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/* * 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. */ /** * Code to search indices. * * Table Of Contents * * Search Basics * The Query Classes * Scoring: Introduction * Scoring: Basics * Changing the Scoring * Appendix: Search Algorithm * * * * * Search Basics * * Lucene offers a wide variety of {@link org.apache.lucene.search.Query} implementations, most of which are in * this package, its subpackage ({@link org.apache.lucene.search.spans spans}, * or the queries module. These implementations can be combined in a wide * variety of ways to provide complex querying capabilities along with information about where matches took place in the document * collection. The Query Classes section below highlights some of the more important Query classes. For details * on implementing your own Query class, see Custom Queries -- Expert Level below. * * To perform a search, applications usually call {@link * org.apache.lucene.search.IndexSearcher#search(Query,int)}. * * Once a Query has been created and submitted to the {@link org.apache.lucene.search.IndexSearcher IndexSearcher}, the scoring * process begins. After some infrastructure setup, control finally passes to the {@link org.apache.lucene.search.Weight Weight} * implementation and its {@link org.apache.lucene.search.Scorer Scorer} or {@link org.apache.lucene.search.BulkScorer BulkScorer} * instances. See the Algorithm section for more notes on the process. * * * * * * Query Classes * * {@link org.apache.lucene.search.TermQuery TermQuery} * * * Of the various implementations of * {@link org.apache.lucene.search.Query Query}, the * {@link org.apache.lucene.search.TermQuery TermQuery} * is the easiest to understand and the most often used in applications. A * {@link org.apache.lucene.search.TermQuery TermQuery} matches all the documents that contain the * specified * {@link org.apache.lucene.index.Term Term}, * which is a word that occurs in a certain * {@link org.apache.lucene.document.Field Field}. * Thus, a {@link org.apache.lucene.search.TermQuery TermQuery} identifies and scores all * {@link org.apache.lucene.document.Document Document}s that have a * {@link org.apache.lucene.document.Field Field} with the specified string in it. * Constructing a {@link org.apache.lucene.search.TermQuery TermQuery} * is as simple as: * * TermQuery tq = new TermQuery(new Term("fieldName", "term")); * In this example, the {@link org.apache.lucene.search.Query Query} identifies all * {@link org.apache.lucene.document.Document Document}s that have the * {@link org.apache.lucene.document.Field Field} named "fieldName" * containing the word "term". * * {@link org.apache.lucene.search.BooleanQuery BooleanQuery} * * * Things start to get interesting when one combines multiple * {@link org.apache.lucene.search.TermQuery TermQuery} instances into a * {@link org.apache.lucene.search.BooleanQuery BooleanQuery}. * A {@link org.apache.lucene.search.BooleanQuery BooleanQuery} contains multiple * {@link org.apache.lucene.search.BooleanClause BooleanClause}s, * where each clause contains a sub-query ({@link org.apache.lucene.search.Query Query} * instance) and an operator (from * {@link org.apache.lucene.search.BooleanClause.Occur BooleanClause.Occur}) * describing how that sub-query is combined with the other clauses: * * * {@link org.apache.lucene.search.BooleanClause.Occur#SHOULD SHOULD} — Use this operator when a clause can occur in the result set, but is not required. * If a query is made up of all SHOULD clauses, then every document in the result * set matches at least one of these clauses. * * {@link org.apache.lucene.search.BooleanClause.Occur#MUST MUST} — Use this operator when a clause is required to occur in the result set and should * contribute to the score. Every document in the result set will match all such clauses. * * {@link org.apache.lucene.search.BooleanClause.Occur#FILTER FILTER} — Use this operator when a clause is required to occur in the result set but * should not contribute to the score. Every document in the result set will match all such clauses. * * {@link org.apache.lucene.search.BooleanClause.Occur#MUST_NOT MUST NOT} — Use this operator when a * clause must not occur in the result set. No * document in the result set will match * any such clauses. * * Boolean queries are constructed by adding two or more * {@link org.apache.lucene.search.BooleanClause BooleanClause} * instances. If too many clauses are added, a {@link org.apache.lucene.search.BooleanQuery.TooManyClauses TooManyClauses} * exception will be thrown during searching. This most often occurs * when a {@link org.apache.lucene.search.Query Query} * is rewritten into a {@link org.apache.lucene.search.BooleanQuery BooleanQuery} with many * {@link org.apache.lucene.search.TermQuery TermQuery} clauses, * for example by {@link org.apache.lucene.search.WildcardQuery WildcardQuery}. * The default setting for the maximum number * of clauses is 1024, but this can be changed via the * static method {@link org.apache.lucene.search.BooleanQuery#setMaxClauseCount(int)}. * * Phrases * * Another common search is to find documents containing certain phrases. This * is handled three different ways: * * * {@link org.apache.lucene.search.PhraseQuery PhraseQuery} * — Matches a sequence of * {@link org.apache.lucene.index.Term Term}s. * {@link org.apache.lucene.search.PhraseQuery PhraseQuery} uses a slop factor to determine * how many positions may occur between any two terms in the phrase and still be considered a match. * The slop is 0 by default, meaning the phrase must match exactly. * * * {@link org.apache.lucene.search.MultiPhraseQuery MultiPhraseQuery} * — A more general form of PhraseQuery that accepts multiple Terms * for a position in the phrase. For example, this can be used to perform phrase queries that also * incorporate synonyms. * * * {@link org.apache.lucene.search.spans.SpanNearQuery SpanNearQuery} * — Matches a sequence of other * {@link org.apache.lucene.search.spans.SpanQuery SpanQuery} * instances. {@link org.apache.lucene.search.spans.SpanNearQuery SpanNearQuery} allows for * much more * complicated phrase queries since it is constructed from other * {@link org.apache.lucene.search.spans.SpanQuery SpanQuery} * instances, instead of only {@link org.apache.lucene.search.TermQuery TermQuery} * instances. * * * * * {@link org.apache.lucene.search.PointRangeQuery PointRangeQuery} * * * The * {@link org.apache.lucene.search.PointRangeQuery PointRangeQuery} * matches all documents that occur in a numeric range. * For PointRangeQuery to work, you must index the values * using a one of the numeric fields ({@link org.apache.lucene.document.IntPoint IntPoint}, * {@link org.apache.lucene.document.LongPoint LongPoint}, {@link org.apache.lucene.document.FloatPoint FloatPoint}, * or {@link org.apache.lucene.document.DoublePoint DoublePoint}). * * * {@link org.apache.lucene.search.PrefixQuery PrefixQuery}, * {@link org.apache.lucene.search.WildcardQuery WildcardQuery}, * {@link org.apache.lucene.search.RegexpQuery RegexpQuery} * * * While the * {@link org.apache.lucene.search.PrefixQuery PrefixQuery} * has a different implementation, it is essentially a special case of the * {@link org.apache.lucene.search.WildcardQuery WildcardQuery}. * The {@link org.apache.lucene.search.PrefixQuery PrefixQuery} allows an application * to identify all documents with terms that begin with a certain string. The * {@link org.apache.lucene.search.WildcardQuery WildcardQuery} generalizes this by allowing * for the use of * (matches 0 or more characters) and ? (matches exactly one character) wildcards. * Note that the {@link org.apache.lucene.search.WildcardQuery WildcardQuery} can be quite slow. Also * note that * {@link org.apache.lucene.search.WildcardQuery WildcardQuery} should * not start with * and ?, as these are extremely slow. * Some QueryParsers may not allow this by default, but provide a setAllowLeadingWildcard method * to remove that protection. * The {@link org.apache.lucene.search.RegexpQuery RegexpQuery} is even more general than WildcardQuery, * allowing an application to identify all documents with terms that match a regular expression pattern. * * {@link org.apache.lucene.search.FuzzyQuery FuzzyQuery} * * * A * {@link org.apache.lucene.search.FuzzyQuery FuzzyQuery} * matches documents that contain terms similar to the specified term. Similarity is * determined using * Levenshtein distance. * This type of query can be useful when accounting for spelling variations in the collection. * * * * Scoring — Introduction * Lucene scoring is the heart of why we all love Lucene. It is blazingly fast and it hides * almost all of the complexity from the user. In a nutshell, it works. At least, that is, * until it doesn't work, or doesn't work as one would expect it to work. Then we are left * digging into Lucene internals or asking for help on * [email protected] to figure out * why a document with five of our query terms scores lower than a different document with * only one of the query terms. * While this document won't answer your specific scoring issues, it will, hopefully, point you * to the places that can help you figure out the what and why of Lucene scoring. * Lucene scoring supports a number of pluggable information retrieval * models, including: * * Vector Space Model (VSM) * Probabilistic Models such as * Okapi BM25 and * DFR * Language models * * These models can be plugged in via the {@link org.apache.lucene.search.similarities Similarity API}, * and offer extension hooks and parameters for tuning. In general, Lucene first finds the documents * that need to be scored based on boolean logic in the Query specification, and then ranks this subset of * matching documents via the retrieval model. For some valuable references on VSM and IR in general refer to * Lucene Wiki IR references. * The rest of this document will cover Scoring basics and explain how to * change your {@link org.apache.lucene.search.similarities.Similarity Similarity}. Next, it will cover * ways you can customize the lucene internals in * Custom Queries -- Expert Level, which gives details on * implementing your own {@link org.apache.lucene.search.Query Query} class and related functionality. * Finally, we will finish up with some reference material in the Appendix. * * * * Scoring — Basics * Scoring is very much dependent on the way documents are indexed, so it is important to understand * indexing. (see Lucene overview * before continuing on with this section) Be sure to use the useful * {@link org.apache.lucene.search.IndexSearcher#explain(org.apache.lucene.search.Query, int) IndexSearcher.explain(Query, doc)} * to understand how the score for a certain matching document was * computed. * * Generally, the Query determines which documents match (a binary * decision), while the Similarity determines how to assign scores to * the matching documents. * * * Fields and Documents * In Lucene, the objects we are scoring are {@link org.apache.lucene.document.Document Document}s. * A Document is a collection of {@link org.apache.lucene.document.Field Field}s. Each Field has * {@link org.apache.lucene.document.FieldType semantics} about how it is created and stored * ({@link org.apache.lucene.document.FieldType#tokenized() tokenized}, * {@link org.apache.lucene.document.FieldType#stored() stored}, etc). It is important to note that * Lucene scoring works on Fields and then combines the results to return Documents. This is * important because two Documents with the exact same content, but one having the content in two * Fields and the other in one Field may return different scores for the same query due to length * normalization. * Score Boosting * Lucene allows influencing the score contribution of various parts of the query by wrapping with * {@link org.apache.lucene.search.BoostQuery}. * * * Changing Scoring — Similarity * Changing the scoring formula * * Changing {@link org.apache.lucene.search.similarities.Similarity Similarity} is an easy way to * influence scoring, this is done at index-time with * {@link org.apache.lucene.index.IndexWriterConfig#setSimilarity(org.apache.lucene.search.similarities.Similarity) * IndexWriterConfig.setSimilarity(Similarity)} and at query-time with * {@link org.apache.lucene.search.IndexSearcher#setSimilarity(org.apache.lucene.search.similarities.Similarity) * IndexSearcher.setSimilarity(Similarity)}. Be sure to use the same * Similarity at query-time as at index-time (so that norms are * encoded/decoded correctly); Lucene makes no effort to verify this. * * You can influence scoring by configuring a different built-in Similarity implementation, or by tweaking its * parameters, subclassing it to override behavior. Some implementations also offer a modular API which you can * extend by plugging in a different component (e.g. term frequency normalizer). * * Finally, you can extend the low level {@link org.apache.lucene.search.similarities.Similarity Similarity} directly * to implement a new retrieval model. * * See the {@link org.apache.lucene.search.similarities} package documentation for information * on the built-in available scoring models and extending or changing Similarity. * * Integrating field values into the score * While similarities help score a document relatively to a query, it is also common for documents to hold * features that measure the quality of a match. Such features are best integrated into the score by indexing * a {@link org.apache.lucene.document.FeatureField FeatureField} with the document at index-time, and then * combining the similarity score and the feature score using a linear combination. For instance the below * query matches the same documents as {@code originalQuery} and computes scores as * {@code similarityScore + 0.7 * featureScore}: * * Query originalQuery = new BooleanQuery.Builder() * .add(new TermQuery(new Term("body", "apache")), Occur.SHOULD) * .add(new TermQuery(new Term("body", "lucene")), Occur.SHOULD) * .build(); * Query featureQuery = FeatureField.newSaturationQuery("features", "pagerank"); * Query query = new BooleanQuery.Builder() * .add(originalQuery, Occur.MUST) * .add(new BoostQuery(featureQuery, 0.7f), Occur.SHOULD) * .build(); * * * A less efficient yet more flexible way of modifying scores is to index scoring features into * doc-value fields and then combine them with the similarity score using a * FunctionScoreQuery * from the queries module. For instance * the below example shows how to compute scores as {@code similarityScore * Math.log(popularity)} * using the expressions module and * assuming that values for the {@code popularity} field have been set in a * {@link org.apache.lucene.document.NumericDocValuesField NumericDocValuesField} at index time: * * // compile an expression: * Expression expr = JavascriptCompiler.compile("_score * ln(popularity)"); * * // SimpleBindings just maps variables to SortField instances * SimpleBindings bindings = new SimpleBindings(); * bindings.add(new SortField("_score", SortField.Type.SCORE)); * bindings.add(new SortField("popularity", SortField.Type.INT)); * * // create a query that matches based on 'originalQuery' but * // scores using expr * Query query = new FunctionScoreQuery( * originalQuery, * expr.getDoubleValuesSource(bindings)); * * * * Custom Queries — Expert Level * * Custom queries are an expert level task, so tread carefully and be prepared to share your code if * you want help. * * With the warning out of the way, it is possible to change a lot more than just the Similarity * when it comes to matching and scoring in Lucene. Lucene's search is a complex mechanism that is grounded by * three main classes: * * * {@link org.apache.lucene.search.Query Query} — The abstract object representation of the * user's information need. * * {@link org.apache.lucene.search.Weight Weight} — A specialization of a Query for a given * index. This typically associates a Query object with index statistics that are later used to * compute document scores. * * {@link org.apache.lucene.search.Scorer Scorer} — The core class of the scoring process: * for a given segment, scorers return {@link org.apache.lucene.search.Scorer#iterator iterators} * over matches and give a way to compute the {@link org.apache.lucene.search.Scorer#score score} * of these matches. * * {@link org.apache.lucene.search.BulkScorer BulkScorer} — An abstract class that scores * a range of documents. A default implementation simply iterates through the hits from * {@link org.apache.lucene.search.Scorer Scorer}, but some queries such as * {@link org.apache.lucene.search.BooleanQuery BooleanQuery} have more efficient * implementations. * * Details on each of these classes, and their children, can be found in the subsections below. * The Query Class * In some sense, the * {@link org.apache.lucene.search.Query Query} * class is where it all begins. Without a Query, there would be * nothing to score. Furthermore, the Query class is the catalyst for the other scoring classes as it * is often responsible * for creating them or coordinating the functionality between them. The * {@link org.apache.lucene.search.Query Query} class has several methods that are important for * derived classes: * * {@link org.apache.lucene.search.Query#createWeight(IndexSearcher,ScoreMode,float) createWeight(IndexSearcher searcher, ScoreMode scoreMode, float boost)} — A * {@link org.apache.lucene.search.Weight Weight} is the internal representation of the * Query, so each Query implementation must * provide an implementation of Weight. See the subsection on The Weight Interface below for details on implementing the Weight * interface. * {@link org.apache.lucene.search.Query#rewrite(org.apache.lucene.index.IndexReader) rewrite(IndexReader reader)} — Rewrites queries into primitive queries. Primitive queries are: * {@link org.apache.lucene.search.TermQuery TermQuery}, * {@link org.apache.lucene.search.BooleanQuery BooleanQuery}, and other queries that implement {@link org.apache.lucene.search.Query#createWeight(IndexSearcher,ScoreMode,float) createWeight(IndexSearcher searcher,ScoreMode scoreMode, float boost)} * * * The Weight Interface * The * {@link org.apache.lucene.search.Weight Weight} * interface provides an internal representation of the Query so that it can be reused. Any * {@link org.apache.lucene.search.IndexSearcher IndexSearcher} * dependent state should be stored in the Weight implementation, * not in the Query class. The interface defines four main methods: * * * {@link org.apache.lucene.search.Weight#scorer scorer()} — * Construct a new {@link org.apache.lucene.search.Scorer Scorer} for this Weight. See The Scorer Class * below for help defining a Scorer. As the name implies, the Scorer is responsible for doing the actual scoring of documents * given the Query. * * * {@link org.apache.lucene.search.Weight#explain(org.apache.lucene.index.LeafReaderContext, int) * explain(LeafReaderContext context, int doc)} — Provide a means for explaining why a given document was * scored the way it was. * Typically a weight such as TermWeight * that scores via a {@link org.apache.lucene.search.similarities.Similarity Similarity} will make use of the Similarity's implementation: * {@link org.apache.lucene.search.similarities.Similarity.SimScorer#explain(Explanation, long) SimScorer#explain(Explanation freq, long norm)}. * * * {@link org.apache.lucene.search.Weight#extractTerms(java.util.Set) extractTerms(Set<Term> terms)} — Extract terms that * this query operates on. This is typically used to support distributed search: knowing the terms that a query operates on helps * merge index statistics of these terms so that scores are computed over a subset of the data like they would if all documents * were in the same index. * * * {@link org.apache.lucene.search.Weight#matches matches(LeafReaderContext context, int doc)} — Give information about positions * and offsets of matches. This is typically useful to implement highlighting. * * * * The Scorer Class * The * {@link org.apache.lucene.search.Scorer Scorer} * abstract class provides common scoring functionality for all Scorer implementations and * is the heart of the Lucene scoring process. The Scorer defines the following methods which * must be implemented: * * * {@link org.apache.lucene.search.Scorer#iterator iterator()} — Return a * {@link org.apache.lucene.search.DocIdSetIterator DocIdSetIterator} that can iterate over all * document that matches this Query. * * * {@link org.apache.lucene.search.Scorer#docID docID()} — Returns the id of the * {@link org.apache.lucene.document.Document Document} that contains the match. * * * {@link org.apache.lucene.search.Scorer#score score()} — Return the score of the * current document. This value can be determined in any appropriate way for an application. For instance, the * {@link org.apache.lucene.search.TermScorer TermScorer} simply defers to the configured Similarity: * {@link org.apache.lucene.search.similarities.Similarity.SimScorer#score(float, long) SimScorer.score(float freq, long norm)}. * * * {@link org.apache.lucene.search.Scorer#getChildren getChildren()} — Returns any child subscorers * underneath this scorer. This allows for users to navigate the scorer hierarchy and receive more fine-grained * details on the scoring process. * * * * The BulkScorer Class * The * {@link org.apache.lucene.search.BulkScorer BulkScorer} scores a range of documents. There is only one * abstract method: * * * {@link org.apache.lucene.search.BulkScorer#score(org.apache.lucene.search.LeafCollector,org.apache.lucene.util.Bits,int,int) score(LeafCollector,Bits,int,int)} — * Score all documents up to but not including the specified max document. * * * Why would I want to add my own Query? * * In a nutshell, you want to add your own custom Query implementation when you think that Lucene's * aren't appropriate for the * task that you want to do. You might be doing some cutting edge research or you need more information * back * out of Lucene (similar to Doug adding SpanQuery functionality). * * * * * * Appendix: Search Algorithm * This section is mostly notes on stepping through the Scoring process and serves as * fertilizer for the earlier sections. * In the typical search application, a {@link org.apache.lucene.search.Query Query} * is passed to the {@link org.apache.lucene.search.IndexSearcher IndexSearcher}, * beginning the scoring process. * Once inside the IndexSearcher, a {@link org.apache.lucene.search.Collector Collector} * is used for the scoring and sorting of the search results. * These important objects are involved in a search: * * The {@link org.apache.lucene.search.Weight Weight} object of the Query. The * Weight object is an internal representation of the Query that allows the Query * to be reused by the IndexSearcher. * The IndexSearcher that initiated the call. * A {@link org.apache.lucene.search.Sort Sort} object for specifying how to sort * the results if the standard score-based sort method is not desired. * * Assuming we are not sorting (since sorting doesn't affect the raw Lucene score), * we call one of the search methods of the IndexSearcher, passing in the * {@link org.apache.lucene.search.Weight Weight} object created by * {@link org.apache.lucene.search.IndexSearcher#createWeight(org.apache.lucene.search.Query,ScoreMode,float) * IndexSearcher.createWeight(Query,ScoreMode,float)} and the number of results we want. * This method returns a {@link org.apache.lucene.search.TopDocs TopDocs} object, * which is an internal collection of search results. The IndexSearcher creates * a {@link org.apache.lucene.search.TopScoreDocCollector TopScoreDocCollector} and * passes it along with the Weight to another expert search method (for * more on the {@link org.apache.lucene.search.Collector Collector} mechanism, * see {@link org.apache.lucene.search.IndexSearcher IndexSearcher}). The TopScoreDocCollector * uses a {@link org.apache.lucene.util.PriorityQueue PriorityQueue} to collect the * top results for the search. * At last, we are actually going to score some documents. The score method takes in the Collector * (most likely the TopScoreDocCollector or TopFieldCollector) and does its business. Of course, here * is where things get involved. The {@link org.apache.lucene.search.Scorer Scorer} that is returned * by the {@link org.apache.lucene.search.Weight Weight} object depends on what type of Query was * submitted. In most real world applications with multiple query terms, the * {@link org.apache.lucene.search.Scorer Scorer} is going to be a BooleanScorer2 created * from {@link org.apache.lucene.search.BooleanWeight BooleanWeight} (see the section on * custom queries for info on changing this). *

Assuming a BooleanScorer2, we get a internal Scorer based on the required, optional and prohibited parts of the query. * Using this internal Scorer, the BooleanScorer2 then proceeds into a while loop based on the * {@link org.apache.lucene.search.DocIdSetIterator#nextDoc DocIdSetIterator.nextDoc()} method. The nextDoc() method advances * to the next document matching the query. This is an abstract method in the Scorer class and is thus * overridden by all derived implementations. If you have a simple OR query your internal Scorer is most * likely a DisjunctionSumScorer, which essentially combines the scorers from the sub scorers of the OR'd terms. */ package org.apache.lucene.search;