dungba88 commented on code in PR #14226: URL: https://github.com/apache/lucene/pull/14226#discussion_r1960939250
########## lucene/core/src/java/org/apache/lucene/search/OptimisticKnnVectorQuery.java: ########## @@ -0,0 +1,205 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.lucene.search; + +import java.io.IOException; +import java.util.ArrayList; +import java.util.HashMap; +import java.util.Iterator; +import java.util.List; +import java.util.Map; +import java.util.concurrent.Callable; +import org.apache.lucene.codecs.lucene90.IndexedDISI; +import org.apache.lucene.index.IndexReader; +import org.apache.lucene.index.KnnVectorValues; +import org.apache.lucene.index.LeafReaderContext; +import org.apache.lucene.search.knn.KnnCollectorManager; +import org.apache.lucene.search.knn.KnnSearchStrategy; + +/** + * Like {@link KnnFloatVectorQuery} but makes an optimistic assumption about the distribution of + * documents among segments: namely that they are uniform-random w.r.t. vector distance. This is + * unsafe, so it checks the assumption after running the queries and runs a second pass if needed in + * any segments for which the assumption proves to be false. The check is simple: is the worst hit + * in the result queue for a segment in the global top K? If so, explore that segment further using + * seeded KNN search query, seeding with the initial results. + */ +// TODO: rename as float? move methods to AbstractKnnVectorQuery? make a Strategy? Replace existing +// collection strategy? +// yes, I think we should merge this stuff w/AbstractKnnVectorQuery, enable it with a +// KnnSearchStrategy, +// and extend KnnFloatVectorQuery/KnnByteVectorQuery in a simple way +public class OptimisticKnnVectorQuery extends KnnFloatVectorQuery { + + public OptimisticKnnVectorQuery(String field, float[] target, int k, Query filter) { + super(field, target, k, filter); + } + + public OptimisticKnnVectorQuery(String field, float[] target, int k) { + super(field, target, k, null); + } + + @Override + public Query rewrite(IndexSearcher indexSearcher) throws IOException { + IndexReader reader = indexSearcher.getIndexReader(); + + final Weight filterWeight; + if (filter != null) { + BooleanQuery booleanQuery = + new BooleanQuery.Builder() + .add(filter, BooleanClause.Occur.FILTER) + .add(new FieldExistsQuery(field), BooleanClause.Occur.FILTER) + .build(); + Query rewritten = indexSearcher.rewrite(booleanQuery); + filterWeight = indexSearcher.createWeight(rewritten, ScoreMode.COMPLETE_NO_SCORES, 1f); + } else { + filterWeight = null; + } + + TimeLimitingKnnCollectorManager knnCollectorManager = + new TimeLimitingKnnCollectorManager( + getKnnCollectorManager(k, indexSearcher), indexSearcher.getTimeout()); + TaskExecutor taskExecutor = indexSearcher.getTaskExecutor(); + List<LeafReaderContext> leafReaderContexts = new ArrayList<>(reader.leaves()); + List<Callable<TopDocs>> tasks = new ArrayList<>(leafReaderContexts.size()); + for (LeafReaderContext context : leafReaderContexts) { + tasks.add(() -> searchLeaf(context, filterWeight, knnCollectorManager)); + } + TopDocs topK = null; + Map<Integer, TopDocs> perLeafResults = new HashMap<>(leafReaderContexts.size()); + int kInLoop = k; + while (tasks.isEmpty() == false) { + List<TopDocs> taskResults = taskExecutor.invokeAll(tasks); + for (int i = 0; i < taskResults.size(); i++) { + perLeafResults.put(leafReaderContexts.get(i).ord, taskResults.get(i)); + } + tasks.clear(); + // Merge sort the results + topK = mergeLeafResults(perLeafResults.values().toArray(TopDocs[]::new)); + if (topK.scoreDocs.length == 0 || perLeafResults.size() <= 1) { + break; + } + float minTopKScore = topK.scoreDocs[topK.scoreDocs.length - 1].score; + kInLoop *= 2; + TimeLimitingKnnCollectorManager knnCollectorManagerInner = + new TimeLimitingKnnCollectorManager( + new ReentrantKnnCollectorManager( + getKnnCollectorManager(kInLoop, indexSearcher), perLeafResults), + indexSearcher.getTimeout()); + // System.out.println("k=" + k + " kloop=" + kInLoop); + Iterator<LeafReaderContext> ctxIter = leafReaderContexts.iterator(); + while (ctxIter.hasNext()) { + LeafReaderContext ctx = ctxIter.next(); + TopDocs perLeaf = perLeafResults.get(ctx.ord); + if (perLeaf.scoreDocs.length > 0 Review Comment: Can we publish the metrics for each case here to see how often/how many passes we usually need to take? -- This is an automated message from the Apache Git Service. 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