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Code to search indices.

See: Description

Package Description

Code to search indices.

Table Of Contents

  1. Search Basics
  2. The Query Classes
  3. Scoring: Introduction
  4. Scoring: Basics
  5. Changing the Scoring
  6. Appendix: Search Algorithm

Search Basics

Lucene offers a wide variety of Query implementations, most of which are in this package, its subpackage (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,int).

Once a Query has been created and submitted to the IndexSearcher, the scoring process begins. After some infrastructure setup, control finally passes to the Weight implementation and its Scorer or BulkScorer instances. See the Algorithm section for more notes on the process.

Query Classes


Of the various implementations of Query, the TermQuery is the easiest to understand and the most often used in applications. A TermQuery matches all the documents that contain the specified Term, which is a word that occurs in a certain Field. Thus, a TermQuery identifies and scores all Documents that have a Field with the specified string in it. Constructing a TermQuery is as simple as:

         TermQuery tq = new TermQuery(new Term("fieldName", "term"));
In this example, the Query identifies all Documents that have the Field named "fieldName" containing the word "term".


Things start to get interesting when one combines multiple TermQuery instances into a BooleanQuery. A BooleanQuery contains multiple BooleanClauses, where each clause contains a sub-query (Query instance) and an operator (from BooleanClause.Occur) describing how that sub-query is combined with the other clauses:

  1. 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.

  2. 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.

  3. 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.

  4. 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 BooleanClause instances. If too many clauses are added, a TooManyClauses exception will be thrown during searching. This most often occurs when a Query is rewritten into a BooleanQuery with many TermQuery clauses, for example by WildcardQuery. The default setting for the maximum number of clauses is 1024, but this can be changed via the static method BooleanQuery.setMaxClauseCount(int).


Another common search is to find documents containing certain phrases. This is handled three different ways:

  1. PhraseQuery — Matches a sequence of Terms. 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.

  2. 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.

  3. SpanNearQuery — Matches a sequence of other SpanQuery instances. SpanNearQuery allows for much more complicated phrase queries since it is constructed from other SpanQuery instances, instead of only TermQuery instances.


The 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 (IntPoint, LongPoint, FloatPoint, or DoublePoint).

PrefixQuery, WildcardQuery, RegexpQuery

While the PrefixQuery has a different implementation, it is essentially a special case of the WildcardQuery. The PrefixQuery allows an application to identify all documents with terms that begin with a certain string. The WildcardQuery generalizes this by allowing for the use of * (matches 0 or more characters) and ? (matches exactly one character) wildcards. Note that the WildcardQuery can be quite slow. Also note that 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 RegexpQuery is even more general than WildcardQuery, allowing an application to identify all documents with terms that match a regular expression pattern.


A 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 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:

These models can be plugged in via the 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 Similarity. Next, it will cover ways you can customize the lucene internals in Custom Queries -- Expert Level, which gives details on implementing your own 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 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 Documents. A Document is a collection of Fields. Each Field has semantics about how it is created and stored (tokenized, 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 BoostQuery.

Changing Scoring — Similarity

Changing the scoring formula

Changing Similarity is an easy way to influence scoring, this is done at index-time with IndexWriterConfig.setSimilarity(Similarity) and at query-time with 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 Similarity directly to implement a new retrieval model.

See the 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 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 originalQuery and computes scores as 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)
 Query featureQuery = FeatureField.newSaturationQuery("features", "pagerank");
 Query query = new BooleanQuery.Builder()
     .add(originalQuery, Occur.MUST)
     .add(new BoostQuery(featureQuery, 0.7f), Occur.SHOULD)

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 similarityScore * Math.log(popularity) using the expressions module and assuming that values for the popularity field have been set in a 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(

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:

  1. Query — The abstract object representation of the user's information need.
  2. 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.
  3. Scorer — The core class of the scoring process: for a given segment, scorers return iterators over matches and give a way to compute the score of these matches.
  4. BulkScorer — An abstract class that scores a range of documents. A default implementation simply iterates through the hits from Scorer, but some queries such as 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 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 Query class has several methods that are important for derived classes:

  1. createWeight(IndexSearcher searcher, ScoreMode scoreMode, float boost) — A 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.
  2. rewrite(IndexReader reader) — Rewrites queries into primitive queries. Primitive queries are: TermQuery, BooleanQuery, and other queries that implement createWeight(IndexSearcher searcher,ScoreMode scoreMode, float boost)

The Weight Interface

The Weight interface provides an internal representation of the Query so that it can be reused. Any IndexSearcher dependent state should be stored in the Weight implementation, not in the Query class. The interface defines four main methods:

  1. scorer() — Construct a new 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.
  2. 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 Similarity will make use of the Similarity's implementation: SimScorer#explain(Explanation freq, long norm).
  3. 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.
  4. matches(LeafReaderContext context, int doc) — Give information about positions and offsets of matches. This is typically useful to implement highlighting.

The Scorer Class

The 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:

  1. iterator() — Return a DocIdSetIterator that can iterate over all document that matches this Query.
  2. docID() — Returns the id of the Document that contains the match.
  3. score() — Return the score of the current document. This value can be determined in any appropriate way for an application. For instance, the TermScorer simply defers to the configured Similarity: SimScorer.score(float freq, long norm).
  4. 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 BulkScorer scores a range of documents. There is only one abstract method:

  1. 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 Query is passed to the IndexSearcher, beginning the scoring process.

Once inside the IndexSearcher, a Collector is used for the scoring and sorting of the search results. These important objects are involved in a search:

  1. The 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.
  2. The IndexSearcher that initiated the call.
  3. A 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 Weight object created by IndexSearcher.createWeight(Query,ScoreMode,float) and the number of results we want. This method returns a TopDocs object, which is an internal collection of search results. The IndexSearcher creates a TopScoreDocCollector and passes it along with the Weight to another expert search method (for more on the Collector mechanism, see IndexSearcher). The TopScoreDocCollector uses a 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 Scorer that is returned by the Weight object depends on what type of Query was submitted. In most real world applications with multiple query terms, the Scorer is going to be a BooleanScorer2 created from 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 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.

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