jimczi commented on code in PR #12582: URL: https://github.com/apache/lucene/pull/12582#discussion_r1343896112
########## lucene/core/src/java/org/apache/lucene/codecs/lucene99/Lucene99HnswVectorsWriter.java: ########## @@ -0,0 +1,1170 @@ +/* + * 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.codecs.lucene99; + +import static org.apache.lucene.codecs.lucene99.Lucene99HnswVectorsFormat.DIRECT_MONOTONIC_BLOCK_SHIFT; +import static org.apache.lucene.codecs.lucene99.Lucene99ScalarQuantizedVectorsFormat.calculateDefaultQuantile; +import static org.apache.lucene.search.DocIdSetIterator.NO_MORE_DOCS; + +import java.io.IOException; +import java.nio.ByteBuffer; +import java.nio.ByteOrder; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import org.apache.lucene.codecs.CodecUtil; +import org.apache.lucene.codecs.HnswGraphProvider; +import org.apache.lucene.codecs.KnnFieldVectorsWriter; +import org.apache.lucene.codecs.KnnVectorsReader; +import org.apache.lucene.codecs.KnnVectorsWriter; +import org.apache.lucene.codecs.lucene90.IndexedDISI; +import org.apache.lucene.codecs.perfield.PerFieldKnnVectorsFormat; +import org.apache.lucene.index.ByteVectorValues; +import org.apache.lucene.index.DocsWithFieldSet; +import org.apache.lucene.index.FieldInfo; +import org.apache.lucene.index.FloatVectorValues; +import org.apache.lucene.index.IndexFileNames; +import org.apache.lucene.index.MergeState; +import org.apache.lucene.index.SegmentWriteState; +import org.apache.lucene.index.Sorter; +import org.apache.lucene.index.VectorEncoding; +import org.apache.lucene.search.DocIdSetIterator; +import org.apache.lucene.store.IndexInput; +import org.apache.lucene.store.IndexOutput; +import org.apache.lucene.util.ArrayUtil; +import org.apache.lucene.util.Bits; +import org.apache.lucene.util.IOUtils; +import org.apache.lucene.util.InfoStream; +import org.apache.lucene.util.RamUsageEstimator; +import org.apache.lucene.util.hnsw.HnswGraph; +import org.apache.lucene.util.hnsw.HnswGraph.NodesIterator; +import org.apache.lucene.util.hnsw.HnswGraphBuilder; +import org.apache.lucene.util.hnsw.NeighborArray; +import org.apache.lucene.util.hnsw.OnHeapHnswGraph; +import org.apache.lucene.util.hnsw.RandomAccessVectorValues; +import org.apache.lucene.util.hnsw.RandomVectorScorerSupplier; +import org.apache.lucene.util.packed.DirectMonotonicWriter; + +/** + * Writes vector values and knn graphs to index segments. + * + * @lucene.experimental + */ +public final class Lucene99HnswVectorsWriter extends KnnVectorsWriter { + + private final SegmentWriteState segmentWriteState; + private final IndexOutput meta, vectorData, quantizedVectorData, vectorIndex; + private final int M; + private final int beamWidth; + private final Lucene99ScalarQuantizedVectorsWriter quantizedVectorsWriter; + + private final List<FieldWriter<?>> fields = new ArrayList<>(); + private boolean finished; + + Lucene99HnswVectorsWriter( + SegmentWriteState state, + int M, + int beamWidth, + Lucene99ScalarQuantizedVectorsFormat quantizedVectorsFormat) + throws IOException { + this.M = M; + this.beamWidth = beamWidth; + segmentWriteState = state; + String metaFileName = + IndexFileNames.segmentFileName( + state.segmentInfo.name, state.segmentSuffix, Lucene99HnswVectorsFormat.META_EXTENSION); + + String vectorDataFileName = + IndexFileNames.segmentFileName( + state.segmentInfo.name, + state.segmentSuffix, + Lucene99HnswVectorsFormat.VECTOR_DATA_EXTENSION); + + String indexDataFileName = + IndexFileNames.segmentFileName( + state.segmentInfo.name, + state.segmentSuffix, + Lucene99HnswVectorsFormat.VECTOR_INDEX_EXTENSION); + + final String quantizedVectorDataFileName = + quantizedVectorsFormat != null + ? IndexFileNames.segmentFileName( + state.segmentInfo.name, + state.segmentSuffix, + Lucene99ScalarQuantizedVectorsFormat.QUANTIZED_VECTOR_DATA_EXTENSION) + : null; + boolean success = false; + try { + meta = state.directory.createOutput(metaFileName, state.context); + vectorData = state.directory.createOutput(vectorDataFileName, state.context); + vectorIndex = state.directory.createOutput(indexDataFileName, state.context); + + CodecUtil.writeIndexHeader( + meta, + Lucene99HnswVectorsFormat.META_CODEC_NAME, + Lucene99HnswVectorsFormat.VERSION_CURRENT, + state.segmentInfo.getId(), + state.segmentSuffix); + CodecUtil.writeIndexHeader( + vectorData, + Lucene99HnswVectorsFormat.VECTOR_DATA_CODEC_NAME, + Lucene99HnswVectorsFormat.VERSION_CURRENT, + state.segmentInfo.getId(), + state.segmentSuffix); + CodecUtil.writeIndexHeader( + vectorIndex, + Lucene99HnswVectorsFormat.VECTOR_INDEX_CODEC_NAME, + Lucene99HnswVectorsFormat.VERSION_CURRENT, + state.segmentInfo.getId(), + state.segmentSuffix); + if (quantizedVectorDataFileName != null) { + quantizedVectorData = + state.directory.createOutput(quantizedVectorDataFileName, state.context); + CodecUtil.writeIndexHeader( + quantizedVectorData, + Lucene99ScalarQuantizedVectorsFormat.QUANTIZED_VECTOR_DATA_CODEC_NAME, + Lucene99ScalarQuantizedVectorsFormat.VERSION_CURRENT, + state.segmentInfo.getId(), + state.segmentSuffix); + quantizedVectorsWriter = + new Lucene99ScalarQuantizedVectorsWriter( + quantizedVectorData, quantizedVectorsFormat.quantile); + } else { + quantizedVectorData = null; + quantizedVectorsWriter = null; + } + success = true; + } finally { + if (success == false) { + IOUtils.closeWhileHandlingException(this); + } + } + } + + @Override + public KnnFieldVectorsWriter<?> addField(FieldInfo fieldInfo) throws IOException { + Lucene99ScalarQuantizedVectorsWriter.QuantizationVectorWriter quantizedVectorFieldWriter = null; + // Quantization only supports FLOAT32 for now + if (quantizedVectorsWriter != null + && fieldInfo.getVectorEncoding().equals(VectorEncoding.FLOAT32)) { + quantizedVectorFieldWriter = quantizedVectorsWriter.addField(fieldInfo); + } + FieldWriter<?> newField = + FieldWriter.create( + fieldInfo, M, beamWidth, segmentWriteState.infoStream, quantizedVectorFieldWriter); + fields.add(newField); + return newField; + } + + @Override + public void flush(int maxDoc, Sorter.DocMap sortMap) throws IOException { + for (FieldWriter<?> field : fields) { + long[] quantizedVectorOffsetAndLen = null; + if (field.quantizedWriter != null) { + assert quantizedVectorsWriter != null; + quantizedVectorOffsetAndLen = + quantizedVectorsWriter.flush(sortMap, field.quantizedWriter, field.docsWithField); + } + if (sortMap == null) { + writeField(field, maxDoc, quantizedVectorOffsetAndLen); + } else { + writeSortingField(field, maxDoc, sortMap, quantizedVectorOffsetAndLen); + } + } + } + + @Override + public void finish() throws IOException { + if (finished) { + throw new IllegalStateException("already finished"); + } + finished = true; + if (quantizedVectorsWriter != null) { + quantizedVectorsWriter.finish(); + } + + if (meta != null) { + // write end of fields marker + meta.writeInt(-1); + CodecUtil.writeFooter(meta); + } + if (vectorData != null) { + CodecUtil.writeFooter(vectorData); + CodecUtil.writeFooter(vectorIndex); + } + } + + @Override + public long ramBytesUsed() { + long total = 0; + for (FieldWriter<?> field : fields) { + total += field.ramBytesUsed(); + } + return total; + } + + private void writeField(FieldWriter<?> fieldData, int maxDoc, long[] quantizedVecOffsetAndLen) + throws IOException { + // write vector values + long vectorDataOffset = vectorData.alignFilePointer(Float.BYTES); + switch (fieldData.fieldInfo.getVectorEncoding()) { + case BYTE -> writeByteVectors(fieldData); + case FLOAT32 -> writeFloat32Vectors(fieldData); + } + long vectorDataLength = vectorData.getFilePointer() - vectorDataOffset; + + // write graph + long vectorIndexOffset = vectorIndex.getFilePointer(); + OnHeapHnswGraph graph = fieldData.getGraph(); + int[][] graphLevelNodeOffsets = writeGraph(graph); + long vectorIndexLength = vectorIndex.getFilePointer() - vectorIndexOffset; + + writeMeta( + fieldData.isQuantized(), + fieldData.fieldInfo, + maxDoc, + fieldData.getConfiguredQuantile(), + fieldData.getMinQuantile(), + fieldData.getMaxQuantile(), + quantizedVecOffsetAndLen, + vectorDataOffset, + vectorDataLength, + vectorIndexOffset, + vectorIndexLength, + fieldData.docsWithField, + graph, + graphLevelNodeOffsets); + } + + private void writeFloat32Vectors(FieldWriter<?> fieldData) throws IOException { + final ByteBuffer buffer = + ByteBuffer.allocate(fieldData.dim * Float.BYTES).order(ByteOrder.LITTLE_ENDIAN); + for (Object v : fieldData.vectors) { + buffer.asFloatBuffer().put((float[]) v); + vectorData.writeBytes(buffer.array(), buffer.array().length); + } + } + + private void writeByteVectors(FieldWriter<?> fieldData) throws IOException { + for (Object v : fieldData.vectors) { + byte[] vector = (byte[]) v; + vectorData.writeBytes(vector, vector.length); + } + } + + private void writeSortingField( + FieldWriter<?> fieldData, + int maxDoc, + Sorter.DocMap sortMap, + long[] quantizedVectorOffsetAndLen) + throws IOException { + final int[] docIdOffsets = new int[sortMap.size()]; + int offset = 1; // 0 means no vector for this (field, document) + DocIdSetIterator iterator = fieldData.docsWithField.iterator(); + for (int docID = iterator.nextDoc(); + docID != DocIdSetIterator.NO_MORE_DOCS; + docID = iterator.nextDoc()) { + int newDocID = sortMap.oldToNew(docID); + docIdOffsets[newDocID] = offset++; + } + DocsWithFieldSet newDocsWithField = new DocsWithFieldSet(); + final int[] ordMap = new int[offset - 1]; // new ord to old ord + final int[] oldOrdMap = new int[offset - 1]; // old ord to new ord + int ord = 0; + int doc = 0; + for (int docIdOffset : docIdOffsets) { + if (docIdOffset != 0) { + ordMap[ord] = docIdOffset - 1; + oldOrdMap[docIdOffset - 1] = ord; + newDocsWithField.add(doc); + ord++; + } + doc++; + } + + // write vector values + long vectorDataOffset = + switch (fieldData.fieldInfo.getVectorEncoding()) { + case BYTE -> writeSortedByteVectors(fieldData, ordMap); + case FLOAT32 -> writeSortedFloat32Vectors(fieldData, ordMap); + }; + long vectorDataLength = vectorData.getFilePointer() - vectorDataOffset; + + // write graph + long vectorIndexOffset = vectorIndex.getFilePointer(); + OnHeapHnswGraph graph = fieldData.getGraph(); + int[][] graphLevelNodeOffsets = graph == null ? new int[0][] : new int[graph.numLevels()][]; + HnswGraph mockGraph = reconstructAndWriteGraph(graph, ordMap, oldOrdMap, graphLevelNodeOffsets); + long vectorIndexLength = vectorIndex.getFilePointer() - vectorIndexOffset; + + writeMeta( + fieldData.isQuantized(), + fieldData.fieldInfo, + maxDoc, + fieldData.getConfiguredQuantile(), + fieldData.getMinQuantile(), + fieldData.getMaxQuantile(), + quantizedVectorOffsetAndLen, + vectorDataOffset, + vectorDataLength, + vectorIndexOffset, + vectorIndexLength, + newDocsWithField, + mockGraph, + graphLevelNodeOffsets); + } + + private long writeSortedFloat32Vectors(FieldWriter<?> fieldData, int[] ordMap) + throws IOException { + long vectorDataOffset = vectorData.alignFilePointer(Float.BYTES); + final ByteBuffer buffer = + ByteBuffer.allocate(fieldData.dim * Float.BYTES).order(ByteOrder.LITTLE_ENDIAN); + for (int ordinal : ordMap) { + float[] vector = (float[]) fieldData.vectors.get(ordinal); + buffer.asFloatBuffer().put(vector); + vectorData.writeBytes(buffer.array(), buffer.array().length); + } + return vectorDataOffset; + } + + private long writeSortedByteVectors(FieldWriter<?> fieldData, int[] ordMap) throws IOException { + long vectorDataOffset = vectorData.alignFilePointer(Float.BYTES); + for (int ordinal : ordMap) { + byte[] vector = (byte[]) fieldData.vectors.get(ordinal); + vectorData.writeBytes(vector, vector.length); + } + return vectorDataOffset; + } + + /** + * Reconstructs the graph given the old and new node ids. + * + * <p>Additionally, the graph node connections are written to the vectorIndex. + * + * @param graph The current on heap graph + * @param newToOldMap the new node ids indexed to the old node ids + * @param oldToNewMap the old node ids indexed to the new node ids + * @param levelNodeOffsets where to place the new offsets for the nodes in the vector index. + * @return The graph + * @throws IOException if writing to vectorIndex fails + */ + private HnswGraph reconstructAndWriteGraph( + OnHeapHnswGraph graph, int[] newToOldMap, int[] oldToNewMap, int[][] levelNodeOffsets) + throws IOException { + if (graph == null) return null; + + List<int[]> nodesByLevel = new ArrayList<>(graph.numLevels()); + nodesByLevel.add(null); + + int maxOrd = graph.size(); + NodesIterator nodesOnLevel0 = graph.getNodesOnLevel(0); + levelNodeOffsets[0] = new int[nodesOnLevel0.size()]; + while (nodesOnLevel0.hasNext()) { + int node = nodesOnLevel0.nextInt(); + NeighborArray neighbors = graph.getNeighbors(0, newToOldMap[node]); + long offset = vectorIndex.getFilePointer(); + reconstructAndWriteNeigbours(neighbors, oldToNewMap, maxOrd); + levelNodeOffsets[0][node] = Math.toIntExact(vectorIndex.getFilePointer() - offset); + } + + for (int level = 1; level < graph.numLevels(); level++) { + NodesIterator nodesOnLevel = graph.getNodesOnLevel(level); + int[] newNodes = new int[nodesOnLevel.size()]; + for (int n = 0; nodesOnLevel.hasNext(); n++) { + newNodes[n] = oldToNewMap[nodesOnLevel.nextInt()]; + } + Arrays.sort(newNodes); + nodesByLevel.add(newNodes); + levelNodeOffsets[level] = new int[newNodes.length]; + int nodeOffsetIndex = 0; + for (int node : newNodes) { + NeighborArray neighbors = graph.getNeighbors(level, newToOldMap[node]); + long offset = vectorIndex.getFilePointer(); + reconstructAndWriteNeigbours(neighbors, oldToNewMap, maxOrd); + levelNodeOffsets[level][nodeOffsetIndex++] = + Math.toIntExact(vectorIndex.getFilePointer() - offset); + } + } + return new HnswGraph() { + @Override + public int nextNeighbor() { + throw new UnsupportedOperationException("Not supported on a mock graph"); + } + + @Override + public void seek(int level, int target) { + throw new UnsupportedOperationException("Not supported on a mock graph"); + } + + @Override + public int size() { + return graph.size(); + } + + @Override + public int numLevels() { + return graph.numLevels(); + } + + @Override + public int entryNode() { + throw new UnsupportedOperationException("Not supported on a mock graph"); + } + + @Override + public NodesIterator getNodesOnLevel(int level) { + if (level == 0) { + return graph.getNodesOnLevel(0); + } else { + return new ArrayNodesIterator(nodesByLevel.get(level), nodesByLevel.get(level).length); + } + } + }; + } + + private void reconstructAndWriteNeigbours(NeighborArray neighbors, int[] oldToNewMap, int maxOrd) + throws IOException { + int size = neighbors.size(); + vectorIndex.writeVInt(size); + + // Destructively modify; it's ok we are discarding it after this + int[] nnodes = neighbors.node(); + for (int i = 0; i < size; i++) { + nnodes[i] = oldToNewMap[nnodes[i]]; + } + Arrays.sort(nnodes, 0, size); + // Now that we have sorted, do delta encoding to minimize the required bits to store the + // information + for (int i = size - 1; i > 0; --i) { + assert nnodes[i] < maxOrd : "node too large: " + nnodes[i] + ">=" + maxOrd; + nnodes[i] -= nnodes[i - 1]; + } + for (int i = 0; i < size; i++) { + vectorIndex.writeVInt(nnodes[i]); + } + } + + @Override + public void mergeOneField(FieldInfo fieldInfo, MergeState mergeState) throws IOException { + long vectorDataOffset = vectorData.alignFilePointer(Float.BYTES); + IndexOutput tempVectorData = + segmentWriteState.directory.createTempOutput( + vectorData.getName(), "temp", segmentWriteState.context); + IndexInput vectorDataInput = null; + IndexInput quantizationDataInput = null; + Lucene99ScalarQuantizedVectorsWriter.MergedQuantileState quantizationState = null; + boolean success = false; + try { + long[] quantizedVectorDataOffsetAndLength = null; + if (quantizedVectorsWriter != null) { + quantizationState = + quantizedVectorsWriter.mergeOneField(segmentWriteState, fieldInfo, mergeState); + if (quantizationState != null) { + quantizedVectorDataOffsetAndLength = new long[2]; + quantizedVectorDataOffsetAndLength[0] = quantizedVectorData.alignFilePointer(Float.BYTES); + quantizationDataInput = + segmentWriteState.directory.openInput( + quantizationState.tempVectorFileName, segmentWriteState.context); + quantizedVectorData.copyBytes( + quantizationDataInput, quantizationDataInput.length() - CodecUtil.footerLength()); + quantizedVectorDataOffsetAndLength[1] = + quantizedVectorData.getFilePointer() - quantizedVectorDataOffsetAndLength[0]; + CodecUtil.retrieveChecksum(quantizationDataInput); + } + } + // write the vector data to a temporary file + DocsWithFieldSet docsWithField = + switch (fieldInfo.getVectorEncoding()) { + case BYTE -> writeByteVectorData( + tempVectorData, MergedVectorValues.mergeByteVectorValues(fieldInfo, mergeState)); + case FLOAT32 -> writeVectorData( + tempVectorData, MergedVectorValues.mergeFloatVectorValues(fieldInfo, mergeState)); + }; + CodecUtil.writeFooter(tempVectorData); + IOUtils.close(tempVectorData); + + // copy the temporary file vectors to the actual data file + vectorDataInput = + segmentWriteState.directory.openInput( + tempVectorData.getName(), segmentWriteState.context); + vectorData.copyBytes(vectorDataInput, vectorDataInput.length() - CodecUtil.footerLength()); + CodecUtil.retrieveChecksum(vectorDataInput); + long vectorDataLength = vectorData.getFilePointer() - vectorDataOffset; + long vectorIndexOffset = vectorIndex.getFilePointer(); + // build the graph using the temporary vector data + // we use Lucene99HnswVectorsReader.DenseOffHeapVectorValues for the graph construction + // doesn't need to know docIds + // TODO: separate random access vector values from DocIdSetIterator? + int byteSize = fieldInfo.getVectorDimension() * fieldInfo.getVectorEncoding().byteSize; + OnHeapHnswGraph graph = null; + int[][] vectorIndexNodeOffsets = null; + if (docsWithField.cardinality() != 0) { + // build graph + int initializerIndex = selectGraphForInitialization(mergeState, fieldInfo); + graph = + switch (fieldInfo.getVectorEncoding()) { + case BYTE -> { + OffHeapByteVectorValues.DenseOffHeapVectorValues vectorValues = + new OffHeapByteVectorValues.DenseOffHeapVectorValues( + fieldInfo.getVectorDimension(), + docsWithField.cardinality(), + vectorDataInput, + byteSize); + RandomVectorScorerSupplier scorerSupplier = + RandomVectorScorerSupplier.createBytes( + vectorValues, fieldInfo.getVectorSimilarityFunction()); + HnswGraphBuilder hnswGraphBuilder = + createHnswGraphBuilder(mergeState, fieldInfo, scorerSupplier, initializerIndex); + hnswGraphBuilder.setInfoStream(segmentWriteState.infoStream); + yield hnswGraphBuilder.build(vectorValues.size()); + } + case FLOAT32 -> { + final int maxOrd; + final RandomVectorScorerSupplier scorerSupplier; + if (quantizationState != null) { + RandomAccessQuantizedByteVectorValues values = + new OffHeapQuantizedByteVectorValues.DenseOffHeapVectorValues( + fieldInfo.getVectorDimension(), + docsWithField.cardinality(), + quantizationDataInput); + maxOrd = values.size(); + scorerSupplier = + new ScalarQuantizedRandomVectorScorerSupplier( + fieldInfo.getVectorSimilarityFunction(), + quantizationState.mergeQuantile, + values); + } else { + OffHeapFloatVectorValues.DenseOffHeapVectorValues vectorValues = + new OffHeapFloatVectorValues.DenseOffHeapVectorValues( + fieldInfo.getVectorDimension(), + docsWithField.cardinality(), + vectorDataInput, + byteSize); + maxOrd = vectorValues.size(); + scorerSupplier = + RandomVectorScorerSupplier.createFloats( + vectorValues, fieldInfo.getVectorSimilarityFunction()); + } + HnswGraphBuilder hnswGraphBuilder = + createHnswGraphBuilder(mergeState, fieldInfo, scorerSupplier, initializerIndex); + hnswGraphBuilder.setInfoStream(segmentWriteState.infoStream); + yield hnswGraphBuilder.build(maxOrd); + } + }; + vectorIndexNodeOffsets = writeGraph(graph); + } + long vectorIndexLength = vectorIndex.getFilePointer() - vectorIndexOffset; + writeMeta( + quantizationState != null, + fieldInfo, + segmentWriteState.segmentInfo.maxDoc(), + quantizationState == null ? null : quantizationState.configuredQuantile, + quantizationState == null ? null : quantizationState.mergeQuantile.getLowerQuantile(), + quantizationState == null ? null : quantizationState.mergeQuantile.getUpperQuantile(), + quantizedVectorDataOffsetAndLength, + vectorDataOffset, + vectorDataLength, + vectorIndexOffset, + vectorIndexLength, + docsWithField, + graph, + vectorIndexNodeOffsets); + success = true; + } finally { + IOUtils.close(vectorDataInput, quantizationDataInput); + if (success) { + segmentWriteState.directory.deleteFile(tempVectorData.getName()); + if (quantizationState != null) { + segmentWriteState.directory.deleteFile(quantizationState.tempVectorFileName); + } + } else { + IOUtils.closeWhileHandlingException(tempVectorData); + IOUtils.deleteFilesIgnoringExceptions( + segmentWriteState.directory, tempVectorData.getName()); + } + } + } + + private HnswGraphBuilder createHnswGraphBuilder( + MergeState mergeState, + FieldInfo fieldInfo, + RandomVectorScorerSupplier scorerSupplier, + int initializerIndex) + throws IOException { + if (initializerIndex == -1) { + return HnswGraphBuilder.create(scorerSupplier, M, beamWidth, HnswGraphBuilder.randSeed); + } + + HnswGraph initializerGraph = + getHnswGraphFromReader(fieldInfo.name, mergeState.knnVectorsReaders[initializerIndex]); + Map<Integer, Integer> ordinalMapper = + getOldToNewOrdinalMap(mergeState, fieldInfo, initializerIndex); + return HnswGraphBuilder.create( + scorerSupplier, M, beamWidth, HnswGraphBuilder.randSeed, initializerGraph, ordinalMapper); + } + + private int selectGraphForInitialization(MergeState mergeState, FieldInfo fieldInfo) + throws IOException { + // Find the KnnVectorReader with the most docs that meets the following criteria: + // 1. Does not contain any deleted docs + // 2. Is a Lucene99HnswVectorsReader/PerFieldKnnVectorReader + // If no readers exist that meet this criteria, return -1. If they do, return their index in + // merge state + int maxCandidateVectorCount = 0; + int initializerIndex = -1; + + for (int i = 0; i < mergeState.liveDocs.length; i++) { + KnnVectorsReader currKnnVectorsReader = mergeState.knnVectorsReaders[i]; + if (mergeState.knnVectorsReaders[i] + instanceof PerFieldKnnVectorsFormat.FieldsReader candidateReader) { + currKnnVectorsReader = candidateReader.getFieldReader(fieldInfo.name); + } + + if (!allMatch(mergeState.liveDocs[i]) + || !(currKnnVectorsReader instanceof HnswGraphProvider)) { + continue; + } + + int candidateVectorCount = 0; + switch (fieldInfo.getVectorEncoding()) { + case BYTE -> { + ByteVectorValues byteVectorValues = + currKnnVectorsReader.getByteVectorValues(fieldInfo.name); + if (byteVectorValues == null) { + continue; + } + candidateVectorCount = byteVectorValues.size(); + } + case FLOAT32 -> { + FloatVectorValues vectorValues = + currKnnVectorsReader.getFloatVectorValues(fieldInfo.name); + if (vectorValues == null) { + continue; + } + candidateVectorCount = vectorValues.size(); + } + } + + if (candidateVectorCount > maxCandidateVectorCount) { + maxCandidateVectorCount = candidateVectorCount; + initializerIndex = i; + } + } + return initializerIndex; + } + + private HnswGraph getHnswGraphFromReader(String fieldName, KnnVectorsReader knnVectorsReader) + throws IOException { + if (knnVectorsReader instanceof PerFieldKnnVectorsFormat.FieldsReader perFieldReader + && perFieldReader.getFieldReader(fieldName) instanceof HnswGraphProvider fieldReader) { + return fieldReader.getGraph(fieldName); + } + + if (knnVectorsReader instanceof HnswGraphProvider) { + return ((HnswGraphProvider) knnVectorsReader).getGraph(fieldName); + } + + // We should not reach here because knnVectorsReader's type is checked in + // selectGraphForInitialization + throw new IllegalArgumentException( + "Invalid KnnVectorsReader type for field: " + + fieldName + + ". Must be Lucene99HnswVectorsReader or newer"); + } + + private Map<Integer, Integer> getOldToNewOrdinalMap( + MergeState mergeState, FieldInfo fieldInfo, int initializerIndex) throws IOException { + + DocIdSetIterator initializerIterator = null; + + switch (fieldInfo.getVectorEncoding()) { + case BYTE -> initializerIterator = + mergeState.knnVectorsReaders[initializerIndex].getByteVectorValues(fieldInfo.name); + case FLOAT32 -> initializerIterator = + mergeState.knnVectorsReaders[initializerIndex].getFloatVectorValues(fieldInfo.name); + } + + MergeState.DocMap initializerDocMap = mergeState.docMaps[initializerIndex]; + + Map<Integer, Integer> newIdToOldOrdinal = new HashMap<>(); + int oldOrd = 0; + int maxNewDocID = -1; + for (int oldId = initializerIterator.nextDoc(); + oldId != NO_MORE_DOCS; + oldId = initializerIterator.nextDoc()) { + if (isCurrentVectorNull(initializerIterator)) { + continue; + } + int newId = initializerDocMap.get(oldId); + maxNewDocID = Math.max(newId, maxNewDocID); + newIdToOldOrdinal.put(newId, oldOrd); + oldOrd++; + } + + if (maxNewDocID == -1) { + return Collections.emptyMap(); + } + + Map<Integer, Integer> oldToNewOrdinalMap = new HashMap<>(); + + DocIdSetIterator vectorIterator = null; + switch (fieldInfo.getVectorEncoding()) { + case BYTE -> vectorIterator = MergedVectorValues.mergeByteVectorValues(fieldInfo, mergeState); + case FLOAT32 -> vectorIterator = + MergedVectorValues.mergeFloatVectorValues(fieldInfo, mergeState); + } + + int newOrd = 0; + for (int newDocId = vectorIterator.nextDoc(); + newDocId <= maxNewDocID; + newDocId = vectorIterator.nextDoc()) { + if (isCurrentVectorNull(vectorIterator)) { + continue; + } + + if (newIdToOldOrdinal.containsKey(newDocId)) { + oldToNewOrdinalMap.put(newIdToOldOrdinal.get(newDocId), newOrd); + } + newOrd++; + } + + return oldToNewOrdinalMap; + } + + private boolean isCurrentVectorNull(DocIdSetIterator docIdSetIterator) throws IOException { + if (docIdSetIterator instanceof FloatVectorValues) { + return ((FloatVectorValues) docIdSetIterator).vectorValue() == null; + } + + if (docIdSetIterator instanceof ByteVectorValues) { + return ((ByteVectorValues) docIdSetIterator).vectorValue() == null; + } + + return true; + } + + private boolean allMatch(Bits bits) { + if (bits == null) { + return true; + } + + for (int i = 0; i < bits.length(); i++) { + if (!bits.get(i)) { + return false; + } + } + return true; + } + + /** + * @param graph Write the graph in a compressed format + * @return The non-cumulative offsets for the nodes. Should be used to create cumulative offsets. + * @throws IOException if writing to vectorIndex fails + */ + private int[][] writeGraph(OnHeapHnswGraph graph) throws IOException { + if (graph == null) return new int[0][0]; + // write vectors' neighbours on each level into the vectorIndex file + int countOnLevel0 = graph.size(); + int[][] offsets = new int[graph.numLevels()][]; + for (int level = 0; level < graph.numLevels(); level++) { + int[] sortedNodes = getSortedNodes(graph.getNodesOnLevel(level)); + offsets[level] = new int[sortedNodes.length]; + int nodeOffsetId = 0; + for (int node : sortedNodes) { + NeighborArray neighbors = graph.getNeighbors(level, node); + int size = neighbors.size(); + // Write size in VInt as the neighbors list is typically small + long offsetStart = vectorIndex.getFilePointer(); + vectorIndex.writeVInt(size); + // Destructively modify; it's ok we are discarding it after this + int[] nnodes = neighbors.node(); + Arrays.sort(nnodes, 0, size); + // Now that we have sorted, do delta encoding to minimize the required bits to store the + // information + for (int i = size - 1; i > 0; --i) { + assert nnodes[i] < countOnLevel0 : "node too large: " + nnodes[i] + ">=" + countOnLevel0; + nnodes[i] -= nnodes[i - 1]; + } + for (int i = 0; i < size; i++) { + vectorIndex.writeVInt(nnodes[i]); + } + offsets[level][nodeOffsetId++] = + Math.toIntExact(vectorIndex.getFilePointer() - offsetStart); + } + } + return offsets; + } + + public static int[] getSortedNodes(NodesIterator nodesOnLevel) { + int[] sortedNodes = new int[nodesOnLevel.size()]; + for (int n = 0; nodesOnLevel.hasNext(); n++) { + sortedNodes[n] = nodesOnLevel.nextInt(); + } + Arrays.sort(sortedNodes); + return sortedNodes; + } + + private void writeMeta( + boolean isQuantized, + FieldInfo field, + int maxDoc, + Float configuredQuantizationQuantile, + Float lowerQuantile, + Float upperQuantile, + long[] quantizedVectorDataOffsetAndLen, + long vectorDataOffset, + long vectorDataLength, + long vectorIndexOffset, + long vectorIndexLength, + DocsWithFieldSet docsWithField, + HnswGraph graph, + int[][] graphLevelNodeOffsets) + throws IOException { + meta.writeInt(field.number); + meta.writeInt(field.getVectorEncoding().ordinal()); + meta.writeInt(field.getVectorSimilarityFunction().ordinal()); + meta.writeByte(isQuantized ? (byte) 1 : (byte) 0); + if (isQuantized) { + assert lowerQuantile != null + && upperQuantile != null + && quantizedVectorDataOffsetAndLen != null; + assert quantizedVectorDataOffsetAndLen.length == 2; + meta.writeInt( + Float.floatToIntBits( + configuredQuantizationQuantile != null + ? configuredQuantizationQuantile + : calculateDefaultQuantile(field.getVectorDimension()))); + meta.writeInt(Float.floatToIntBits(lowerQuantile)); + meta.writeInt(Float.floatToIntBits(upperQuantile)); + meta.writeVLong(quantizedVectorDataOffsetAndLen[0]); + meta.writeVLong(quantizedVectorDataOffsetAndLen[1]); + } else { + assert configuredQuantizationQuantile == null + && lowerQuantile == null + && upperQuantile == null + && quantizedVectorDataOffsetAndLen == null; + } + meta.writeVLong(vectorDataOffset); + meta.writeVLong(vectorDataLength); + meta.writeVLong(vectorIndexOffset); + meta.writeVLong(vectorIndexLength); + meta.writeVInt(field.getVectorDimension()); + + // write docIDs + int count = docsWithField.cardinality(); + meta.writeInt(count); + if (isQuantized) { + writeOrdToDoc(quantizedVectorData, docsWithField, count, maxDoc); Review Comment: Is it necessary to create a new mapping? Could we leverage the existing mapping that was established for the raw vectors? ########## lucene/core/src/java/org/apache/lucene/codecs/lucene99/Lucene99HnswVectorsWriter.java: ########## @@ -0,0 +1,1170 @@ +/* + * 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.codecs.lucene99; + +import static org.apache.lucene.codecs.lucene99.Lucene99HnswVectorsFormat.DIRECT_MONOTONIC_BLOCK_SHIFT; +import static org.apache.lucene.codecs.lucene99.Lucene99ScalarQuantizedVectorsFormat.calculateDefaultQuantile; +import static org.apache.lucene.search.DocIdSetIterator.NO_MORE_DOCS; + +import java.io.IOException; +import java.nio.ByteBuffer; +import java.nio.ByteOrder; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import org.apache.lucene.codecs.CodecUtil; +import org.apache.lucene.codecs.HnswGraphProvider; +import org.apache.lucene.codecs.KnnFieldVectorsWriter; +import org.apache.lucene.codecs.KnnVectorsReader; +import org.apache.lucene.codecs.KnnVectorsWriter; +import org.apache.lucene.codecs.lucene90.IndexedDISI; +import org.apache.lucene.codecs.perfield.PerFieldKnnVectorsFormat; +import org.apache.lucene.index.ByteVectorValues; +import org.apache.lucene.index.DocsWithFieldSet; +import org.apache.lucene.index.FieldInfo; +import org.apache.lucene.index.FloatVectorValues; +import org.apache.lucene.index.IndexFileNames; +import org.apache.lucene.index.MergeState; +import org.apache.lucene.index.SegmentWriteState; +import org.apache.lucene.index.Sorter; +import org.apache.lucene.index.VectorEncoding; +import org.apache.lucene.search.DocIdSetIterator; +import org.apache.lucene.store.IndexInput; +import org.apache.lucene.store.IndexOutput; +import org.apache.lucene.util.ArrayUtil; +import org.apache.lucene.util.Bits; +import org.apache.lucene.util.IOUtils; +import org.apache.lucene.util.InfoStream; +import org.apache.lucene.util.RamUsageEstimator; +import org.apache.lucene.util.hnsw.HnswGraph; +import org.apache.lucene.util.hnsw.HnswGraph.NodesIterator; +import org.apache.lucene.util.hnsw.HnswGraphBuilder; +import org.apache.lucene.util.hnsw.NeighborArray; +import org.apache.lucene.util.hnsw.OnHeapHnswGraph; +import org.apache.lucene.util.hnsw.RandomAccessVectorValues; +import org.apache.lucene.util.hnsw.RandomVectorScorerSupplier; +import org.apache.lucene.util.packed.DirectMonotonicWriter; + +/** + * Writes vector values and knn graphs to index segments. + * + * @lucene.experimental + */ +public final class Lucene99HnswVectorsWriter extends KnnVectorsWriter { + + private final SegmentWriteState segmentWriteState; + private final IndexOutput meta, vectorData, quantizedVectorData, vectorIndex; + private final int M; + private final int beamWidth; + private final Lucene99ScalarQuantizedVectorsWriter quantizedVectorsWriter; + + private final List<FieldWriter<?>> fields = new ArrayList<>(); + private boolean finished; + + Lucene99HnswVectorsWriter( + SegmentWriteState state, + int M, + int beamWidth, + Lucene99ScalarQuantizedVectorsFormat quantizedVectorsFormat) + throws IOException { + this.M = M; + this.beamWidth = beamWidth; + segmentWriteState = state; + String metaFileName = + IndexFileNames.segmentFileName( + state.segmentInfo.name, state.segmentSuffix, Lucene99HnswVectorsFormat.META_EXTENSION); + + String vectorDataFileName = + IndexFileNames.segmentFileName( + state.segmentInfo.name, + state.segmentSuffix, + Lucene99HnswVectorsFormat.VECTOR_DATA_EXTENSION); + + String indexDataFileName = + IndexFileNames.segmentFileName( + state.segmentInfo.name, + state.segmentSuffix, + Lucene99HnswVectorsFormat.VECTOR_INDEX_EXTENSION); + + final String quantizedVectorDataFileName = + quantizedVectorsFormat != null + ? IndexFileNames.segmentFileName( + state.segmentInfo.name, + state.segmentSuffix, + Lucene99ScalarQuantizedVectorsFormat.QUANTIZED_VECTOR_DATA_EXTENSION) + : null; + boolean success = false; + try { + meta = state.directory.createOutput(metaFileName, state.context); + vectorData = state.directory.createOutput(vectorDataFileName, state.context); + vectorIndex = state.directory.createOutput(indexDataFileName, state.context); + + CodecUtil.writeIndexHeader( + meta, + Lucene99HnswVectorsFormat.META_CODEC_NAME, + Lucene99HnswVectorsFormat.VERSION_CURRENT, + state.segmentInfo.getId(), + state.segmentSuffix); + CodecUtil.writeIndexHeader( + vectorData, + Lucene99HnswVectorsFormat.VECTOR_DATA_CODEC_NAME, + Lucene99HnswVectorsFormat.VERSION_CURRENT, + state.segmentInfo.getId(), + state.segmentSuffix); + CodecUtil.writeIndexHeader( + vectorIndex, + Lucene99HnswVectorsFormat.VECTOR_INDEX_CODEC_NAME, + Lucene99HnswVectorsFormat.VERSION_CURRENT, + state.segmentInfo.getId(), + state.segmentSuffix); + if (quantizedVectorDataFileName != null) { + quantizedVectorData = + state.directory.createOutput(quantizedVectorDataFileName, state.context); + CodecUtil.writeIndexHeader( + quantizedVectorData, + Lucene99ScalarQuantizedVectorsFormat.QUANTIZED_VECTOR_DATA_CODEC_NAME, + Lucene99ScalarQuantizedVectorsFormat.VERSION_CURRENT, + state.segmentInfo.getId(), + state.segmentSuffix); + quantizedVectorsWriter = + new Lucene99ScalarQuantizedVectorsWriter( + quantizedVectorData, quantizedVectorsFormat.quantile); + } else { + quantizedVectorData = null; + quantizedVectorsWriter = null; + } + success = true; + } finally { + if (success == false) { + IOUtils.closeWhileHandlingException(this); + } + } + } + + @Override + public KnnFieldVectorsWriter<?> addField(FieldInfo fieldInfo) throws IOException { + Lucene99ScalarQuantizedVectorsWriter.QuantizationVectorWriter quantizedVectorFieldWriter = null; + // Quantization only supports FLOAT32 for now + if (quantizedVectorsWriter != null + && fieldInfo.getVectorEncoding().equals(VectorEncoding.FLOAT32)) { + quantizedVectorFieldWriter = quantizedVectorsWriter.addField(fieldInfo); + } + FieldWriter<?> newField = + FieldWriter.create( + fieldInfo, M, beamWidth, segmentWriteState.infoStream, quantizedVectorFieldWriter); + fields.add(newField); + return newField; + } + + @Override + public void flush(int maxDoc, Sorter.DocMap sortMap) throws IOException { + for (FieldWriter<?> field : fields) { + long[] quantizedVectorOffsetAndLen = null; + if (field.quantizedWriter != null) { + assert quantizedVectorsWriter != null; + quantizedVectorOffsetAndLen = + quantizedVectorsWriter.flush(sortMap, field.quantizedWriter, field.docsWithField); + } + if (sortMap == null) { + writeField(field, maxDoc, quantizedVectorOffsetAndLen); + } else { + writeSortingField(field, maxDoc, sortMap, quantizedVectorOffsetAndLen); + } + } + } + + @Override + public void finish() throws IOException { + if (finished) { + throw new IllegalStateException("already finished"); + } + finished = true; + if (quantizedVectorsWriter != null) { + quantizedVectorsWriter.finish(); + } + + if (meta != null) { + // write end of fields marker + meta.writeInt(-1); + CodecUtil.writeFooter(meta); + } + if (vectorData != null) { + CodecUtil.writeFooter(vectorData); + CodecUtil.writeFooter(vectorIndex); + } + } + + @Override + public long ramBytesUsed() { + long total = 0; + for (FieldWriter<?> field : fields) { + total += field.ramBytesUsed(); + } + return total; + } + + private void writeField(FieldWriter<?> fieldData, int maxDoc, long[] quantizedVecOffsetAndLen) + throws IOException { + // write vector values + long vectorDataOffset = vectorData.alignFilePointer(Float.BYTES); + switch (fieldData.fieldInfo.getVectorEncoding()) { + case BYTE -> writeByteVectors(fieldData); + case FLOAT32 -> writeFloat32Vectors(fieldData); + } + long vectorDataLength = vectorData.getFilePointer() - vectorDataOffset; + + // write graph + long vectorIndexOffset = vectorIndex.getFilePointer(); + OnHeapHnswGraph graph = fieldData.getGraph(); + int[][] graphLevelNodeOffsets = writeGraph(graph); + long vectorIndexLength = vectorIndex.getFilePointer() - vectorIndexOffset; + + writeMeta( + fieldData.isQuantized(), + fieldData.fieldInfo, + maxDoc, + fieldData.getConfiguredQuantile(), + fieldData.getMinQuantile(), + fieldData.getMaxQuantile(), + quantizedVecOffsetAndLen, + vectorDataOffset, + vectorDataLength, + vectorIndexOffset, + vectorIndexLength, + fieldData.docsWithField, + graph, + graphLevelNodeOffsets); + } + + private void writeFloat32Vectors(FieldWriter<?> fieldData) throws IOException { + final ByteBuffer buffer = + ByteBuffer.allocate(fieldData.dim * Float.BYTES).order(ByteOrder.LITTLE_ENDIAN); + for (Object v : fieldData.vectors) { + buffer.asFloatBuffer().put((float[]) v); + vectorData.writeBytes(buffer.array(), buffer.array().length); + } + } + + private void writeByteVectors(FieldWriter<?> fieldData) throws IOException { + for (Object v : fieldData.vectors) { + byte[] vector = (byte[]) v; + vectorData.writeBytes(vector, vector.length); + } + } + + private void writeSortingField( + FieldWriter<?> fieldData, + int maxDoc, + Sorter.DocMap sortMap, + long[] quantizedVectorOffsetAndLen) + throws IOException { + final int[] docIdOffsets = new int[sortMap.size()]; + int offset = 1; // 0 means no vector for this (field, document) + DocIdSetIterator iterator = fieldData.docsWithField.iterator(); + for (int docID = iterator.nextDoc(); + docID != DocIdSetIterator.NO_MORE_DOCS; + docID = iterator.nextDoc()) { + int newDocID = sortMap.oldToNew(docID); + docIdOffsets[newDocID] = offset++; + } + DocsWithFieldSet newDocsWithField = new DocsWithFieldSet(); + final int[] ordMap = new int[offset - 1]; // new ord to old ord + final int[] oldOrdMap = new int[offset - 1]; // old ord to new ord + int ord = 0; + int doc = 0; + for (int docIdOffset : docIdOffsets) { + if (docIdOffset != 0) { + ordMap[ord] = docIdOffset - 1; + oldOrdMap[docIdOffset - 1] = ord; + newDocsWithField.add(doc); + ord++; + } + doc++; + } + + // write vector values + long vectorDataOffset = + switch (fieldData.fieldInfo.getVectorEncoding()) { + case BYTE -> writeSortedByteVectors(fieldData, ordMap); + case FLOAT32 -> writeSortedFloat32Vectors(fieldData, ordMap); + }; + long vectorDataLength = vectorData.getFilePointer() - vectorDataOffset; + + // write graph + long vectorIndexOffset = vectorIndex.getFilePointer(); + OnHeapHnswGraph graph = fieldData.getGraph(); + int[][] graphLevelNodeOffsets = graph == null ? new int[0][] : new int[graph.numLevels()][]; + HnswGraph mockGraph = reconstructAndWriteGraph(graph, ordMap, oldOrdMap, graphLevelNodeOffsets); + long vectorIndexLength = vectorIndex.getFilePointer() - vectorIndexOffset; + + writeMeta( + fieldData.isQuantized(), + fieldData.fieldInfo, + maxDoc, + fieldData.getConfiguredQuantile(), + fieldData.getMinQuantile(), + fieldData.getMaxQuantile(), + quantizedVectorOffsetAndLen, + vectorDataOffset, + vectorDataLength, + vectorIndexOffset, + vectorIndexLength, + newDocsWithField, + mockGraph, + graphLevelNodeOffsets); + } + + private long writeSortedFloat32Vectors(FieldWriter<?> fieldData, int[] ordMap) + throws IOException { + long vectorDataOffset = vectorData.alignFilePointer(Float.BYTES); + final ByteBuffer buffer = + ByteBuffer.allocate(fieldData.dim * Float.BYTES).order(ByteOrder.LITTLE_ENDIAN); + for (int ordinal : ordMap) { + float[] vector = (float[]) fieldData.vectors.get(ordinal); + buffer.asFloatBuffer().put(vector); + vectorData.writeBytes(buffer.array(), buffer.array().length); + } + return vectorDataOffset; + } + + private long writeSortedByteVectors(FieldWriter<?> fieldData, int[] ordMap) throws IOException { + long vectorDataOffset = vectorData.alignFilePointer(Float.BYTES); + for (int ordinal : ordMap) { + byte[] vector = (byte[]) fieldData.vectors.get(ordinal); + vectorData.writeBytes(vector, vector.length); + } + return vectorDataOffset; + } + + /** + * Reconstructs the graph given the old and new node ids. + * + * <p>Additionally, the graph node connections are written to the vectorIndex. + * + * @param graph The current on heap graph + * @param newToOldMap the new node ids indexed to the old node ids + * @param oldToNewMap the old node ids indexed to the new node ids + * @param levelNodeOffsets where to place the new offsets for the nodes in the vector index. + * @return The graph + * @throws IOException if writing to vectorIndex fails + */ + private HnswGraph reconstructAndWriteGraph( + OnHeapHnswGraph graph, int[] newToOldMap, int[] oldToNewMap, int[][] levelNodeOffsets) + throws IOException { + if (graph == null) return null; + + List<int[]> nodesByLevel = new ArrayList<>(graph.numLevels()); + nodesByLevel.add(null); + + int maxOrd = graph.size(); + NodesIterator nodesOnLevel0 = graph.getNodesOnLevel(0); + levelNodeOffsets[0] = new int[nodesOnLevel0.size()]; + while (nodesOnLevel0.hasNext()) { + int node = nodesOnLevel0.nextInt(); + NeighborArray neighbors = graph.getNeighbors(0, newToOldMap[node]); + long offset = vectorIndex.getFilePointer(); + reconstructAndWriteNeigbours(neighbors, oldToNewMap, maxOrd); + levelNodeOffsets[0][node] = Math.toIntExact(vectorIndex.getFilePointer() - offset); + } + + for (int level = 1; level < graph.numLevels(); level++) { + NodesIterator nodesOnLevel = graph.getNodesOnLevel(level); + int[] newNodes = new int[nodesOnLevel.size()]; + for (int n = 0; nodesOnLevel.hasNext(); n++) { + newNodes[n] = oldToNewMap[nodesOnLevel.nextInt()]; + } + Arrays.sort(newNodes); + nodesByLevel.add(newNodes); + levelNodeOffsets[level] = new int[newNodes.length]; + int nodeOffsetIndex = 0; + for (int node : newNodes) { + NeighborArray neighbors = graph.getNeighbors(level, newToOldMap[node]); + long offset = vectorIndex.getFilePointer(); + reconstructAndWriteNeigbours(neighbors, oldToNewMap, maxOrd); + levelNodeOffsets[level][nodeOffsetIndex++] = + Math.toIntExact(vectorIndex.getFilePointer() - offset); + } + } + return new HnswGraph() { + @Override + public int nextNeighbor() { + throw new UnsupportedOperationException("Not supported on a mock graph"); + } + + @Override + public void seek(int level, int target) { + throw new UnsupportedOperationException("Not supported on a mock graph"); + } + + @Override + public int size() { + return graph.size(); + } + + @Override + public int numLevels() { + return graph.numLevels(); + } + + @Override + public int entryNode() { + throw new UnsupportedOperationException("Not supported on a mock graph"); + } + + @Override + public NodesIterator getNodesOnLevel(int level) { + if (level == 0) { + return graph.getNodesOnLevel(0); + } else { + return new ArrayNodesIterator(nodesByLevel.get(level), nodesByLevel.get(level).length); + } + } + }; + } + + private void reconstructAndWriteNeigbours(NeighborArray neighbors, int[] oldToNewMap, int maxOrd) + throws IOException { + int size = neighbors.size(); + vectorIndex.writeVInt(size); + + // Destructively modify; it's ok we are discarding it after this + int[] nnodes = neighbors.node(); + for (int i = 0; i < size; i++) { + nnodes[i] = oldToNewMap[nnodes[i]]; + } + Arrays.sort(nnodes, 0, size); + // Now that we have sorted, do delta encoding to minimize the required bits to store the + // information + for (int i = size - 1; i > 0; --i) { + assert nnodes[i] < maxOrd : "node too large: " + nnodes[i] + ">=" + maxOrd; + nnodes[i] -= nnodes[i - 1]; + } + for (int i = 0; i < size; i++) { + vectorIndex.writeVInt(nnodes[i]); + } + } + + @Override + public void mergeOneField(FieldInfo fieldInfo, MergeState mergeState) throws IOException { + long vectorDataOffset = vectorData.alignFilePointer(Float.BYTES); + IndexOutput tempVectorData = + segmentWriteState.directory.createTempOutput( + vectorData.getName(), "temp", segmentWriteState.context); + IndexInput vectorDataInput = null; + IndexInput quantizationDataInput = null; + Lucene99ScalarQuantizedVectorsWriter.MergedQuantileState quantizationState = null; + boolean success = false; + try { + long[] quantizedVectorDataOffsetAndLength = null; + if (quantizedVectorsWriter != null) { + quantizationState = + quantizedVectorsWriter.mergeOneField(segmentWriteState, fieldInfo, mergeState); + if (quantizationState != null) { + quantizedVectorDataOffsetAndLength = new long[2]; + quantizedVectorDataOffsetAndLength[0] = quantizedVectorData.alignFilePointer(Float.BYTES); + quantizationDataInput = + segmentWriteState.directory.openInput( + quantizationState.tempVectorFileName, segmentWriteState.context); + quantizedVectorData.copyBytes( + quantizationDataInput, quantizationDataInput.length() - CodecUtil.footerLength()); + quantizedVectorDataOffsetAndLength[1] = + quantizedVectorData.getFilePointer() - quantizedVectorDataOffsetAndLength[0]; + CodecUtil.retrieveChecksum(quantizationDataInput); + } + } + // write the vector data to a temporary file Review Comment: We don't need to write the raw vectors in a temporary file if we build the graph with the quantised vectors? This part could be extracted in a separate function that writes the raw and quantised vectors and returns a "closeable" `RandomVectorScorerSupplier`. ########## lucene/core/src/java/org/apache/lucene/codecs/lucene99/package-info.java: ########## @@ -0,0 +1,437 @@ +/* + * 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. + */ + +/** + * Lucene 9.8 file format. + * + * <h2>Apache Lucene - Index File Formats</h2> + * + * <div> + * + * <ul> + * <li><a href="#Introduction">Introduction</a> + * <li><a href="#Definitions">Definitions</a> + * <ul> + * <li><a href="#Inverted_Indexing">Inverted Indexing</a> + * <li><a href="#Types_of_Fields">Types of Fields</a> + * <li><a href="#Segments">Segments</a> + * <li><a href="#Document_Numbers">Document Numbers</a> + * </ul> + * <li><a href="#Overview">Index Structure Overview</a> + * <li><a href="#File_Naming">File Naming</a> + * <li><a href="#file-names">Summary of File Extensions</a> + * <ul> + * <li><a href="#Lock_File">Lock File</a> + * <li><a href="#History">History</a> + * <li><a href="#Limitations">Limitations</a> + * </ul> + * </ul> + * + * </div> <a id="Introduction"></a> + * + * <h3>Introduction</h3> + * + * <div> + * + * <p>This document defines the index file formats used in this version of Lucene. If you are using + * a different version of Lucene, please consult the copy of <code>docs/</code> that was distributed + * with the version you are using. + * + * <p>This document attempts to provide a high-level definition of the Apache Lucene file formats. + * </div> <a id="Definitions"></a> + * + * <h3>Definitions</h3> + * + * <div> + * + * <p>The fundamental concepts in Lucene are index, document, field and term. + * + * <p>An index contains a sequence of documents. + * + * <ul> + * <li>A document is a sequence of fields. + * <li>A field is a named sequence of terms. + * <li>A term is a sequence of bytes. + * </ul> + * + * <p>The same sequence of bytes in two different fields is considered a different term. Thus terms + * are represented as a pair: the string naming the field, and the bytes within the field. <a + * id="Inverted_Indexing"></a> + * + * <h4>Inverted Indexing</h4> + * + * <p>Lucene's index stores terms and statistics about those terms in order to make term-based + * search more efficient. Lucene's terms index falls into the family of indexes known as an + * <i>inverted index.</i> This is because it can list, for a term, the documents that contain it. + * This is the inverse of the natural relationship, in which documents list terms. <a + * id="Types_of_Fields"></a> + * + * <h4>Types of Fields</h4> + * + * <p>In Lucene, fields may be <i>stored</i>, in which case their text is stored in the index + * literally, in a non-inverted manner. Fields that are inverted are called <i>indexed</i>. A field + * may be both stored and indexed. + * + * <p>The text of a field may be <i>tokenized</i> into terms to be indexed, or the text of a field + * may be used literally as a term to be indexed. Most fields are tokenized, but sometimes it is + * useful for certain identifier fields to be indexed literally. + * + * <p>See the {@link org.apache.lucene.document.Field Field} java docs for more information on + * Fields. <a id="Segments"></a> + * + * <h4>Segments</h4> + * + * <p>Lucene indexes may be composed of multiple sub-indexes, or <i>segments</i>. Each segment is a + * fully independent index, which could be searched separately. Indexes evolve by: + * + * <ol> + * <li>Creating new segments for newly added documents. + * <li>Merging existing segments. + * </ol> + * + * <p>Searches may involve multiple segments and/or multiple indexes, each index potentially + * composed of a set of segments. <a id="Document_Numbers"></a> + * + * <h4>Document Numbers</h4> + * + * <p>Internally, Lucene refers to documents by an integer <i>document number</i>. The first + * document added to an index is numbered zero, and each subsequent document added gets a number one + * greater than the previous. + * + * <p>Note that a document's number may change, so caution should be taken when storing these + * numbers outside of Lucene. In particular, numbers may change in the following situations: + * + * <ul> + * <li> + * <p>The numbers stored in each segment are unique only within the segment, and must be + * converted before they can be used in a larger context. The standard technique is to + * allocate each segment a range of values, based on the range of numbers used in that + * segment. To convert a document number from a segment to an external value, the segment's + * <i>base</i> document number is added. To convert an external value back to a + * segment-specific value, the segment is identified by the range that the external value is + * in, and the segment's base value is subtracted. For example two five document segments + * might be combined, so that the first segment has a base value of zero, and the second of + * five. Document three from the second segment would have an external value of eight. + * <li> + * <p>When documents are deleted, gaps are created in the numbering. These are eventually + * removed as the index evolves through merging. Deleted documents are dropped when segments + * are merged. A freshly-merged segment thus has no gaps in its numbering. + * </ul> + * + * </div> <a id="Overview"></a> + * + * <h3>Index Structure Overview</h3> + * + * <div> + * + * <p>Each segment index maintains the following: + * + * <ul> + * <li>{@link org.apache.lucene.codecs.lucene90.Lucene90SegmentInfoFormat Segment info}. This + * contains metadata about a segment, such as the number of documents, what files it uses, and + * information about how the segment is sorted + * <li>{@link org.apache.lucene.codecs.lucene94.Lucene94FieldInfosFormat Field names}. This + * contains metadata about the set of named fields used in the index. + * <li>{@link org.apache.lucene.codecs.lucene90.Lucene90StoredFieldsFormat Stored Field values}. + * This contains, for each document, a list of attribute-value pairs, where the attributes are + * field names. These are used to store auxiliary information about the document, such as its + * title, url, or an identifier to access a database. The set of stored fields are what is + * returned for each hit when searching. This is keyed by document number. + * <li>{@link org.apache.lucene.codecs.lucene90.Lucene90PostingsFormat Term dictionary}. A + * dictionary containing all of the terms used in all of the indexed fields of all of the + * documents. The dictionary also contains the number of documents which contain the term, and + * pointers to the term's frequency and proximity data. + * <li>{@link org.apache.lucene.codecs.lucene90.Lucene90PostingsFormat Term Frequency data}. For + * each term in the dictionary, the numbers of all the documents that contain that term, and + * the frequency of the term in that document, unless frequencies are omitted ({@link + * org.apache.lucene.index.IndexOptions#DOCS IndexOptions.DOCS}) + * <li>{@link org.apache.lucene.codecs.lucene90.Lucene90PostingsFormat Term Proximity data}. For + * each term in the dictionary, the positions that the term occurs in each document. Note that + * this will not exist if all fields in all documents omit position data. + * <li>{@link org.apache.lucene.codecs.lucene90.Lucene90NormsFormat Normalization factors}. For + * each field in each document, a value is stored that is multiplied into the score for hits + * on that field. + * <li>{@link org.apache.lucene.codecs.lucene90.Lucene90TermVectorsFormat Term Vectors}. For each + * field in each document, the term vector (sometimes called document vector) may be stored. A + * term vector consists of term text and term frequency. To add Term Vectors to your index see + * the {@link org.apache.lucene.document.Field Field} constructors + * <li>{@link org.apache.lucene.codecs.lucene90.Lucene90DocValuesFormat Per-document values}. Like + * stored values, these are also keyed by document number, but are generally intended to be + * loaded into main memory for fast access. Whereas stored values are generally intended for + * summary results from searches, per-document values are useful for things like scoring + * factors. + * <li>{@link org.apache.lucene.codecs.lucene90.Lucene90LiveDocsFormat Live documents}. An + * optional file indicating which documents are live. + * <li>{@link org.apache.lucene.codecs.lucene90.Lucene90PointsFormat Point values}. Optional pair + * of files, recording dimensionally indexed fields, to enable fast numeric range filtering + * and large numeric values like BigInteger and BigDecimal (1D) and geographic shape + * intersection (2D, 3D). + * <li>{@link org.apache.lucene.codecs.lucene99.Lucene99HnswVectorsFormat Vector values}. The + * vector format stores numeric vectors in a format optimized for random access and + * computation, supporting high-dimensional nearest-neighbor search. + * <li>{@link org.apache.lucene.codecs.lucene99.Lucene99ScalarQuantizedVectorsFormat quantized + * vector values}. The quantized vector format stores numeric vectors in a scalar quantized + * format optimized for random access and computation. Can be used in conjunction with HNSW to + * support approximate nearest-neighbor search at query time. + * </ul> + * + * <p>Details on each of these are provided in their linked pages. </div> <a id="File_Naming"></a> + * + * <h3>File Naming</h3> + * + * <div> + * + * <p>All files belonging to a segment have the same name with varying extensions. The extensions + * correspond to the different file formats described below. When using the Compound File format + * (default for small segments) these files (except for the Segment info file, the Lock file, and + * Deleted documents file) are collapsed into a single .cfs file (see below for details) + * + * <p>Typically, all segments in an index are stored in a single directory, although this is not + * required. + * + * <p>File names are never re-used. That is, when any file is saved to the Directory it is given a + * never before used filename. This is achieved using a simple generations approach. For example, + * the first segments file is segments_1, then segments_2, etc. The generation is a sequential long + * integer represented in alpha-numeric (base 36) form. </div> <a id="file-names"></a> + * + * <h3>Summary of File Extensions</h3> + * + * <div> + * + * <p>The following table summarizes the names and extensions of the files in Lucene: + * + * <table class="padding4" style="border-spacing: 1px; border-collapse: separate"> + * <caption>lucene filenames by extension</caption> + * <tr> + * <th>Name</th> + * <th>Extension</th> + * <th>Brief Description</th> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.index.SegmentInfos Segments File}</td> + * <td>segments_N</td> + * <td>Stores information about a commit point</td> + * </tr> + * <tr> + * <td><a href="#Lock_File">Lock File</a></td> + * <td>write.lock</td> + * <td>The Write lock prevents multiple IndexWriters from writing to the same + * file.</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90SegmentInfoFormat Segment Info}</td> + * <td>.si</td> + * <td>Stores metadata about a segment</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90CompoundFormat Compound File}</td> + * <td>.cfs, .cfe</td> + * <td>An optional "virtual" file consisting of all the other index files for + * systems that frequently run out of file handles.</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene94.Lucene94FieldInfosFormat Fields}</td> + * <td>.fnm</td> + * <td>Stores information about the fields</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90StoredFieldsFormat Field Index}</td> + * <td>.fdx</td> + * <td>Contains pointers to field data</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90StoredFieldsFormat Field Data}</td> + * <td>.fdt</td> + * <td>The stored fields for documents</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90PostingsFormat Term Dictionary}</td> + * <td>.tim</td> + * <td>The term dictionary, stores term info</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90PostingsFormat Term Index}</td> + * <td>.tip</td> + * <td>The index into the Term Dictionary</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90PostingsFormat Frequencies}</td> + * <td>.doc</td> + * <td>Contains the list of docs which contain each term along with frequency</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90PostingsFormat Positions}</td> + * <td>.pos</td> + * <td>Stores position information about where a term occurs in the index</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90PostingsFormat Payloads}</td> + * <td>.pay</td> + * <td>Stores additional per-position metadata information such as character offsets and user payloads</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90NormsFormat Norms}</td> + * <td>.nvd, .nvm</td> + * <td>Encodes length and boost factors for docs and fields</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90DocValuesFormat Per-Document Values}</td> + * <td>.dvd, .dvm</td> + * <td>Encodes additional scoring factors or other per-document information.</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90TermVectorsFormat Term Vector Index}</td> + * <td>.tvx</td> + * <td>Stores offset into the document data file</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90TermVectorsFormat Term Vector Data}</td> + * <td>.tvd</td> + * <td>Contains term vector data.</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90LiveDocsFormat Live Documents}</td> + * <td>.liv</td> + * <td>Info about what documents are live</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene90.Lucene90PointsFormat Point values}</td> + * <td>.dii, .dim</td> + * <td>Holds indexed points</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene99.Lucene99HnswVectorsFormat Vector values}</td> + * <td>.vec, .vem</td> + * <td>Holds indexed vectors; <code>.vec</code> files contain the raw vector data, and + * <code>.vem</code> the vector metadata</td> + * </tr> + * <tr> + * <td>{@link org.apache.lucene.codecs.lucene99.Lucene99ScalarQuantizedVectorsFormat Quantized Vector values}</td> + * <td>.veq, .vemq</td> + * <td><code>.veq</code>Holds quantized vector values; the byte[] encoded vectors and relevant offsets + * <code>.vemq</code> the quantized vector metadata</td> + * </tr> + * </table> + * + * </div> <a id="Lock_File"></a> + * + * <h3>Lock File</h3> + * + * The write lock, which is stored in the index directory by default, is named "write.lock". If the + * lock directory is different from the index directory then the write lock will be named + * "XXXX-write.lock" where XXXX is a unique prefix derived from the full path to the index + * directory. When this file is present, a writer is currently modifying the index (adding or + * removing documents). This lock file ensures that only one writer is modifying the index at a + * time. <a id="History"></a> + * + * <h3>History</h3> + * + * <p>Compatibility notes are provided in this document, describing how file formats have changed + * from prior versions: + * + * <ul> + * <li>In version 2.1, the file format was changed to allow lock-less commits (ie, no more commit + * lock). The change is fully backwards compatible: you can open a pre-2.1 index for searching + * or adding/deleting of docs. When the new segments file is saved (committed), it will be + * written in the new file format (meaning no specific "upgrade" process is needed). But note + * that once a commit has occurred, pre-2.1 Lucene will not be able to read the index. + * <li>In version 2.3, the file format was changed to allow segments to share a single set of doc + * store (vectors & stored fields) files. This allows for faster indexing in certain + * cases. The change is fully backwards compatible (in the same way as the lock-less commits + * change in 2.1). + * <li>In version 2.4, Strings are now written as true UTF-8 byte sequence, not Java's modified + * UTF-8. See <a href="http://issues.apache.org/jira/browse/LUCENE-510";>LUCENE-510</a> for + * details. + * <li>In version 2.9, an optional opaque Map<String,String> CommitUserData may be passed to + * IndexWriter's commit methods (and later retrieved), which is recorded in the segments_N + * file. See <a href="http://issues.apache.org/jira/browse/LUCENE-1382";>LUCENE-1382</a> for + * details. Also, diagnostics were added to each segment written recording details about why + * it was written (due to flush, merge; which OS/JRE was used; etc.). See issue <a + * href="http://issues.apache.org/jira/browse/LUCENE-1654";>LUCENE-1654</a> for details. + * <li>In version 3.0, compressed fields are no longer written to the index (they can still be + * read, but on merge the new segment will write them, uncompressed). See issue <a + * href="http://issues.apache.org/jira/browse/LUCENE-1960";>LUCENE-1960</a> for details. + * <li>In version 3.1, segments records the code version that created them. See <a + * href="http://issues.apache.org/jira/browse/LUCENE-2720";>LUCENE-2720</a> for details. + * Additionally segments track explicitly whether or not they have term vectors. See <a + * href="http://issues.apache.org/jira/browse/LUCENE-2811";>LUCENE-2811</a> for details. + * <li>In version 3.2, numeric fields are written as natively to stored fields file, previously + * they were stored in text format only. + * <li>In version 3.4, fields can omit position data while still indexing term frequencies. + * <li>In version 4.0, the format of the inverted index became extensible via the {@link + * org.apache.lucene.codecs.Codec Codec} api. Fast per-document storage ({@code DocValues}) + * was introduced. Normalization factors need no longer be a single byte, they can be any + * {@link org.apache.lucene.index.NumericDocValues NumericDocValues}. Terms need not be + * unicode strings, they can be any byte sequence. Term offsets can optionally be indexed into + * the postings lists. Payloads can be stored in the term vectors. + * <li>In version 4.1, the format of the postings list changed to use either of FOR compression or + * variable-byte encoding, depending upon the frequency of the term. Terms appearing only once + * were changed to inline directly into the term dictionary. Stored fields are compressed by + * default. + * <li>In version 4.2, term vectors are compressed by default. DocValues has a new multi-valued + * type (SortedSet), that can be used for faceting/grouping/joining on multi-valued fields. + * <li>In version 4.5, DocValues were extended to explicitly represent missing values. + * <li>In version 4.6, FieldInfos were extended to support per-field DocValues generation, to + * allow updating NumericDocValues fields. + * <li>In version 4.8, checksum footers were added to the end of each index file for improved data + * integrity. Specifically, the last 8 bytes of every index file contain the zlib-crc32 + * checksum of the file. + * <li>In version 4.9, DocValues has a new multi-valued numeric type (SortedNumeric) that is + * suitable for faceting/sorting/analytics. + * <li>In version 5.4, DocValues have been improved to store more information on disk: addresses + * for binary fields and ord indexes for multi-valued fields. + * <li>In version 6.0, Points were added, for multi-dimensional range/distance search. + * <li>In version 6.2, new Segment info format that reads/writes the index sort, to support index + * sorting. + * <li>In version 7.0, DocValues have been improved to better support sparse doc values thanks to + * an iterator API. + * <li>In version 8.0, postings have been enhanced to record, for each block of doc ids, the (term + * freq, normalization factor) pairs that may trigger the maximum score of the block. This + * information is recorded alongside skip data in order to be able to skip blocks of doc ids + * if they may not produce high enough scores. Additionally doc values and norms has been + * extended with jump-tables to make access O(1) instead of O(n), where n is the number of + * elements to skip when advancing in the data. + * <li>In version 8.4, postings, positions, offsets and payload lengths have move to a more + * performant encoding that is vectorized. + * <li>In version 8.6, index sort serialization is delegated to the sorts themselves, to allow + * user-defined sorts to be used + * <li>In version 8.7, stored fields compression became adaptive to better handle documents with + * smaller stored fields. + * <li>In version 9.0, vector-valued fields were added. + * <li>In version 9.1, vector-valued fields were modified to add a graph hierarchy. + * <li>In version 9.2, docs of vector-valued fields were moved from .vem to .vec and encoded by + * IndexDISI. ordToDoc mappings was added to .vem. + * <li>In version 9.5, HNSW graph connections were changed to be delta-encoded with vints. + * Additionally, metadata file size improvements were made by delta-encoding nodes by graph + * layer and not writing the node ids for the zeroth layer. + * <li>In version 9.8, Vector scalar quantization support was added. Allowing the HNSW vector Review Comment: ```suggestion * <li>In version 9.9, Vector scalar quantization support was added. Allowing the HNSW vector ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@lucene.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@lucene.apache.org For additional commands, e-mail: issues-h...@lucene.apache.org
