Java tutorial
/* * 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.index; import java.io.IOException; import java.math.BigInteger; import java.net.InetAddress; import org.apache.lucene.document.BinaryPoint; import org.apache.lucene.document.DoublePoint; import org.apache.lucene.document.Field; import org.apache.lucene.document.FloatPoint; import org.apache.lucene.document.IntPoint; import org.apache.lucene.document.LatLonPoint; import org.apache.lucene.document.LongPoint; import org.apache.lucene.search.DocIdSetIterator; import org.apache.lucene.util.FutureArrays; import org.apache.lucene.util.bkd.BKDWriter; /** * Access to indexed numeric values. * <p> * Points represent numeric values and are indexed differently than ordinary text. Instead of an inverted index, * points are indexed with datastructures such as <a href="https://en.wikipedia.org/wiki/K-d_tree">KD-trees</a>. * These structures are optimized for operations such as <i>range</i>, <i>distance</i>, <i>nearest-neighbor</i>, * and <i>point-in-polygon</i> queries. * <h1>Basic Point Types</h1> * <table summary="Basic point types in Java and Lucene"> * <tr><th>Java type</th><th>Lucene class</th></tr> * <tr><td>{@code int}</td><td>{@link IntPoint}</td></tr> * <tr><td>{@code long}</td><td>{@link LongPoint}</td></tr> * <tr><td>{@code float}</td><td>{@link FloatPoint}</td></tr> * <tr><td>{@code double}</td><td>{@link DoublePoint}</td></tr> * <tr><td>{@code byte[]}</td><td>{@link BinaryPoint}</td></tr> * <tr><td>{@link BigInteger}</td><td><a href="{@docRoot}/../sandbox/org/apache/lucene/document/BigIntegerPoint.html">BigIntegerPoint</a>*</td></tr> * <tr><td>{@link InetAddress}</td><td><a href="{@docRoot}/../misc/org/apache/lucene/document/InetAddressPoint.html">InetAddressPoint</a>*</td></tr> * </table> * * in the <i>lucene-sandbox</i> jar<br> * <p> * Basic Lucene point types behave like their java peers: for example {@link IntPoint} represents a signed 32-bit * {@link Integer}, supporting values ranging from {@link Integer#MIN_VALUE} to {@link Integer#MAX_VALUE}, ordered * consistent with {@link Integer#compareTo(Integer)}. In addition to indexing support, point classes also contain * static methods (such as {@link IntPoint#newRangeQuery(String, int, int)}) for creating common queries. For example: * <pre class="prettyprint"> * // add year 1970 to document * document.add(new IntPoint("year", 1970)); * // index document * writer.addDocument(document); * ... * // issue range query of 1960-1980 * Query query = IntPoint.newRangeQuery("year", 1960, 1980); * TopDocs docs = searcher.search(query, ...); * </pre> * <h1>Geospatial Point Types</h1> * Although basic point types such as {@link DoublePoint} support points in multi-dimensional space too, Lucene has * specialized classes for location data. These classes are optimized for location data: they are more space-efficient and * support special operations such as <i>distance</i> and <i>polygon</i> queries. There are currently two implementations: * <br> * <ol> * <li>{@link LatLonPoint}: indexes {@code (latitude,longitude)} as {@code (x,y)} in two-dimensional space. * <li><a href="{@docRoot}/../spatial3d/org/apache/lucene/spatial3d/Geo3DPoint.html">Geo3DPoint</a>* in <i>lucene-spatial3d</i>: indexes {@code (latitude,longitude)} as {@code (x,y,z)} in three-dimensional space. * </ol> * * does <b>not</b> support altitude, 3D here means "uses three dimensions under-the-hood"<br> * <h1>Advanced usage</h1> * Custom structures can be created on top of single- or multi- dimensional basic types, on top of * {@link BinaryPoint} for more flexibility, or via custom {@link Field} subclasses. * * @lucene.experimental */ public abstract class PointValues { /** Maximum number of bytes for each dimension */ public static final int MAX_NUM_BYTES = 16; /** Maximum number of dimensions */ public static final int MAX_DIMENSIONS = BKDWriter.MAX_DIMS; /** Return the cumulated number of points across all leaves of the given * {@link IndexReader}. Leaves that do not have points for the given field * are ignored. * @see PointValues#size() */ public static long size(IndexReader reader, String field) throws IOException { long size = 0; for (LeafReaderContext ctx : reader.leaves()) { PointValues values = ctx.reader().getPointValues(field); if (values != null) { size += values.size(); } } return size; } /** Return the cumulated number of docs that have points across all leaves * of the given {@link IndexReader}. Leaves that do not have points for the * given field are ignored. * @see PointValues#getDocCount() */ public static int getDocCount(IndexReader reader, String field) throws IOException { int count = 0; for (LeafReaderContext ctx : reader.leaves()) { PointValues values = ctx.reader().getPointValues(field); if (values != null) { count += values.getDocCount(); } } return count; } /** Return the minimum packed values across all leaves of the given * {@link IndexReader}. Leaves that do not have points for the given field * are ignored. * @see PointValues#getMinPackedValue() */ public static byte[] getMinPackedValue(IndexReader reader, String field) throws IOException { byte[] minValue = null; for (LeafReaderContext ctx : reader.leaves()) { PointValues values = ctx.reader().getPointValues(field); if (values == null) { continue; } byte[] leafMinValue = values.getMinPackedValue(); if (leafMinValue == null) { continue; } if (minValue == null) { minValue = leafMinValue.clone(); } else { final int numDimensions = values.getNumIndexDimensions(); final int numBytesPerDimension = values.getBytesPerDimension(); for (int i = 0; i < numDimensions; ++i) { int offset = i * numBytesPerDimension; if (FutureArrays.compareUnsigned(leafMinValue, offset, offset + numBytesPerDimension, minValue, offset, offset + numBytesPerDimension) < 0) { System.arraycopy(leafMinValue, offset, minValue, offset, numBytesPerDimension); } } } } return minValue; } /** Return the maximum packed values across all leaves of the given * {@link IndexReader}. Leaves that do not have points for the given field * are ignored. * @see PointValues#getMaxPackedValue() */ public static byte[] getMaxPackedValue(IndexReader reader, String field) throws IOException { byte[] maxValue = null; for (LeafReaderContext ctx : reader.leaves()) { PointValues values = ctx.reader().getPointValues(field); if (values == null) { continue; } byte[] leafMaxValue = values.getMaxPackedValue(); if (leafMaxValue == null) { continue; } if (maxValue == null) { maxValue = leafMaxValue.clone(); } else { final int numDimensions = values.getNumIndexDimensions(); final int numBytesPerDimension = values.getBytesPerDimension(); for (int i = 0; i < numDimensions; ++i) { int offset = i * numBytesPerDimension; if (FutureArrays.compareUnsigned(leafMaxValue, offset, offset + numBytesPerDimension, maxValue, offset, offset + numBytesPerDimension) > 0) { System.arraycopy(leafMaxValue, offset, maxValue, offset, numBytesPerDimension); } } } } return maxValue; } /** Default constructor */ protected PointValues() { } /** Used by {@link #intersect} to check how each recursive cell corresponds to the query. */ public enum Relation { /** Return this if the cell is fully contained by the query */ CELL_INSIDE_QUERY, /** Return this if the cell and query do not overlap */ CELL_OUTSIDE_QUERY, /** Return this if the cell partially overlaps the query */ CELL_CROSSES_QUERY }; /** We recurse the BKD tree, using a provided instance of this to guide the recursion. * * @lucene.experimental */ public interface IntersectVisitor { /** Called for all documents in a leaf cell that's fully contained by the query. The * consumer should blindly accept the docID. */ void visit(int docID) throws IOException; /** Called for all documents in a leaf cell that crosses the query. The consumer * should scrutinize the packedValue to decide whether to accept it. In the 1D case, * values are visited in increasing order, and in the case of ties, in increasing * docID order. */ void visit(int docID, byte[] packedValue) throws IOException; /** Similar to {@link IntersectVisitor#visit(int, byte[])} but in this case the packedValue * can have more than one docID associated to it. The provided iterator should not escape the * scope of this method so that implementations of PointValues are free to reuse it,*/ default void visit(DocIdSetIterator iterator, byte[] packedValue) throws IOException { int docID; while ((docID = iterator.nextDoc()) != DocIdSetIterator.NO_MORE_DOCS) { visit(docID, packedValue); } } /** Called for non-leaf cells to test how the cell relates to the query, to * determine how to further recurse down the tree. */ Relation compare(byte[] minPackedValue, byte[] maxPackedValue); /** Notifies the caller that this many documents are about to be visited */ default void grow(int count) { }; } /** Finds all documents and points matching the provided visitor. * This method does not enforce live documents, so it's up to the caller * to test whether each document is deleted, if necessary. */ public abstract void intersect(IntersectVisitor visitor) throws IOException; /** Estimate the number of points that would be visited by {@link #intersect} * with the given {@link IntersectVisitor}. This should run many times faster * than {@link #intersect(IntersectVisitor)}. */ public abstract long estimatePointCount(IntersectVisitor visitor); /** Estimate the number of documents that would be matched by {@link #intersect} * with the given {@link IntersectVisitor}. This should run many times faster * than {@link #intersect(IntersectVisitor)}. * @see DocIdSetIterator#cost */ public long estimateDocCount(IntersectVisitor visitor) { long estimatedPointCount = estimatePointCount(visitor); int docCount = getDocCount(); double size = size(); if (estimatedPointCount >= size) { // math all docs return docCount; } else if (size == docCount || estimatedPointCount == 0L) { // if the point count estimate is 0 or we have only single values // return this estimate return estimatedPointCount; } else { // in case of multi values estimate the number of docs using the solution provided in // https://math.stackexchange.com/questions/1175295/urn-problem-probability-of-drawing-balls-of-k-unique-colors // then approximate the solution for points per doc << size() which results in the expression // D * (1 - ((N - n) / N)^(N/D)) // where D is the total number of docs, N the total number of points and n the estimated point count long docEstimate = (long) (docCount * (1d - Math.pow((size - estimatedPointCount) / size, size / docCount))); return docEstimate == 0L ? 1L : docEstimate; } } /** Returns minimum value for each dimension, packed, or null if {@link #size} is <code>0</code> */ public abstract byte[] getMinPackedValue() throws IOException; /** Returns maximum value for each dimension, packed, or null if {@link #size} is <code>0</code> */ public abstract byte[] getMaxPackedValue() throws IOException; /** Returns how many data dimensions are represented in the values */ public abstract int getNumDataDimensions() throws IOException; /** Returns how many dimensions are used for the index */ public abstract int getNumIndexDimensions() throws IOException; /** Returns the number of bytes per dimension */ public abstract int getBytesPerDimension() throws IOException; /** Returns the total number of indexed points across all documents. */ public abstract long size(); /** Returns the total number of documents that have indexed at least one point. */ public abstract int getDocCount(); }