef`
+  candidates, *then* dropped the deleted ones, *then* took `k`. Deleted nodes
+  are now traversed as waypoints but never occupy a result slot, so a search
+  returns the `k` nearest live records. Matters for any store that deletes or
+  supersedes memories without compacting straight away.
+
 ## v2.4.0 (2026-09-19)
 
 ### Upgrade Notes

CLAUDE.md

@@ -33,6 +33,8 @@ Cargo workspace with 16 crates under `crates/` (plus `libaec-sys`, an internal F
   the approximate `clawhdf5-ann` index for the vector stage (the index mirrors
   the cache and self-heals on drift). Build the agent with
   `--no-default-features --features float16` to force the exact linear cosine scan.
+  The agent's `parallel` feature (also default) builds the index on a thread
+  pool; the graph is identical with or without it.
   The index uses the HNSW paper's diversity heuristic for neighbour selection
   (plain closest-M capped recall on clustered data: 0.31 recall@10 at 100K). Its
   graph is saved to `<store>.h5.ann` at each checkpoint and reloaded by `open()`

crates/clawhdf5-agent/Cargo.toml

@@ -45,9 +45,11 @@ name = "memory_bench"
 harness = false
 
 [features]
-default = ["float16", "hnsw"]
+default = ["float16", "hnsw", "parallel"]
 float16 = ["half"]
-parallel = ["rayon"]
+# Rayon-parallel brute-force search strategies, and a parallel bulk build of
+# the HNSW index (same graph, several times faster on a multi-core machine).
+parallel = ["rayon", "clawhdf5-ann?/parallel"]
 # Compress embeddings with Zstd instead of deflate when
 # `MemoryConfig::compression` is on. Off by default: it links libzstd (C).
 zstd = ["clawhdf5/zstd"]

crates/clawhdf5-ann/src/hnsw.rs

@@ -248,65 +248,61 @@ impl HnswIndex {
         let mut entry_point = 0;
         let mut ep_level = node_levels[0];
 
-        // Insert nodes one by one
+        // Insert in batches. Each batch is planned against the graph as it
-        for i in 1..n {
+        // stood when the batch began (read-only, so the plans are independent
-            let node_level = node_levels[i];
+        // and run in parallel with the `parallel` feature), then linked, then
-            let mut ep = entry_point;
+        // every neighbour list that overflowed is pruned once. Pruning is ~90%
-
+        // of a build's distance evaluations, and a node that gains several
-            // Phase 1: greedy search from top layer down to node_level + 1
+        // back-links in one batch is pruned once instead of once per link.
-            let start_layer = ep_level;
+        //
-            for layer in (node_level + 1..=start_layer).rev() {
+        // Nodes in the same batch cannot see each other while planning, so
-                ep = greedy_closest(vectors, &graph[layer], &vectors[i], ep, metric);
+        // batches start at one node and grow only as the graph does — a batch
+        // is never more than a small fraction of what is already linked. The
+        // result is deterministic and identical with or without `parallel`.
+        let mut next = 1;
+        while next < n {
+            let mut end = (next + batch_len(next)).min(n);
+            // A node that raises the top layer becomes the new entry point and
+            // changes how every later node descends: give it a batch alone.
+            if let Some(tall) = (next..end).find(|&i| node_levels[i] > ep_level) {
+                end = if tall == next { next + 1 } else { tall };
             }
 
-            // Phase 2: search and connect at layers node_level down to 0
+            let plans = plan_batch(
-            let bottom = if node_level < start_layer {
+                vectors,
-                node_level
+                &graph,
-            } else {
+                &node_levels,
-                start_layer
+                next..end,
-            };
+                entry_point,
-            for layer in (0..=bottom).rev() {
+                ep_level,
-                let max_conn = if layer == 0 { m_max0 } else { m };
+                (m, m_max0, ef_construction),
+                metric,
+            );
 
-                let neighbors = search_layer(
+            let mut overflowed: Vec<(usize, usize)> = Vec::new();
-                    vectors,
+            for (offset, plan) in plans.into_iter().enumerate() {
-                    &graph[layer],
+                let node = next + offset;
-                    &vectors[i],
+                for (layer, selected) in plan {
-                    ep,
+                    let max_conn = if layer == 0 { m_max0 } else { m };
-                    ef_construction,
+                    for &neighbor in &selected {
-                    metric,
+                        let list = &mut graph[layer][neighbor];
-                );
+                        list.push(node);
-
+                        if list.len() == max_conn + 1 {
-                let scored: Vec<(usize, f32)> =
+                            overflowed.push((layer, neighbor));
-                    neighbors.iter().map(|c| (c.id, c.distance)).collect();
+                        }
-                let selected = select_neighbors(vectors, &scored, max_conn, metric);
-
-                // Add bidirectional connections
-                graph[layer][i] = selected.clone();
-                for &neighbor in &selected {
-                    graph[layer][neighbor].push(i);
-                    // Prune if over limit
-                    if graph[layer][neighbor].len() > max_conn {
-                        prune_connections(
-                            vectors,
-                            &mut graph[layer][neighbor],
-                            neighbor,
-                            max_conn,
-                            metric,
-                        );
                     }
-                }
+                    graph[layer][node] = selected;
-
-                if !selected.is_empty() {
-                    ep = selected[0];
                 }
             }
+            prune_overflowed(vectors, &mut graph, overflowed, (m, m_max0), metric);
 
-            // Update entry point if this node has a higher level
+            for (i, &level) in node_levels.iter().enumerate().take(end).skip(next) {
-            if node_level > ep_level {
+                if level > ep_level {
-                entry_point = i;
+                    entry_point = i;
-                ep_level = node_level;
+                    ep_level = level;
+                }
             }
+            next = end;
         }
 
         Self {
@@ -408,22 +404,19 @@ impl HnswIndex {
                 ep,
                 self.ef_construction,
                 self.metric,
+                None,
             );
             let scored: Vec<(usize, f32)> = neighbors.iter().map(|c| (c.id, c.distance)).collect();
             let selected = select_neighbors(&self.vectors, &scored, max_conn, self.metric);
             self.graph[layer][id] = selected.clone();
-            for &neighbor in &selected {
+            link_back(
-                self.graph[layer][neighbor].push(id);
+                &self.vectors,
-                if self.graph[layer][neighbor].len() > max_conn {
+                &mut self.graph[layer],
-                    prune_connections(
+                id,
-                        &self.vectors,
+                &selected,
-                        &mut self.graph[layer][neighbor],
+                max_conn,
-                        neighbor,
+                self.metric,
-                        max_conn,
+            );
-                        self.metric,
-                    );
-                }
-            }
             if !selected.is_empty() {
                 ep = selected[0];
             }
@@ -517,13 +510,21 @@ impl HnswIndex {
             ep = greedy_closest(&self.vectors, &self.graph[layer], query, ep, self.metric);
         }
 
-        // Search layer 0 with ef candidates. Deleted nodes are still traversed
+        // Search layer 0 for the ef nearest *live* nodes. Deleted nodes are
-        // (they remain valid graph waypoints) but are filtered from the result.
+        // still traversed (they remain valid graph waypoints) but take no
-        let candidates = search_layer(&self.vectors, &self.graph[0], query, ep, ef, self.metric);
+        // result slot, so deletions near the query don't shrink the answer.
+        let candidates = search_layer(
+            &self.vectors,
+            &self.graph[0],
+            query,
+            ep,
+            ef,
+            self.metric,
+            Some(&self.deleted),
+        );
 
         candidates
             .into_iter()
-            .filter(|c| !self.deleted[c.id])
             .take(k)
             .map(|c| (c.id, c.distance))
             .collect()
@@ -940,6 +941,14 @@ fn greedy_closest(
 }
 
 /// Search a single layer for the ef closest nodes to `query`.
+/// Best-first search of one layer, returning up to `ef` nodes by ascending
+/// distance.
+///
+/// `skip` marks nodes that must not be *returned* (soft-deleted ones). They
+/// are still traversed — a tombstone is a perfectly good waypoint — but they
+/// never occupy one of the `ef` result slots. Filtering them out afterwards
+/// instead meant a query whose neighbourhood had been deleted got back fewer
+/// than `k` results, or none, however many live records were nearby.
 fn search_layer(
     vectors: &[Vec<f32>],
     layer: &[Vec<usize>],
@@ -947,6 +956,7 @@ fn search_layer(
     ep: usize,
     ef: usize,
     metric: DistanceMetric,
+    skip: Option<&[bool]>,
 ) -> Vec<Candidate> {
     let ep_dist = compute_distance(query, &vectors[ep], metric);
 
@@ -959,16 +969,18 @@ fn search_layer(
 
     // Max-heap of current results (furthest first)
     let mut results = BinaryHeap::new();
-    results.push(FarCandidate {
+    if !skip.is_some_and(|s| s[ep]) {
-        id: ep,
+        results.push(FarCandidate {
-        distance: ep_dist,
+            id: ep,
-    });
+            distance: ep_dist,
+        });
+    }
 
     VISITED.with_borrow_mut(|visited| {
         visited.begin(vectors.len());
         visited.insert(ep);
         search_layer_visit(
-            vectors, layer, query, ef, metric, visited, candidates, results,
+            vectors, layer, query, ef, metric, skip, visited, candidates, results,
         )
     })
 }
@@ -1016,6 +1028,7 @@ fn search_layer_visit(
     query: &[f32],
     ef: usize,
     metric: DistanceMetric,
+    skip: Option<&[bool]>,
     visited: &mut Visited,
     mut candidates: BinaryHeap<Candidate>,
     mut results: BinaryHeap<FarCandidate>,
@@ -1039,6 +1052,9 @@ fn search_layer_visit(
                     id: neighbor,
                     distance: d,
                 });
+                if skip.is_some_and(|s| s[neighbor]) {
+                    continue; // explore through it, but never return it
+                }
                 results.push(FarCandidate {
                     id: neighbor,
                     distance: d,
@@ -1111,6 +1127,125 @@ fn select_neighbors(
     selected
 }
 
+/// How many nodes to plan together once `linked` nodes are in the graph.
+fn batch_len(linked: usize) -> usize {
+    (linked / 16).clamp(1, 512)
+}
+
+/// For each node in `batch`: the neighbours to link it to on each of its
+/// layers, found by searching the graph as it currently stands.
+#[allow(clippy::too_many_arguments)]
+fn plan_batch(
+    vectors: &[Vec<f32>],
+    graph: &[Vec<Vec<usize>>],
+    node_levels: &[usize],
+    batch: std::ops::Range<usize>,
+    entry_point: usize,
+    ep_level: usize,
+    (m, m_max0, ef_construction): (usize, usize, usize),
+    metric: DistanceMetric,
+) -> Vec<Vec<(usize, Vec<usize>)>> {
+    let plan_one = |i: usize| -> Vec<(usize, Vec<usize>)> {
+        let node_level = node_levels[i];
+        let mut ep = entry_point;
+        // Phase 1: greedy descent from the top layer down to node_level + 1.
+        for layer in (node_level + 1..=ep_level).rev() {
+            ep = greedy_closest(vectors, &graph[layer], &vectors[i], ep, metric);
+        }
+        // Phase 2: search and select on every layer the node lives on.
+        let mut plan = Vec::with_capacity(node_level.min(ep_level) + 1);
+        for layer in (0..=node_level.min(ep_level)).rev() {
+            let max_conn = if layer == 0 { m_max0 } else { m };
+            let neighbors = search_layer(
+                vectors,
+                &graph[layer],
+                &vectors[i],
+                ep,
+                ef_construction,
+                metric,
+                None,
+            );
+            let scored: Vec<(usize, f32)> = neighbors.iter().map(|c| (c.id, c.distance)).collect();
+            let selected = select_neighbors(vectors, &scored, max_conn, metric);
+            if let Some(&closest) = selected.first() {
+                ep = closest;
+            }
+            plan.push((layer, selected));
+        }
+        plan
+    };
+
+    #[cfg(feature = "parallel")]
+    if batch.len() >= PARALLEL_MIN {
+        use rayon::prelude::*;
+        return batch.into_par_iter().map(plan_one).collect();
+    }
+    batch.map(plan_one).collect()
+}
+
+/// Prune every `(layer, node)` neighbour list in `overflowed` back to its
+/// limit. Each list belongs to a different node, so they are independent.
+fn prune_overflowed(
+    vectors: &[Vec<f32>],
+    graph: &mut [Vec<Vec<usize>>],
+    overflowed: Vec<(usize, usize)>,
+    (m, m_max0): (usize, usize),
+    metric: DistanceMetric,
+) {
+    let limit = |layer: usize| if layer == 0 { m_max0 } else { m };
+
+    #[cfg(feature = "parallel")]
+    if overflowed.len() >= PARALLEL_MIN {
+        use rayon::prelude::*;
+        let mut work: Vec<(usize, usize, Vec<usize>)> = overflowed
+            .into_iter()
+            .map(|(layer, node)| (layer, node, std::mem::take(&mut graph[layer][node])))
+            .collect();
+        work.par_iter_mut().for_each(|(layer, node, list)| {
+            prune_connections(vectors, list, *node, limit(*layer), metric);
+        });
+        for (layer, node, list) in work {
+            graph[layer][node] = list;
+        }
+        return;
+    }
+
+    for (layer, node) in overflowed {
+        prune_connections(vectors, &mut graph[layer][node], node, limit(layer), metric);
+    }
+}
+
+/// Fewest independent tasks worth handing to the thread pool.
+#[cfg(feature = "parallel")]
+const PARALLEL_MIN: usize = 8;
+
+/// Add the back-link `neighbor -> new_id` for every selected neighbour, pruning
+/// each list that overflows.
+///
+/// Used by incremental [`HnswIndex::insert`]. (A single insert's handful of
+/// prunes is too fine-grained to parallelise profitably — measured 1.45x on 16
+/// cores; bulk builds batch their pruning instead, see `prune_overflowed`.)
+fn link_back(
+    vectors: &[Vec<f32>],
+    layer: &mut [Vec<usize>],
+    new_id: usize,
+    selected: &[usize],
+    max_conn: usize,
+    metric: DistanceMetric,
+) {
+    let mut overflowed: Vec<usize> = Vec::new();
+    for &neighbor in selected {
+        layer[neighbor].push(new_id);
+        if layer[neighbor].len() > max_conn {
+            overflowed.push(neighbor);
+        }
+    }
+
+    for node in overflowed {
+        prune_connections(vectors, &mut layer[node], node, max_conn, metric);
+    }
+}
+
 /// Trim `node`'s neighbour list back to `max_conn` with [`select_neighbors`].
 fn prune_connections(
     vectors: &[Vec<f32>],
@@ -1384,6 +1519,58 @@ mod tests {
         assert!(recall >= 0.95, "incremental recall@10 = {recall}");
     }
 
+    #[test]
+    fn deletions_near_the_query_do_not_shrink_or_degrade_results() {
+        let mut vectors = clustered(2040, 16, 20, 11);
+        let queries = vectors.split_off(2000);
+        let mut index = HnswIndex::build_with_metric(&vectors, 8, 40, DistanceMetric::L2);
+
+        let mut short = 0;
+        let mut hits = 0;
+        for q in &queries {
+            // Delete this query's 40 nearest neighbours: more than ef, so every
+            // candidate a plain search collects is a tombstone.
+            let mut exact: Vec<(usize, f32)> = vectors
+                .iter()
+                .enumerate()
+                .filter(|(i, _)| !index.is_deleted(*i))
+                .map(|(i, v)| (i, compute_distance(q, v, DistanceMetric::L2)))
+                .collect();
+            exact.sort_by(|a, b| a.1.total_cmp(&b.1));
+            for &(id, _) in &exact[..40] {
+                index.mark_deleted(id);
+            }
+            let want: Vec<usize> = exact[40..50].iter().map(|e| e.0).collect();
+
+            let got = index.search(q, 10, 32);
+            assert!(got.iter().all(|(id, _)| !index.is_deleted(*id)));
+            short += usize::from(got.len() < 10);
+            hits += got.iter().filter(|(id, _)| want.contains(id)).count();
+        }
+        assert_eq!(short, 0, "searches returned fewer than k live results");
+        let recall = hits as f64 / (10 * queries.len()) as f64;
+        assert!(recall >= 0.9, "recall@10 among live records = {recall}");
+    }
+
+    #[test]
+    fn bulk_build_is_deterministic() {
+        // Batched planning runs on a thread pool with the `parallel` feature;
+        // the graph must not depend on scheduling. (It is also the same graph
+        // with and without the feature: both take this exact code path.)
+        let vectors = clustered(2500, 16, 20, 21);
+        let a = HnswIndex::build_with_metric(&vectors, 8, 40, DistanceMetric::Cosine);
+        let b = HnswIndex::build_with_metric(&vectors, 8, 40, DistanceMetric::Cosine);
+        assert_eq!(a.graph_to_bytes(), b.graph_to_bytes());
+    }
+
+    #[test]
+    fn batches_stay_a_small_fraction_of_the_graph() {
+        assert_eq!(batch_len(1), 1);
+        assert_eq!(batch_len(15), 1);
+        assert_eq!(batch_len(160), 10);
+        assert_eq!(batch_len(1_000_000), 512);
+    }
+
     #[test]
     fn graph_bytes_round_trip_gives_identical_searches() {
         let mut vectors = clustered(1260, 16, 12, 9);

crates/clawhdf5-bench/src/bin/search_harness.rs

@@ -485,8 +485,12 @@ fn main() {
 
     let mut json = Vec::new();
     println!("## Search harness");
-    for &n in sizes {
+    // `--e2e-only` skips the index benchmarks, so the end-to-end section runs
-        bench_ann(n, &mut json);
+    // in a process that has not already spun up a thread pool.
+    if !args.iter().any(|a| a == "--e2e-only") {
+        for &n in sizes {
+            bench_ann(n, &mut json);
+        }
     }
 
     if ann_only {

scripts/ci-test.sh

@@ -63,6 +63,13 @@ run_step "cargo clippy (format feature matrix)" cargo clippy \
     --features parallel,lz4,zstd,pcodec,fast-checksum \
     -- -D warnings
 
+# The HNSW index's parallel bulk build is feature-gated too.
+run_step "cargo clippy (ann parallel)" cargo clippy \
+    -p clawhdf5-ann \
+    --all-targets \
+    --features parallel \
+    -- -D warnings
+
 # 4. Tests (exclude clawhdf5-py)
 run_step "cargo test" cargo test \
     --workspace \
@@ -72,6 +79,10 @@ run_step "cargo test (format feature matrix)" cargo test \
     -p clawhdf5-format \
     --features parallel,lz4,zstd,pcodec,fast-checksum
 
+run_step "cargo test (ann parallel)" cargo test \
+    -p clawhdf5-ann \
+    --features parallel
+
 # 5. Python interop suites. The h5py writer tests are #[ignore]d so a plain
 #    `cargo test` stays hermetic; run them explicitly here.
 if python3 -c "import h5py" >/dev/null 2>&1 || [ "${CLAWHDF5_REQUIRE_INTEROP:-0}" = "1" ]; then