Package org.apache.lucene.search
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 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 IndexSearcher.search(Query,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
TermQuery
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
Document
s 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
Document
s that have the
Field
named "fieldName"
containing the word "term".
BooleanQuery
Things start to get interesting when one combines multiple
TermQuery
instances into a
BooleanQuery
.
A BooleanQuery
contains multiple
BooleanClause
s,
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:
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.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.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.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.
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)
.
Phrases
Another common search is to find documents containing certain phrases. This is handled three different ways:
-
PhraseQuery
— Matches a sequence ofTerm
s.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. -
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. -
SpanNearQuery
— Matches a sequence of otherSpanQuery
instances.SpanNearQuery
allows for much more complicated phrase queries since it is constructed from otherSpanQuery
instances, instead of onlyTermQuery
instances.
PointRangeQuery
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.
FuzzyQuery
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 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:
These models can be plugged in via theSimilarity 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 Document
s.
A Document is a collection of Field
s. 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 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 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) .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 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( 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:
-
Query
— The abstract object representation of the user's information need. -
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. -
Scorer
— The core class of the scoring process: for a given segment, scorers returniterators
over matches and give a way to compute thescore
of these matches. -
BulkScorer
— An abstract class that scores a range of documents. A default implementation simply iterates through the hits fromScorer
, but some queries such asBooleanQuery
have more efficient implementations.
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:
createWeight(IndexSearcher searcher, ScoreMode scoreMode, float boost)
— AWeight
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.rewrite(IndexReader reader)
— Rewrites queries into primitive queries. Primitive queries are:TermQuery
,BooleanQuery
, and other queries that implementcreateWeight(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:
-
scorer()
— Construct a newScorer
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. -
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 aSimilarity
will make use of the Similarity's implementation:SimScorer#explain(Explanation freq, long norm)
. -
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. -
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:
-
iterator()
— Return aDocIdSetIterator
that can iterate over all document that matches this Query. -
docID()
— Returns the id of theDocument
that contains the match. -
score()
— Return the score of the current document. This value can be determined in any appropriate way for an application. For instance, theTermScorer
simply defers to the configured Similarity:SimScorer.score(float freq, long norm)
. -
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:
-
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:
- 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. - The IndexSearcher that initiated the call.
- 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.
-
Interface Summary Interface Description BoostAttribute Add thisAttribute
to aTermsEnum
returned byMultiTermQuery.getTermsEnum(Terms,AttributeSource)
and update the boost on each returned term.Collector Expert: Collectors are primarily meant to be used to gather raw results from a search, and implement sorting or custom result filtering, collation, etc.CollectorManager<C extends Collector,T> A manager of collectors.LeafCollector Collector decouples the score from the collected doc: the score computation is skipped entirely if it's not needed.LeafFieldComparator Expert: comparator that gets instantiated on each leaf from a top-levelFieldComparator
instance.Matches Reports the positions and optionally offsets of all matching terms in a query for a single document To obtain aMatchesIterator
for a particular field, callMatches.getMatches(String)
.MatchesIterator An iterator over match positions (and optionally offsets) for a single document and field To iterate over the matches, callMatchesIterator.next()
until it returnsfalse
, retrieving positions and/or offsets after each call.MaxNonCompetitiveBoostAttribute Add thisAttribute
to a freshAttributeSource
before callingMultiTermQuery.getTermsEnum(Terms,AttributeSource)
.QueryCache A cache for queries.QueryCachingPolicy A policy defining which filters should be cached.ReferenceManager.RefreshListener Use to receive notification when a refresh has finished.SearcherLifetimeManager.Pruner SegmentCacheable Interface defining whether or not an object can be cached against aLeafReader
Objects that depend only on segment-immutable structures such as Points or postings lists can just returntrue
fromSegmentCacheable.isCacheable(LeafReaderContext)
Objects that depend on doc values should returnDocValues.isCacheable(LeafReaderContext, String...)
, which will check to see if the doc values fields have been updated. -
Class Summary Class Description AutomatonQuery AQuery
that will match terms against a finite-state machine.BlendedTermQuery AQuery
that blends index statistics across multiple terms.BlendedTermQuery.Builder A Builder forBlendedTermQuery
.BlendedTermQuery.DisjunctionMaxRewrite ABlendedTermQuery.RewriteMethod
that creates aDisjunctionMaxQuery
out of the sub queries.BlendedTermQuery.RewriteMethod ABlendedTermQuery.RewriteMethod
defines how queries for individual terms should be merged.BlockMaxDISI DocIdSetIterator
that skips non-competitive docs by checking the max score of the providedScorer
for the current block.BooleanClause A clause in a BooleanQuery.BooleanQuery A Query that matches documents matching boolean combinations of other queries, e.g.BooleanQuery.Builder A builder for boolean queries.BoostAttributeImpl Implementation class forBoostAttribute
.BoostQuery AQuery
wrapper that allows to give a boost to the wrapped query.BulkScorer This class is used to score a range of documents at once, and is returned byWeight.bulkScorer(org.apache.lucene.index.LeafReaderContext)
.CachingCollector Caches all docs, and optionally also scores, coming from a search, and is then able to replay them to another collector.CollectionStatistics Contains statistics for a collection (field).ConjunctionDISI A conjunction of DocIdSetIterators.ConstantScoreQuery A query that wraps another query and simply returns a constant score equal to 1 for every document that matches the query.ConstantScoreQuery.ConstantBulkScorer We return this as ourBulkScorer
so that if the CSQ wraps a query with its own optimized top-level scorer (e.g.ConstantScoreScorer A constant-scoringScorer
.ConstantScoreWeight A Weight that has a constant score equal to the boost of the wrapped query.ControlledRealTimeReopenThread<T> Utility class that runs a thread to manage periodicc reopens of aReferenceManager
, with methods to wait for a specific index changes to become visible.DisiPriorityQueue A priority queue of DocIdSetIterators that orders by current doc ID.DisiWrapper Wrapper used inDisiPriorityQueue
.DisjunctionDISIApproximation ADocIdSetIterator
which is a disjunction of the approximations of the provided iterators.DisjunctionMaxQuery A query that generates the union of documents produced by its subqueries, and that scores each document with the maximum score for that document as produced by any subquery, plus a tie breaking increment for any additional matching subqueries.DocIdSet A DocIdSet contains a set of doc ids.DocIdSetIterator This abstract class defines methods to iterate over a set of non-decreasing doc ids.DocValuesFieldExistsQuery AQuery
that matches documents that have a value for a given field as reported by doc values iterators.DocValuesRewriteMethod Rewrites MultiTermQueries into a filter, using DocValues for term enumeration.DoubleValues Per-segment, per-document double values, which can be calculated at search-timeDoubleValuesSource Base class for producingDoubleValues
To obtain aDoubleValues
object for a leaf reader, clients should callDoubleValuesSource.rewrite(IndexSearcher)
against the top-level searcher, and then callDoubleValuesSource.getValues(LeafReaderContext, DoubleValues)
on the resulting DoubleValuesSource.Explanation Expert: Describes the score computation for document and query.FieldComparator<T> Expert: a FieldComparator compares hits so as to determine their sort order when collecting the top results withTopFieldCollector
.FieldComparator.DocComparator Sorts by ascending docIDFieldComparator.DoubleComparator Parses field's values as double (usingLeafReader.getNumericDocValues(java.lang.String)
and sorts by ascending valueFieldComparator.FloatComparator Parses field's values as float (usingLeafReader.getNumericDocValues(String)
and sorts by ascending valueFieldComparator.IntComparator Parses field's values as int (usingLeafReader.getNumericDocValues(String)
and sorts by ascending valueFieldComparator.LongComparator Parses field's values as long (usingLeafReader.getNumericDocValues(String)
and sorts by ascending valueFieldComparator.NumericComparator<T extends Number> Base FieldComparator class for numeric typesFieldComparator.RelevanceComparator Sorts by descending relevance.FieldComparator.TermOrdValComparator Sorts by field's natural Term sort order, using ordinals.FieldComparator.TermValComparator Sorts by field's natural Term sort order.FieldComparatorSource Provides aFieldComparator
for custom field sorting.FieldDoc Expert: A ScoreDoc which also contains information about how to sort the referenced document.FieldValueHitQueue<T extends FieldValueHitQueue.Entry> Expert: A hit queue for sorting by hits by terms in more than one field.FieldValueHitQueue.Entry Extension of ScoreDoc to also store theFieldComparator
slot.FilterCollector Collector
delegator.FilteredDocIdSetIterator Abstract decorator class of a DocIdSetIterator implementation that provides on-demand filter/validation mechanism on an underlying DocIdSetIterator.FilterLeafCollector LeafCollector
delegator.FilterMatchesIterator A MatchesIterator that delegates all calls to another MatchesIteratorFilterScorable Filter aScorable
, intercepting methods and optionally changing their return values The default implementation simply passes all calls to its delegate, with the exception ofScorable.setMinCompetitiveScore(float)
which defaults to a no-op.FilterScorer AFilterScorer
contains anotherScorer
, which it uses as its basic source of data, possibly transforming the data along the way or providing additional functionality.FilterWeight AFilterWeight
contains anotherWeight
and implements all abstract methods by calling the contained weight's method.FuzzyQuery Implements the fuzzy search query.FuzzyTermsEnum Subclass of TermsEnum for enumerating all terms that are similar to the specified filter term.ImpactsDISI DocIdSetIterator
that skips non-competitive docs thanks to the indexed impacts.IndexOrDocValuesQuery A query that uses either an index structure (points or terms) or doc values in order to run a query, depending which one is more efficient.IndexSearcher Implements search over a single IndexReader.IndexSearcher.LeafSlice A class holding a subset of theIndexSearcher
s leaf contexts to be executed within a single thread.LeafSimScorer Similarity.SimScorer
on a specificLeafReader
.LiveFieldValues<S,T> Tracks live field values across NRT reader reopens.LongValues Per-segment, per-document long values, which can be calculated at search-timeLongValuesSource Base class for producingLongValues
To obtain aLongValues
object for a leaf reader, clients should callLongValuesSource.rewrite(IndexSearcher)
against the top-level searcher, and thenLongValuesSource.getValues(LeafReaderContext, DoubleValues)
.LRUQueryCache AQueryCache
that evicts queries using a LRU (least-recently-used) eviction policy in order to remain under a given maximum size and number of bytes used.MatchAllDocsQuery A query that matches all documents.MatchesUtils Contains static functions that aid the implementation ofMatches
andMatchesIterator
interfaces.MatchNoDocsQuery A query that matches no documents.MaxNonCompetitiveBoostAttributeImpl Implementation class forMaxNonCompetitiveBoostAttribute
.MultiCollector MultiCollectorManager ACollectorManager
implements which wrap a set ofCollectorManager
asMultiCollector
acts forCollector
.MultiPhraseQuery A generalized version ofPhraseQuery
, with the possibility of adding more than one term at the same position that are treated as a disjunction (OR).MultiPhraseQuery.Builder A builder for multi-phrase queriesMultiTermQuery An abstractQuery
that matches documents containing a subset of terms provided by aFilteredTermsEnum
enumeration.MultiTermQuery.RewriteMethod Abstract class that defines how the query is rewritten.MultiTermQuery.TopTermsBlendedFreqScoringRewrite A rewrite method that first translates each term intoBooleanClause.Occur.SHOULD
clause in a BooleanQuery, but adjusts the frequencies used for scoring to be blended across the terms, otherwise the rarest term typically ranks highest (often not useful eg in the set of expanded terms in a FuzzyQuery).MultiTermQuery.TopTermsBoostOnlyBooleanQueryRewrite A rewrite method that first translates each term intoBooleanClause.Occur.SHOULD
clause in a BooleanQuery, but the scores are only computed as the boost.MultiTermQuery.TopTermsScoringBooleanQueryRewrite A rewrite method that first translates each term intoBooleanClause.Occur.SHOULD
clause in a BooleanQuery, and keeps the scores as computed by the query.NamedMatches Utility class to help extract the set of sub queries that have matched from a larger query.NGramPhraseQuery This is aPhraseQuery
which is optimized for n-gram phrase query.NormsFieldExistsQuery AQuery
that matches documents that have a value for a given field as reported by field norms.PhraseQuery A Query that matches documents containing a particular sequence of terms.PhraseQuery.Builder A builder for phrase queries.PointInSetQuery Abstract query class to find all documents whose single or multi-dimensional point values, previously indexed with e.g.PointInSetQuery.Stream Iterator of encoded point values.PointRangeQuery Abstract class for range queries against single or multidimensional points such asIntPoint
.PositiveScoresOnlyCollector PrefixQuery A Query that matches documents containing terms with a specified prefix.Query The abstract base class for queries.QueryRescorer ARescorer
that uses a provided Query to assign scores to the first-pass hits.QueryVisitor Allows recursion through a query treeReferenceManager<G> Utility class to safely share instances of a certain type across multiple threads, while periodically refreshing them.RegexpQuery A fast regular expression query based on theorg.apache.lucene.util.automaton
package.Rescorer Re-scores the topN results (TopDocs
) from an original query.Scorable Allows access to the score of a QueryScorable.ChildScorable A child Scorer and its relationship to its parent.ScoreCachingWrappingScorer AScorer
which wraps another scorer and caches the score of the current document.ScoreDoc Holds one hit inTopDocs
.Scorer Expert: Common scoring functionality for different types of queries.ScorerSupplier A supplier ofScorer
.ScoringRewrite<B> Base rewrite method that translates each term into a query, and keeps the scores as computed by the query.SearcherFactory Factory class used bySearcherManager
to create new IndexSearchers.SearcherLifetimeManager Keeps track of current plus old IndexSearchers, closing the old ones once they have timed out.SearcherLifetimeManager.PruneByAge Simple pruner that drops any searcher older by more than the specified seconds, than the newest searcher.SearcherManager Utility class to safely shareIndexSearcher
instances across multiple threads, while periodically reopening.SimpleCollector BaseCollector
implementation that is used to collect all contexts.SimpleFieldComparator<T> BaseFieldComparator
implementation that is used for all contexts.Sort Encapsulates sort criteria for returned hits.SortedNumericSelector Selects a value from the document's list to use as the representative valueSortedNumericSortField SortField forSortedNumericDocValues
.SortedNumericSortField.Provider A SortFieldProvider for this sort fieldSortedSetSelector Selects a value from the document's set to use as the representative valueSortedSetSortField SortField forSortedSetDocValues
.SortedSetSortField.Provider A SortFieldProvider for this sortSortField Stores information about how to sort documents by terms in an individual field.SortField.Provider A SortFieldProvider for field sortsSortRescorer ARescorer
that re-sorts according to a provided Sort.SynonymQuery A query that treats multiple terms as synonyms.SynonymQuery.Builder A builder forSynonymQuery
.TermInSetQuery Specialization for a disjunction over many terms that behaves like aConstantScoreQuery
over aBooleanQuery
containing onlyBooleanClause.Occur.SHOULD
clauses.TermQuery A Query that matches documents containing a term.TermRangeQuery A Query that matches documents within an range of terms.TermStatistics Contains statistics for a specific termTimeLimitingCollector TheTimeLimitingCollector
is used to timeout search requests that take longer than the maximum allowed search time limit.TimeLimitingCollector.TimerThread Thread used to timeout search requests.TopDocs Represents hits returned byIndexSearcher.search(Query,int)
.TopDocsCollector<T extends ScoreDoc> A base class for all collectors that return aTopDocs
output.TopFieldCollector TopFieldDocs Represents hits returned byIndexSearcher.search(Query,int,Sort)
.TopScoreDocCollector TopTermsRewrite<B> Base rewrite method for collecting only the top terms via a priority queue.TotalHitCountCollector Just counts the total number of hits.TotalHits Description of the total number of hits of a query.TwoPhaseIterator Returned byScorer.twoPhaseIterator()
to expose an approximation of aDocIdSetIterator
.UsageTrackingQueryCachingPolicy AQueryCachingPolicy
that tracks usage statistics of recently-used filters in order to decide on which filters are worth caching.Weight Expert: Calculate query weights and build query scorers.Weight.DefaultBulkScorer Just wraps a Scorer and performs top scoring using it.WildcardQuery Implements the wildcard search query. -
Enum Summary Enum Description BooleanClause.Occur Specifies how clauses are to occur in matching documents.ScoreMode Different modes of search.SortedNumericSelector.Type Type of selection to perform.SortedSetSelector.Type Type of selection to perform.SortField.Type Specifies the type of the terms to be sorted, or special types such as CUSTOMTotalHits.Relation How theTotalHits.value
should be interpreted. -
Exception Summary Exception Description BooleanQuery.TooManyClauses Thrown when an attempt is made to add more thanBooleanQuery.getMaxClauseCount()
clauses.CollectionTerminatedException Throw this exception inLeafCollector.collect(int)
to prematurely terminate collection of the current leaf.FuzzyTermsEnum.FuzzyTermsException Thrown to indicate that there was an issue creating a fuzzy query for a given term.TimeLimitingCollector.TimeExceededException Thrown when elapsed search time exceeds allowed search time.