[DiskBBQ] Replace n_probe, related to the number of centroids with visit_percentage, related to the number of documents

qa/vector/src/main/java/org/elasticsearch/test/knn/CmdLineArgs.java

@@ -36,7 +36,7 @@ record CmdLineArgs(
     KnnIndexTester.IndexType indexType,
     int numCandidates,
     int k,
-    int[] nProbes,
+    double[] visitPercentages,
     int ivfClusterSize,
     int overSamplingFactor,
     int hnswM,
@@ -63,7 +63,8 @@ record CmdLineArgs(
     static final ParseField INDEX_TYPE_FIELD = new ParseField("index_type");
     static final ParseField NUM_CANDIDATES_FIELD = new ParseField("num_candidates");
     static final ParseField K_FIELD = new ParseField("k");
-    static final ParseField N_PROBE_FIELD = new ParseField("n_probe");
+    // static final ParseField N_PROBE_FIELD = new ParseField("n_probe");
+    static final ParseField VISIT_PERCENTAGE_FIELD = new ParseField("visit_percentage");
     static final ParseField IVF_CLUSTER_SIZE_FIELD = new ParseField("ivf_cluster_size");
     static final ParseField OVER_SAMPLING_FACTOR_FIELD = new ParseField("over_sampling_factor");
     static final ParseField HNSW_M_FIELD = new ParseField("hnsw_m");
@@ -97,7 +98,8 @@ static CmdLineArgs fromXContent(XContentParser parser) throws IOException {
         PARSER.declareString(Builder::setIndexType, INDEX_TYPE_FIELD);
         PARSER.declareInt(Builder::setNumCandidates, NUM_CANDIDATES_FIELD);
         PARSER.declareInt(Builder::setK, K_FIELD);
-        PARSER.declareIntArray(Builder::setNProbe, N_PROBE_FIELD);
+        // PARSER.declareIntArray(Builder::setNProbe, N_PROBE_FIELD);
+        PARSER.declareDoubleArray(Builder::setVisitPercentages, VISIT_PERCENTAGE_FIELD);
         PARSER.declareInt(Builder::setIvfClusterSize, IVF_CLUSTER_SIZE_FIELD);
         PARSER.declareInt(Builder::setOverSamplingFactor, OVER_SAMPLING_FACTOR_FIELD);
         PARSER.declareInt(Builder::setHnswM, HNSW_M_FIELD);
@@ -132,7 +134,8 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
         builder.field(INDEX_TYPE_FIELD.getPreferredName(), indexType.name().toLowerCase(Locale.ROOT));
         builder.field(NUM_CANDIDATES_FIELD.getPreferredName(), numCandidates);
         builder.field(K_FIELD.getPreferredName(), k);
-        builder.field(N_PROBE_FIELD.getPreferredName(), nProbes);
+        // builder.field(N_PROBE_FIELD.getPreferredName(), nProbes);
+        builder.field(VISIT_PERCENTAGE_FIELD.getPreferredName(), visitPercentages);
         builder.field(IVF_CLUSTER_SIZE_FIELD.getPreferredName(), ivfClusterSize);
         builder.field(OVER_SAMPLING_FACTOR_FIELD.getPreferredName(), overSamplingFactor);
         builder.field(HNSW_M_FIELD.getPreferredName(), hnswM);
@@ -165,7 +168,7 @@ static class Builder {
         private KnnIndexTester.IndexType indexType = KnnIndexTester.IndexType.HNSW;
         private int numCandidates = 1000;
         private int k = 10;
-        private int[] nProbes = new int[] { 10 };
+        private double[] visitPercentages = new double[] { 1.0 };
         private int ivfClusterSize = 1000;
         private int overSamplingFactor = 1;
         private int hnswM = 16;
@@ -223,8 +226,8 @@ public Builder setK(int k) {
             return this;
         }
 
-        public Builder setNProbe(List<Integer> nProbes) {
-            this.nProbes = nProbes.stream().mapToInt(Integer::intValue).toArray();
+        public Builder setVisitPercentages(List<Double> visitPercentages) {
+            this.visitPercentages = visitPercentages.stream().mapToDouble(Double::doubleValue).toArray();
             return this;
         }
 
@@ -330,7 +333,7 @@ public CmdLineArgs build() {
                 indexType,
                 numCandidates,
                 k,
-                nProbes,
+                visitPercentages,
                 ivfClusterSize,
                 overSamplingFactor,
                 hnswM,

qa/vector/src/main/java/org/elasticsearch/test/knn/KnnIndexTester.java

@@ -191,18 +191,18 @@ public static void main(String[] args) throws Exception {
         FormattedResults formattedResults = new FormattedResults();
 
         for (CmdLineArgs cmdLineArgs : cmdLineArgsList) {
-            int[] nProbes = cmdLineArgs.indexType().equals(IndexType.IVF) && cmdLineArgs.numQueries() > 0
-                ? cmdLineArgs.nProbes()
-                : new int[] { 0 };
+            double[] visitPercentages = cmdLineArgs.indexType().equals(IndexType.IVF) && cmdLineArgs.numQueries() > 0
+                ? cmdLineArgs.visitPercentages()
+                : new double[] { 0 };
             String indexType = cmdLineArgs.indexType().name().toLowerCase(Locale.ROOT);
             Results indexResults = new Results(
                 cmdLineArgs.docVectors().get(0).getFileName().toString(),
                 indexType,
                 cmdLineArgs.numDocs(),
                 cmdLineArgs.filterSelectivity()
             );
-            Results[] results = new Results[nProbes.length];
-            for (int i = 0; i < nProbes.length; i++) {
+            Results[] results = new Results[visitPercentages.length];
+            for (int i = 0; i < visitPercentages.length; i++) {
                 results[i] = new Results(
                     cmdLineArgs.docVectors().get(0).getFileName().toString(),
                     indexType,
@@ -240,8 +240,7 @@ public static void main(String[] args) throws Exception {
             numSegments(indexPath, indexResults);
             if (cmdLineArgs.queryVectors() != null && cmdLineArgs.numQueries() > 0) {
                 for (int i = 0; i < results.length; i++) {
-                    int nProbe = nProbes[i];
-                    KnnSearcher knnSearcher = new KnnSearcher(indexPath, cmdLineArgs, nProbe);
+                    KnnSearcher knnSearcher = new KnnSearcher(indexPath, cmdLineArgs, visitPercentages[i]);
                     knnSearcher.runSearch(results[i], cmdLineArgs.earlyTermination());
                 }
             }
@@ -293,7 +292,7 @@ public String toString() {
             String[] searchHeaders = {
                 "index_name",
                 "index_type",
-                "n_probe",
+                "visit_percentage(%)",
                 "latency(ms)",
                 "net_cpu_time(ms)",
                 "avg_cpu_count",
@@ -324,7 +323,7 @@ public String toString() {
                 queryResultsArray[i] = new String[] {
                     queryResult.indexName,
                     queryResult.indexType,
-                    Integer.toString(queryResult.nProbe),
+                    String.format(Locale.ROOT, "%.2f", queryResult.visitPercentage),
                     String.format(Locale.ROOT, "%.2f", queryResult.avgLatency),
                     String.format(Locale.ROOT, "%.2f", queryResult.netCpuTimeMS),
                     String.format(Locale.ROOT, "%.2f", queryResult.avgCpuCount),
@@ -400,7 +399,7 @@ static class Results {
         long indexTimeMS;
         long forceMergeTimeMS;
         int numSegments;
-        int nProbe;
+        double visitPercentage;
         double avgLatency;
         double qps;
         double avgRecall;

qa/vector/src/main/java/org/elasticsearch/test/knn/KnnSearcher.java

@@ -107,7 +107,7 @@ class KnnSearcher {
     private final float selectivity;
     private final int topK;
     private final int efSearch;
-    private final int nProbe;
+    private final double visitPercentage;
     private final KnnIndexTester.IndexType indexType;
     private int dim;
     private final VectorSimilarityFunction similarityFunction;
@@ -116,7 +116,7 @@ class KnnSearcher {
     private final int searchThreads;
     private final int numSearchers;
 
-    KnnSearcher(Path indexPath, CmdLineArgs cmdLineArgs, int nProbe) {
+    KnnSearcher(Path indexPath, CmdLineArgs cmdLineArgs, double visitPercentage) {
         this.docPath = cmdLineArgs.docVectors();
         this.indexPath = indexPath;
         this.queryPath = cmdLineArgs.queryVectors();
@@ -131,7 +131,7 @@ class KnnSearcher {
             throw new IllegalArgumentException("numQueryVectors must be > 0");
         }
         this.efSearch = cmdLineArgs.numCandidates();
-        this.nProbe = nProbe;
+        this.visitPercentage = visitPercentage;
         this.indexType = cmdLineArgs.indexType();
         this.searchThreads = cmdLineArgs.searchThreads();
         this.numSearchers = cmdLineArgs.numSearchers();
@@ -298,7 +298,7 @@ void runSearch(KnnIndexTester.Results finalResults, boolean earlyTermination) th
         }
         logger.info("checking results");
         int[][] nn = getOrCalculateExactNN(offsetByteSize, filterQuery);
-        finalResults.nProbe = indexType == KnnIndexTester.IndexType.IVF ? nProbe : 0;
+        finalResults.visitPercentage = indexType == KnnIndexTester.IndexType.IVF ? visitPercentage : 0;
         finalResults.avgRecall = checkResults(resultIds, nn, topK);
         finalResults.qps = (1000f * numQueryVectors) / elapsed;
         finalResults.avgLatency = (float) elapsed / numQueryVectors;
@@ -424,7 +424,8 @@ TopDocs doVectorQuery(float[] vector, IndexSearcher searcher, Query filterQuery,
         }
         int efSearch = Math.max(topK, this.efSearch);
         if (indexType == KnnIndexTester.IndexType.IVF) {
-            knnQuery = new IVFKnnFloatVectorQuery(VECTOR_FIELD, vector, topK, efSearch, filterQuery, nProbe);
+            float visitRatio = (float) (visitPercentage / 100);
+            knnQuery = new IVFKnnFloatVectorQuery(VECTOR_FIELD, vector, topK, efSearch, filterQuery, visitRatio);
         } else {
             knnQuery = new ESKnnFloatVectorQuery(
                 VECTOR_FIELD,

server/src/main/java/org/elasticsearch/index/codec/vectors/IVFVectorsFormat.java

@@ -60,8 +60,8 @@ public class IVFVectorsFormat extends KnnVectorsFormat {
     );
 
     // This dynamically sets the cluster probe based on the `k` requested and the number of clusters.
-    // useful when searching with 'efSearch' type parameters instead of requiring a specific nprobe.
-    public static final int DYNAMIC_NPROBE = -1;
+    // useful when searching with 'efSearch' type parameters instead of requiring a specific ratio.
+    public static final float DYNAMIC_VISIT_RATIO = 0.0f;
     public static final int DEFAULT_VECTORS_PER_CLUSTER = 384;
     public static final int MIN_VECTORS_PER_CLUSTER = 64;
     public static final int MAX_VECTORS_PER_CLUSTER = 1 << 16; // 65536

server/src/main/java/org/elasticsearch/index/codec/vectors/IVFVectorsReader.java

@@ -35,7 +35,7 @@
 import java.io.IOException;
 
 import static org.apache.lucene.codecs.lucene99.Lucene99HnswVectorsReader.SIMILARITY_FUNCTIONS;
-import static org.elasticsearch.index.codec.vectors.IVFVectorsFormat.DYNAMIC_NPROBE;
+import static org.elasticsearch.index.codec.vectors.IVFVectorsFormat.DYNAMIC_VISIT_RATIO;
 
 /**
  * Reader for IVF vectors. This reader is used to read the IVF vectors from the index.
@@ -222,34 +222,38 @@ public final void search(String field, float[] target, KnnCollector knnCollector
             percentFiltered = Math.max(0f, Math.min(1f, (float) bitSet.approximateCardinality() / bitSet.length()));
         }
         int numVectors = rawVectorsReader.getFloatVectorValues(field).size();
-        int nProbe = DYNAMIC_NPROBE;
+        float visitRatio = DYNAMIC_VISIT_RATIO;
         // Search strategy may be null if this is being called from checkIndex (e.g. from a test)
         if (knnCollector.getSearchStrategy() instanceof IVFKnnSearchStrategy ivfSearchStrategy) {
-            nProbe = ivfSearchStrategy.getNProbe();
+            visitRatio = ivfSearchStrategy.getVisitRatio();
         }
 
         FieldEntry entry = fields.get(fieldInfo.number);
-        if (nProbe == DYNAMIC_NPROBE) {
+        long maxVectorVisited;
+        if (visitRatio == DYNAMIC_VISIT_RATIO) {
             // empirically based, and a good dynamic to get decent recall while scaling a la "efSearch"
-            // scaling by the number of centroids vs. the nearest neighbors requested
+            // scaling by the number of vectors vs. the nearest neighbors requested
             // not perfect, but a comparative heuristic.
-            // we might want to utilize the total vector count as well, but this is a good start
-            nProbe = (int) Math.round(Math.log10(entry.numCentroids) * Math.sqrt(knnCollector.k()));
-            // clip to be between 1 and the number of centroids
-            nProbe = Math.max(Math.min(nProbe, entry.numCentroids), 1);
+            // TODO: we might want to consider the density of the centroids as experiments shows that for fewer vectors per centroid,
+            // the least vectors we need to score to get a good recall.
+            maxVectorVisited = Math.round(1.75f * Math.log10(knnCollector.k()) * Math.log10(knnCollector.k()) * (knnCollector.k()));
+            // clip so we visit at least one vector
+            maxVectorVisited = Math.max(maxVectorVisited, 1L);
+        } else {
+            // we account for soar vectors here. We can potentially visit a vector twice so we multiply by 2 here.
+            maxVectorVisited = (long) (2.0 * visitRatio * numVectors);
         }
         CentroidIterator centroidIterator = getCentroidIterator(fieldInfo, entry.numCentroids, entry.centroidSlice(ivfCentroids), target);
         PostingVisitor scorer = getPostingVisitor(fieldInfo, entry.postingListSlice(ivfClusters), target, acceptDocs);
-        int centroidsVisited = 0;
+
         long expectedDocs = 0;
         long actualDocs = 0;
         // initially we visit only the "centroids to search"
         // Note, numCollected is doing the bare minimum here.
         // TODO do we need to handle nested doc counts similarly to how we handle
         // filtering? E.g. keep exploring until we hit an expected number of parent documents vs. child vectors?
         while (centroidIterator.hasNext()
-            && (centroidsVisited < nProbe || knnCollector.minCompetitiveSimilarity() == Float.NEGATIVE_INFINITY)) {
-            ++centroidsVisited;
+            && (maxVectorVisited > actualDocs || knnCollector.minCompetitiveSimilarity() == Float.NEGATIVE_INFINITY)) {
             // todo do we actually need to know the score???
             long offset = centroidIterator.nextPostingListOffset();
             // todo do we need direct access to the raw centroid???, this is used for quantizing, maybe hydrating and quantizing

server/src/main/java/org/elasticsearch/index/mapper/vectors/DenseVectorFieldMapper.java

@@ -1697,18 +1697,22 @@ public DenseVectorIndexOptions parseIndexOptions(String fieldName, Map<String, ?
                 if (rescoreVector == null) {
                     rescoreVector = new RescoreVector(DEFAULT_OVERSAMPLE);
                 }
-                Object nProbeNode = indexOptionsMap.remove("default_n_probe");
-                int nProbe = -1;
-                if (nProbeNode != null) {
-                    nProbe = XContentMapValues.nodeIntegerValue(nProbeNode);
-                    if (nProbe < 1 && nProbe != -1) {
+                Object visitPercentageNode = indexOptionsMap.remove("default_visit_percentage");
+                double visitPercentage = 0d;
+                if (visitPercentageNode != null) {
+                    visitPercentage = (float) XContentMapValues.nodeDoubleValue(visitPercentageNode);
+                    if (visitPercentage < 0d || visitPercentage > 100d) {
                         throw new IllegalArgumentException(
-                            "default_n_probe must be at least 1 or exactly -1, got: " + nProbe + " for field [" + fieldName + "]"
+                            "default_visit_percentage must be between 0.0 and 100.0, got: "
+                                + visitPercentage
+                                + " for field ["
+                                + fieldName
+                                + "]"
                         );
                     }
                 }
                 MappingParser.checkNoRemainingFields(fieldName, indexOptionsMap);
-                return new BBQIVFIndexOptions(clusterSize, nProbe, rescoreVector);
+                return new BBQIVFIndexOptions(clusterSize, visitPercentage, rescoreVector);
             }
 
             @Override
@@ -2301,12 +2305,12 @@ public boolean validateDimension(int dim, boolean throwOnError) {
 
     static class BBQIVFIndexOptions extends QuantizedIndexOptions {
         final int clusterSize;
-        final int defaultNProbe;
+        final double defaultVisitPercentage;
 
-        BBQIVFIndexOptions(int clusterSize, int defaultNProbe, RescoreVector rescoreVector) {
+        BBQIVFIndexOptions(int clusterSize, double defaultVisitPercentage, RescoreVector rescoreVector) {
             super(VectorIndexType.BBQ_DISK, rescoreVector);
             this.clusterSize = clusterSize;
-            this.defaultNProbe = defaultNProbe;
+            this.defaultVisitPercentage = defaultVisitPercentage;
         }
 
         @Override
@@ -2324,13 +2328,13 @@ public boolean updatableTo(DenseVectorIndexOptions update) {
         boolean doEquals(DenseVectorIndexOptions other) {
             BBQIVFIndexOptions that = (BBQIVFIndexOptions) other;
             return clusterSize == that.clusterSize
-                && defaultNProbe == that.defaultNProbe
+                && defaultVisitPercentage == that.defaultVisitPercentage
                 && Objects.equals(rescoreVector, that.rescoreVector);
         }
 
         @Override
         int doHashCode() {
-            return Objects.hash(clusterSize, defaultNProbe, rescoreVector);
+            return Objects.hash(clusterSize, defaultVisitPercentage, rescoreVector);
         }
 
         @Override
@@ -2343,7 +2347,7 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
             builder.startObject();
             builder.field("type", type);
             builder.field("cluster_size", clusterSize);
-            builder.field("default_n_probe", defaultNProbe);
+            builder.field("default_visit_percentage", defaultVisitPercentage);
             if (rescoreVector != null) {
                 rescoreVector.toXContent(builder, params);
             }
@@ -2740,6 +2744,7 @@ && isNotUnitVector(squaredMagnitude)) {
                         .add(filter, BooleanClause.Occur.FILTER)
                         .build();
             } else if (indexOptions instanceof BBQIVFIndexOptions bbqIndexOptions) {
+                float defaultVisitRatio = (float) (bbqIndexOptions.defaultVisitPercentage / 100d);
                 knnQuery = parentFilter != null
                     ? new DiversifyingChildrenIVFKnnFloatVectorQuery(
                         name(),
@@ -2748,9 +2753,9 @@ && isNotUnitVector(squaredMagnitude)) {
                         numCands,
                         filter,
                         parentFilter,
-                        bbqIndexOptions.defaultNProbe
+                        defaultVisitRatio
                     )
-                    : new IVFKnnFloatVectorQuery(name(), queryVector, adjustedK, numCands, filter, bbqIndexOptions.defaultNProbe);
+                    : new IVFKnnFloatVectorQuery(name(), queryVector, adjustedK, numCands, filter, defaultVisitRatio);
             } else {
                 knnQuery = parentFilter != null
                     ? new ESDiversifyingChildrenFloatKnnVectorQuery(