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 {@link org.apache.lucene.search.Query} implementations, most of which are in this package, its subpackages ({@link org.apache.lucene.search.spans spans}, {@link org.apache.lucene.search.payloads payloads}), 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)} or {@link org.apache.lucene.search.IndexSearcher#search(Query,Filter,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} 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:

1. {@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.

2. {@link org.apache.lucene.search.BooleanClause.Occur#MUST 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. {@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 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:

1. {@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.

2. {@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.

3. {@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.TermRangeQuery TermRangeQuery}

The {@link org.apache.lucene.search.TermRangeQuery TermRangeQuery} matches all documents that occur in the exclusive range of a lower {@link org.apache.lucene.index.Term Term} and an upper {@link org.apache.lucene.index.Term Term} according to {@link org.apache.lucene.index.TermsEnum#getComparator TermsEnum.getComparator()}. It is not intended for numerical ranges; use {@link org.apache.lucene.search.NumericRangeQuery NumericRangeQuery} instead. For example, one could find all documents that have terms beginning with the letters `a` through `c`.

#### {@link org.apache.lucene.search.NumericRangeQuery NumericRangeQuery}

The {@link org.apache.lucene.search.NumericRangeQuery NumericRangeQuery} matches all documents that occur in a numeric range. For NumericRangeQuery to work, you must index the values using a one of the numeric fields ({@link org.apache.lucene.document.IntField IntField}, {@link org.apache.lucene.document.LongField LongField}, {@link org.apache.lucene.document.FloatField FloatField}, or {@link org.apache.lucene.document.DoubleField DoubleField}).

#### {@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 (edit) 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 java-user@lucene.apache.org 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)
- Probablistic 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 search results by "boosting" at different times:

- Index-time boost by calling {@link org.apache.lucene.document.Field#setBoost(float) Field.setBoost()} before a document is added to the index.
- Query-time boost by setting a boost on a query clause, calling {@link org.apache.lucene.search.Query#setBoost(float) Query.setBoost()}.

Indexing time boosts are pre-processed for storage efficiency and written to storage for a field as follows:

- All boosts of that field (i.e. all boosts under the same field name in that doc) are multiplied.
- The boost is then encoded into a normalization value by the Similarity object at index-time: {@link org.apache.lucene.search.similarities.Similarity#computeNorm computeNorm()}. The actual encoding depends upon the Similarity implementation, but note that most use a lossy encoding (such as multiplying the boost with document length or similar, packed into a single byte!).
- Decoding of any index-time normalization values and integration into the document's score is also performed at search time by the Similarity.

## Changing Scoring — Similarity

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, or to use external scoring factors particular to your application. For example, a custom Similarity can access per-document values via {@link org.apache.lucene.search.FieldCache FieldCache} or {@link org.apache.lucene.index.NumericDocValues} and integrate them into the score.

See the {@link org.apache.lucene.search.similarities} package documentation for information on the built-in available scoring models and extending or changing Similarity.

## 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. {@link org.apache.lucene.search.Query Query} — The abstract object representation of the user's information need.
2. {@link org.apache.lucene.search.Weight Weight} — The internal interface representation of the user's Query, so that Query objects may be reused. This is global (across all segments of the index) and generally will require global statistics (such as docFreq for a given term across all segments).
3. {@link org.apache.lucene.search.Scorer Scorer} — An abstract class containing common functionality for scoring. Provides both scoring and explanation capabilities. This is created per-segment.

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:

1. {@link org.apache.lucene.search.Query#createWeight(IndexSearcher) createWeight(IndexSearcher searcher)} — 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.
2. {@link org.apache.lucene.search.Query#rewrite(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) createWeight(IndexSearcher searcher)}

#### 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 five methods that must be implemented:

1. {@link org.apache.lucene.search.Weight#getQuery getQuery()} — Pointer to the Query that this Weight represents.
2. {@link org.apache.lucene.search.Weight#getValueForNormalization() getValueForNormalization()} — A weight can return a floating point value to indicate its magnitude for query normalization. Typically a weight such as TermWeight that scores via a {@link org.apache.lucene.search.similarities.Similarity Similarity} will just defer to the Similarity's implementation: {@link org.apache.lucene.search.similarities.Similarity.SimWeight#getValueForNormalization SimWeight#getValueForNormalization()}. For example, with {@link org.apache.lucene.search.similarities.TFIDFSimilarity Lucene's classic vector-space formula}, this is implemented as the sum of squared weights: `(idf * boost)2`
3. {@link org.apache.lucene.search.Weight#normalize(float,float) normalize(float norm, float topLevelBoost)} — Performs query normalization:
   - `topLevelBoost`: A query-boost factor from any wrapping queries that should be multiplied into every document's score. For example, a TermQuery that is wrapped within a BooleanQuery with a boost of `5` would receive this value at this time. This allows the TermQuery (the leaf node in this case) to compute this up-front a single time (e.g. by multiplying into the IDF), rather than for every document.
   - `norm`: Passes in a a normalization factor which may allow for comparing scores between queries.
   Typically a weight such as TermWeight that scores via a {@link org.apache.lucene.search.similarities.Similarity Similarity} will just defer to the Similarity's implementation: {@link org.apache.lucene.search.similarities.Similarity.SimWeight#normalize SimWeight#normalize(float,float)}.
4. {@link org.apache.lucene.search.Weight#scorer(org.apache.lucene.index.AtomicReaderContext, boolean, boolean, org.apache.lucene.util.Bits) scorer(AtomicReaderContext context, boolean scoresDocsInOrder, boolean topScorer, Bits acceptDocs)} — 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.
5. {@link org.apache.lucene.search.Weight#explain(org.apache.lucene.index.AtomicReaderContext, int) explain(AtomicReaderContext 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(int, Explanation) SimScorer#explain(int doc, Explanation freq)}.

#### 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 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 {@link org.apache.lucene.search.DocIdSetIterator DocIdSetIterator}):

1. {@link org.apache.lucene.search.Scorer#nextDoc nextDoc()} — Advances to the next document that matches this Query, returning true if and only if there is another document that matches.
2. {@link org.apache.lucene.search.Scorer#docID docID()} — Returns the id of the {@link org.apache.lucene.document.Document Document} that contains the match.
3. {@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(int, float) SimScorer.score(int doc, float freq)}.
4. {@link org.apache.lucene.search.Scorer#freq freq()} — Returns the number of matches for 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 term frequency from the inverted index: {@link org.apache.lucene.index.DocsEnum#freq DocsEnum.freq()}.
5. {@link org.apache.lucene.search.Scorer#advance advance()} — 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.
6. {@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.

#### 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:

1. 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.
2. The IndexSearcher that initiated the call.
3. A {@link org.apache.lucene.search.Filter Filter} for limiting the result set. Note, the Filter may be null.
4. 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#createNormalizedWeight(org.apache.lucene.search.Query) IndexSearcher.createNormalizedWeight(Query)}, {@link org.apache.lucene.search.Filter Filter} 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, Filter 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.

If a Filter is being used, some initial setup is done to determine which docs to include. Otherwise, we ask the Weight for a {@link org.apache.lucene.search.Scorer Scorer} for each {@link org.apache.lucene.index.IndexReader IndexReader} segment and proceed by calling {@link org.apache.lucene.search.Scorer#score(org.apache.lucene.search.Collector) Scorer.score()}.

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.BooleanQuery.BooleanWeight BooleanWeight} (see the section on custom queries for info on changing this).

Assuming a BooleanScorer2, we first initialize the Coordinator, which is used to apply the coord() factor. We then 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.Scorer#nextDoc Scorer.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.