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 subpackages (spans
, 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 IndexSearcher.search(Query,int)
or IndexSearcher.search(Query,Filter,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
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. 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 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.
TermRangeQuery
The
TermRangeQuery
matches all documents that occur in the
exclusive range of a lower
Term
and an upper
Term
according to TermsEnum.getComparator()
. 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.
NumericRangeQuery
The
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 (IntField
,
LongField
, FloatField
,
or DoubleField
).
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 (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 [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:
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 search results by "boosting" at different times:
- Index-time boost by calling
Field.setBoost()
before a document is added to the index. - Query-time boost by setting a boost on a query clause, calling
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:
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 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, or to use external scoring factors particular to your application. For example,
a custom Similarity can access per-document values via FieldCache
or
NumericDocValues
and integrate them into the score.
See the 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:
-
Query
— The abstract object representation of the user's information need. -
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). -
Scorer
— An abstract class containing common functionality for scoring. Provides both scoring and explanation capabilities. This is created per-segment.
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)
— 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)
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 five methods that must be implemented:
-
getQuery()
— Pointer to the Query that this Weight represents. -
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 aSimilarity
will just defer to the Similarity's implementation:SimWeight#getValueForNormalization()
. For example, withLucene's classic vector-space formula
, this is implemented as the sum of squared weights:(idf * boost)2
-
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 of5
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.
Similarity
will just defer to the Similarity's implementation:SimWeight#normalize(float,float)
. -
scorer(AtomicReaderContext context, boolean scoresDocsInOrder, boolean topScorer, Bits acceptDocs)
— 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(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 aSimilarity
will make use of the Similarity's implementation:SimScorer#explain(int doc, Explanation freq)
.
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
):
-
nextDoc()
— Advances to the next document that matches this Query, returning true if and only if there is another document that matches. -
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(int doc, float 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, theTermScorer
simply defers to the term frequency from the inverted index:DocsEnum.freq()
. -
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. -
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 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
Filter
for limiting the result set. Note, the Filter may be null. - 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.createNormalizedWeight(Query)
,
Filter
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, Filter 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.
If a Filter is being used, some initial setup is done to determine which docs to include.
Otherwise, we ask the Weight for a Scorer
for each
IndexReader
segment and proceed by calling
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 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 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
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.
-
Interface Summary Interface Description BoostAttribute Add thisAttribute
to aTermsEnum
returned byMultiTermQuery.getTermsEnum(Terms,AttributeSource)
and update the boost on each returned term.FieldCache Expert: Maintains caches of term values.FieldCache.ByteParser Deprecated. FieldCache.DoubleParser Interface to parse doubles from document fields.FieldCache.FloatParser Interface to parse floats from document fields.FieldCache.IntParser Interface to parse ints from document fields.FieldCache.LongParser Interface to parse long from document fields.FieldCache.Parser Marker interface as super-interface to all parsers.FieldCache.ShortParser Deprecated. FuzzyTermsEnum.LevenshteinAutomataAttribute reuses compiled automata across different segments, because they are independent of the indexMaxNonCompetitiveBoostAttribute Add thisAttribute
to a freshAttributeSource
before callingMultiTermQuery.getTermsEnum(Terms,AttributeSource)
.ReferenceManager.RefreshListener Use to receive notification when a refresh has finished.SearcherLifetimeManager.Pruner -
Class Summary Class Description AutomatonQuery AQuery
that will match terms against a finite-state machine.BitsFilteredDocIdSet This implementation supplies a filtered DocIdSet, that excludes all docids which are not in a Bits instance.BooleanClause A clause in a BooleanQuery.BooleanQuery A Query that matches documents matching boolean combinations of other queries, e.g.BoostAttributeImpl Implementation class forBoostAttribute
.CachingCollector Caches all docs, and optionally also scores, coming from a search, and is then able to replay them to another collector.CachingWrapperFilter Wraps anotherFilter
's result and caches it.CollectionStatistics Contains statistics for a collection (field)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.ComplexExplanation Expert: Describes the score computation for document and query, and can distinguish a match independent of a positive value.ConstantScoreQuery A query that wraps another query or a filter and simply returns a constant score equal to the query boost for every document that matches the filter or 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.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.DocTermOrdsRangeFilter A range filter built on top of a cached multi-valued term field (inFieldCache
).DocTermOrdsRewriteMethod Rewrites MultiTermQueries into a filter, using DocTermOrds for term enumeration.Explanation Expert: Describes the score computation for document and query.FieldCache.Bytes Field values as 8-bit signed bytesFieldCache.CacheEntry EXPERT: A unique Identifier/Description for each item in the FieldCache.FieldCache.CreationPlaceholder Placeholder indicating creation of this cache is currently in-progress.FieldCache.Doubles Field values as 64-bit doublesFieldCache.Floats Field values as 32-bit floatsFieldCache.Ints Field values as 32-bit signed integersFieldCache.Longs Field values as 64-bit signed long integersFieldCache.Shorts Field values as 16-bit signed shortsFieldCacheDocIdSet Base class for DocIdSet to be used with FieldCache.FieldCacheRangeFilter<T> A range filter built on top of a cached single term field (inFieldCache
).FieldCacheRewriteMethod Rewrites MultiTermQueries into a filter, using the FieldCache for term enumeration.FieldCacheTermsFilter AFilter
that only accepts documents whose single term value in the specified field is contained in the provided set of allowed terms.FieldComparator<T> Expert: a FieldComparator compares hits so as to determine their sort order when collecting the top results withTopFieldCollector
.FieldComparator.ByteComparator Deprecated. FieldComparator.DocComparator Sorts by ascending docIDFieldComparator.DoubleComparator Parses field's values as double (usingFieldCache.getDoubles(org.apache.lucene.index.AtomicReader, java.lang.String, boolean)
and sorts by ascending valueFieldComparator.FloatComparator Parses field's values as float (usingFieldCache.getFloats(org.apache.lucene.index.AtomicReader, java.lang.String, boolean)
and sorts by ascending valueFieldComparator.IntComparator Parses field's values as int (usingFieldCache.getInts(org.apache.lucene.index.AtomicReader, java.lang.String, boolean)
and sorts by ascending valueFieldComparator.LongComparator Parses field's values as long (usingFieldCache.getLongs(org.apache.lucene.index.AtomicReader, java.lang.String, boolean)
and sorts by ascending valueFieldComparator.NumericComparator<T extends Number> Base FieldComparator class for numeric typesFieldComparator.RelevanceComparator Sorts by descending relevance.FieldComparator.ShortComparator Deprecated. 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.FieldValueFilter AFilter
that accepts all documents that have one or more values in a given field.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.Filter Abstract base class for restricting which documents may be returned during searching.FilteredDocIdSet Abstract decorator class for a DocIdSet implementation that provides on-demand filtering/validation mechanism on a given DocIdSet.FilteredDocIdSetIterator Abstract decorator class of a DocIdSetIterator implementation that provides on-demand filter/validation mechanism on an underlying DocIdSetIterator.FilteredQuery A query that applies a filter to the results of another query.FilteredQuery.FilterStrategy Abstract class that defines how the filter (DocIdSet
) applied during document collection.FilteredQuery.RandomAccessFilterStrategy AFilteredQuery.FilterStrategy
that conditionally uses a random access filter if the givenDocIdSet
supports random access (returns a non-null value fromDocIdSet.bits()
) andFilteredQuery.RandomAccessFilterStrategy.useRandomAccess(Bits, int)
returnstrue
.FuzzyQuery Implements the fuzzy search query.FuzzyTermsEnum Subclass of TermsEnum for enumerating all terms that are similar to the specified filter term.FuzzyTermsEnum.LevenshteinAutomataAttributeImpl Stores compiled automata as a list (indexed by edit distance)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.LiveFieldValues<S,T> Tracks live field values across NRT reader reopens.MatchAllDocsQuery A query that matches all documents.MaxNonCompetitiveBoostAttributeImpl Implementation class forMaxNonCompetitiveBoostAttribute
.MultiCollector MultiPhraseQuery MultiPhraseQuery is a generalized version of PhraseQuery, with an added methodMultiPhraseQuery.add(Term[])
.MultiTermQuery An abstractQuery
that matches documents containing a subset of terms provided by aFilteredTermsEnum
enumeration.MultiTermQuery.ConstantScoreAutoRewrite A rewrite method that tries to pick the best constant-score rewrite method based on term and document counts from the query.MultiTermQuery.RewriteMethod Abstract class that defines how the query is rewritten.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.MultiTermQueryWrapperFilter<Q extends MultiTermQuery> A wrapper forMultiTermQuery
, that exposes its functionality as aFilter
.NGramPhraseQuery This is aPhraseQuery
which is optimized for n-gram phrase query.NumericRangeFilter<T extends Number> AFilter
that only accepts numeric values within a specified range.NumericRangeQuery<T extends Number> AQuery
that matches numeric values within a specified range.PhraseQuery A Query that matches documents containing a particular sequence of terms.PositiveScoresOnlyCollector PrefixFilter A Filter that restricts search results to values that have a matching prefix in a given field.PrefixQuery A Query that matches documents containing terms with a specified prefix.PrefixTermsEnum Subclass of FilteredTermEnum for enumerating all terms that match the specified prefix filter term.Query The abstract base class for queries.QueryWrapperFilter Constrains search results to only match those which also match a provided query.ReferenceManager<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.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.Scorer.ChildScorer A child Scorer and its relationship to its parent.ScoringRewrite<Q extends Query> 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.Sort Encapsulates sort criteria for returned hits.SortField Stores information about how to sort documents by terms in an individual field.TermQuery A Query that matches documents containing a term.TermRangeFilter A Filter that restricts search results to a range of term values in a given field.TermRangeQuery A Query that matches documents within an range of terms.TermRangeTermsEnum Subclass of FilteredTermEnum for enumerating all terms that match the specified range parameters.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,Filter,int)
andIndexSearcher.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,Filter,int,Sort)
.TopScoreDocCollector TopTermsRewrite<Q extends Query> Base rewrite method for collecting only the top terms via a priority queue.TotalHitCountCollector Just counts the total number of hits.Weight Expert: Calculate query weights and build query scorers.WildcardQuery Implements the wildcard search query. -
Enum Summary Enum Description BooleanClause.Occur Specifies how clauses are to occur in matching documents.SortField.Type Specifies the type of the terms to be sorted, or special types such as CUSTOM -
Exception Summary Exception Description BooleanQuery.TooManyClauses Thrown when an attempt is made to add more thanBooleanQuery.getMaxClauseCount()
clauses.CollectionTerminatedException Throw this exception inCollector.collect(int)
to prematurely terminate collection of the current leaf.TimeLimitingCollector.TimeExceededException Thrown when elapsed search time exceeds allowed search time.