Code to search indices.

See: Description

Package Description

Code to search indices.

Table Of Contents

  1. Search Basics
  2. The Query Classes
  3. Changing the Scoring


Search over indices. Applications usually call,int) or,Filter,int).

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. Every document in the result set will match all such clauses.

  3. 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 1024, but this can be changed via the static method setMaxClauseCount in BooleanQuery.


Another common search is to find documents containing certain phrases. This is handled two 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.

  2. 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 TermRangeQuery matches all documents that occur in the exclusive range of a lower Term and an upper Term. according to String.compareTo(String). It is not intended for numerical ranges, use NumericRangeQuery instead. For example, one could find all documents that have terms beginning with the letters a through c. This type of Query is frequently used to find documents that occur in a specific date range.


The NumericRangeQuery matches all documents that occur in a numeric range. For NumericRangeQuery to work, you must index the values using a special NumericField.

PrefixQuery, WildcardQuery

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. To remove this protection and allow a wildcard at the beginning of a term, see method setAllowLeadingWildcard in QueryParser.


A FuzzyQuery matches documents that contain terms similar to the specified term. Similarity is determined using Levenshtein (edit) distance. This type of query can be useful when accounting for spelling variations in the collection.

Changing Similarity

Chances are DefaultSimilarity is sufficient for all your searching needs. However, in some applications it may be necessary to customize your Similarity implementation. For instance, some applications do not need to distinguish between shorter and longer documents (see a "fair" similarity).

To change Similarity, one must do so for both indexing and searching, and the changes must happen before either of these actions take place. Although in theory there is nothing stopping you from changing mid-stream, it just isn't well-defined what is going to happen.

To make this change, implement your own Similarity (likely you'll want to simply subclass DefaultSimilarity) and then use the new class by calling IndexWriter.setSimilarity before indexing and Searcher.setSimilarity before searching.

If you are interested in use cases for changing your similarity, see the Lucene users's mailing list at Overriding Similarity. In summary, here are a few use cases:

  1. SweetSpotSimilaritySweetSpotSimilarity gives small increases as the frequency increases a small amount and then greater increases when you hit the "sweet spot", i.e. where you think the frequency of terms is more significant.

  2. Overriding tf — In some applications, it doesn't matter what the score of a document is as long as a matching term occurs. In these cases people have overridden Similarity to return 1 from the tf() method.

  3. Changing Length Normalization — By overriding lengthNorm, it is possible to discount how the length of a field contributes to a score. In DefaultSimilarity, lengthNorm = 1 / (numTerms in field)^0.5, but if one changes this to be 1 / (numTerms in field), all fields will be treated "fairly".

In general, Chris Hostetter sums it up best in saying (from the Lucene users's mailing list):
[One would override the Similarity in] ... any situation where you know more about your data then just that it's "text" is a situation where it *might* make sense to to override your Similarity method.

Changing Scoring — Expert Level

Changing scoring is 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 scoring in Lucene. Lucene's scoring 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 — The internal interface representation of the user's Query, so that Query objects may be reused.
  3. Scorer — An abstract class containing common functionality for scoring. Provides both scoring and explanation capabilities.
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(Searcher searcher) — 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 Query.html#createWeight(Searcher searcher)

The Weight Interface

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

  1. Weight#getQuery() — Pointer to the Query that this Weight represents.
  2. Weight#getValue() — The weight for this Query. For example, the TermQuery.TermWeight value is equal to the idf^2 * boost * queryNorm
  3. Weight#sumOfSquaredWeights() — The sum of squared weights. For TermQuery, this is (idf * boost)^2
  4. Weight#normalize(float) — Determine the query normalization factor. The query normalization may allow for comparing scores between queries.
  5. Weight#scorer(IndexReader, boolean, boolean) — 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.
  6. Weight#explain(Searcher, IndexReader, int) — Provide a means for explaining why a given document was scored the way it was.

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 abstract (some of them are not yet abstract, but will be in future versions and should be considered as such now) methods which must be implemented (some of them inherited from DocIdSetIterator ):

  1. DocIdSetIterator#nextDoc() — Advances to the next document that matches this Query, returning true if and only if there is another document that matches.
  2. DocIdSetIterator#docID() — Returns the id of the Document that contains the match. It is not valid until next() has been called at least once.
  3. Scorer#score(Collector) — Scores and collects all matching documents using the given Collector.
  4. Scorer#score() — Return the score of the current document. This value can be determined in any appropriate way for an application. For instance, the TermScorer returns the tf * Weight.getValue() * fieldNorm.
  5. DocIdSetIterator#advance(int) — Skip ahead in the document matches to the document whose id is greater than or equal to the passed in value. In many instances, advance can be implemented more efficiently than simply looping through all the matching documents until the target document is identified.

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).