Table Of Contents
- Search Basics
- The Query Classes
- Scoring: Introduction
- Scoring: Basics
- Changing the Scoring
- Appendix: Search Algorithm
Lucene offers a wide variety of
Query implementations, most
of which are in this package 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
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
BulkScorer instances. See the Algorithm section for more notes on the process.
Of the various implementations of
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
Documents that have the
"fieldName"containing the word
Things start to get interesting when one combines multiple
TermQuery instances into 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:
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.
BooleanClauseinstances. If too many clauses are added, a
TooManyClausesexception will be thrown during searching. This most often occurs when a
Queryis rewritten into a
TermQueryclauses, for example by
WildcardQuery. The default setting for the maximum number of clauses is 1024, but this can be changed via the static method
Another common search is to find documents containing certain phrases. This is handled in different ways:
PhraseQuery— Matches a sequence of
PhraseQueryuses 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.
Interval queries in the Queries module
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 (
PrefixQuery has a different
implementation, it is essentially a special case of 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
? (matches exactly one character) wildcards. Note that the
WildcardQuery can be quite slow. Also note that
WildcardQuery should not start with
?, as these are extremely slow. Some QueryParsers may not allow this by default, but
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
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
firstname.lastname@example.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:
Similarity API, and offer extension hooks and parameters for tuning. In general, Lucene first finds the documents that need to be scored based on boolean logic in the Query specification, and then ranks this subset of matching documents via the retrieval model. For some valuable references on VSM and IR in general refer to Lucene Wiki IR references.
The rest of this document will cover Scoring basics and explain
how to change your
Similarity. Next, it
will cover ways you can customize the lucene internals in Custom
Queries -- Expert Level, which gives details on implementing your own
Query class and related functionality. Finally, we will finish up
with some reference material in the Appendix.
Scoring — Basics
Scoring is very much dependent on the way documents are indexed, so it is important to
understand indexing. (see Lucene
overview before continuing on with this section) Be sure to use the useful
IndexSearcher.explain(Query, doc) to understand how the score for a certain matching document
Generally, the Query determines which documents match (a binary decision), while the Similarity determines how to assign scores to the matching documents.
Fields and Documents
In Lucene, the objects we are scoring are
Documents. A Document is a collection of
semantics about how it is created and
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
Lucene allows influencing the score contribution of various parts of the query by wrapping
Changing Scoring — Similarity
Changing the scoring formula
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.
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
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:
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 return
iteratorsover matches and give a way to compute the
scoreof these matches.
BulkScorer— An abstract class that scores a range of documents. A default implementation simply iterates through the hits from
Scorer, but some queries such as
BooleanQueryhave 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)— A
Weightis 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:
BooleanQuery, and other queries that implement
createWeight(IndexSearcher searcher,ScoreMode scoreMode, float boost)
The Weight Interface
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 new
Scorerfor 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 a
Similaritywill make use of the Similarity's implementation:
SimScorer#explain(Explanation freq, long norm).
matches(LeafReaderContext context, int doc)— Give information about positions and offsets of matches. This is typically useful to implement highlighting.
The Scorer Class
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 a
DocIdSetIteratorthat can iterate over all document that matches this Query.
docID()— Returns the id of the
Documentthat 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, the
TermScorersimply 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
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.
Once inside the IndexSearcher, a
Collector is used
for the scoring and sorting of the search results. These important objects are involved in a
Weightobject 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.
Sortobject 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
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
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
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
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
Interface Summary Interface Description BoostAttributeAdd this
MultiTermQuery.getTermsEnum(Terms,AttributeSource)and update the boost on each returned term.
CollectorExpert: 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. LeafCollectorCollector decouples the score from the collected doc: the score computation is skipped entirely if it's not needed. LeafFieldComparatorExpert: comparator that gets instantiated on each leaf from a top-level
MatchesReports the positions and optionally offsets of all matching terms in a query for a single document MatchesIteratorAn iterator over match positions (and optionally offsets) for a single document and field MaxNonCompetitiveBoostAttributeAdd this
Attributeto a fresh
QueryCacheA cache for queries. QueryCachingPolicyA policy defining which filters should be cached. ReferenceManager.RefreshListenerUse to receive notification when a refresh has finished. SearcherLifetimeManager.Pruner SegmentCacheableInterface defining whether or not an object can be cached against a
Class Summary Class Description AutomatonQueryA
Querythat will match terms against a finite-state machine.
Querythat blends index statistics across multiple terms.
BlendedTermQuery.BuilderA Builder for
BlendedTermQuery.RewriteMethoddefines how queries for individual terms should be merged.
BlockMaxDISI BooleanClauseA clause in a BooleanQuery. BooleanQueryA Query that matches documents matching boolean combinations of other queries, e.g. BooleanQuery.BuilderA builder for boolean queries. BoostAttributeImplImplementation class for
Querywrapper that allows to give a boost to the wrapped query.
BulkScorerThis class is used to score a range of documents at once, and is returned by
CachingCollectorCaches all docs, and optionally also scores, coming from a search, and is then able to replay them to another collector. CollectionStatisticsContains statistics for a collection (field). ConjunctionUtilsHelper methods for building conjunction iterators ConstantScoreQueryA query that wraps another query and simply returns a constant score equal to 1 for every document that matches the query. ConstantScoreQuery.ConstantBulkScorerWe return this as our
BulkScorerso that if the CSQ wraps a query with its own optimized top-level scorer (e.g.
ConstantScoreWeightA 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 a
ReferenceManager, with methods to wait for a specific index changes to become visible.
DisiPriorityQueueA priority queue of DocIdSetIterators that orders by current doc ID. DisiWrapperWrapper used in
DocIdSetIteratorwhich is a disjunction of the approximations of the provided iterators.
DisjunctionMaxQueryA 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. DocIdSetA DocIdSet contains a set of doc ids. DocIdSetIteratorThis abstract class defines methods to iterate over a set of non-decreasing doc ids. DocValuesFieldExistsQuery Deprecated. DocValuesRewriteMethodRewrites MultiTermQueries into a filter, using DocValues for term enumeration. DoubleValuesPer-segment, per-document double values, which can be calculated at search-time DoubleValuesSourceBase class for producing
ExactPhraseMatcherExpert: Find exact phrases ExplanationExpert: 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 with
FieldComparator.RelevanceComparatorSorts by descending relevance. FieldComparator.TermValComparatorSorts by field's natural Term sort order. FieldComparatorSourceProvides a
FieldComparatorfor custom field sorting.
FieldDocExpert: A ScoreDoc which also contains information about how to sort the referenced document. FieldExistsQuery FieldValueHitQueue<T extends FieldValueHitQueue.Entry>Expert: A hit queue for sorting by hits by terms in more than one field. FieldValueHitQueue.EntryExtension of ScoreDoc to also store the
FilteredDocIdSetIteratorAbstract decorator class of a DocIdSetIterator implementation that provides on-demand filter/validation mechanism on an underlying DocIdSetIterator. FilterLeafCollector
FilterMatchesIteratorA MatchesIterator that delegates all calls to another MatchesIterator FilterScorableFilter a
Scorable, intercepting methods and optionally changing their return values
Scorer, which it uses as its basic source of data, possibly transforming the data along the way or providing additional functionality.
Weightand implements all abstract methods by calling the contained weight's method.
FuzzyQueryImplements the fuzzy search query. FuzzyTermsEnumSubclass of TermsEnum for enumerating all terms that are similar to the specified filter term. HitQueueExpert: Priority queue containing hit docs ImpactsDISI
DocIdSetIteratorthat skips non-competitive docs thanks to the indexed impacts.
IndexOrDocValuesQueryA 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. IndexSearcherImplements search over a single IndexReader. IndexSearcher.LeafSliceA class holding a subset of the
IndexSearchers leaf contexts to be executed within a single thread.
IndexSortSortedNumericDocValuesRangeQueryA range query that can take advantage of the fact that the index is sorted to speed up execution. IndriAndQueryA Query that matches documents matching combinations of subqueries. IndriAndScorerCombines scores of subscorers. IndriAndWeightThe Weight for IndriAndQuery, used to normalize, score and explain these queries. IndriDisjunctionScorerThe Indri implemenation of a disjunction scorer which stores the subscorers for the child queries. IndriQueryA Basic abstract query that all IndriQueries can extend to implement toString, equals, getClauses, and iterator. IndriScorerThe Indri parent scorer that stores the boost so that IndriScorers can use the boost outside of the term. KnnByteVectorQueryUses
KnnVectorsReader.search(String, byte, int, Bits, int)to perform nearest neighbour search.
KnnVectorsReader.search(String, float, int, Bits, int)to perform nearest neighbour search.
KnnVectorFieldExistsQuery Deprecated. KnnVectorQuery Deprecated.use
LeafSimScorer LiveFieldValues<S,T>Tracks live field values across NRT reader reopens. LongValuesPer-segment, per-document long values, which can be calculated at search-time LongValuesSourceBase class for producing
LongValuesSource.ConstantLongValuesSourceA ConstantLongValuesSource that always returns a constant value LRUQueryCacheA
QueryCachethat evicts queries using a LRU (least-recently-used) eviction policy in order to remain under a given maximum size and number of bytes used.
LRUQueryCache.CacheAndCountCache of doc ids with a count. MatchAllDocsQueryA query that matches all documents. MatchesUtils MatchNoDocsQueryA query that matches no documents. MaxNonCompetitiveBoostAttributeImplImplementation class for
MultiCollector MultiCollectorManager MultiPhraseQueryA generalized version of
PhraseQuery, with the possibility of adding more than one term at the same position that are treated as a disjunction (OR).
MultiPhraseQuery.BuilderA builder for multi-phrase queries MultiPhraseQuery.UnionFullPostingsEnumSlower version of UnionPostingsEnum that delegates offsets and positions, for use by MatchesIterator MultiPhraseQuery.UnionPostingsEnumTakes the logical union of multiple PostingsEnum iterators. Multiset<T>A
Multisetis a set that allows for duplicate elements.
MultiTermQuery MultiTermQuery.RewriteMethodAbstract class that defines how the query is rewritten. MultiTermQuery.TopTermsBlendedFreqScoringRewriteA rewrite method that first translates each term into
BooleanClause.Occur.SHOULDclause 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.TopTermsBoostOnlyBooleanQueryRewriteA rewrite method that first translates each term into
BooleanClause.Occur.SHOULDclause in a BooleanQuery, but the scores are only computed as the boost.
MultiTermQuery.TopTermsScoringBooleanQueryRewriteA rewrite method that first translates each term into
BooleanClause.Occur.SHOULDclause in a BooleanQuery, and keeps the scores as computed by the query.
NamedMatchesUtility class to help extract the set of sub queries that have matched from a larger query. NGramPhraseQueryThis is a
PhraseQuerywhich is optimized for n-gram phrase query.
NormsFieldExistsQuery Deprecated. PhraseMatcherBase class for exact and sloppy phrase matching PhraseQueryA Query that matches documents containing a particular sequence of terms. PhraseQuery.BuilderA builder for phrase queries. PhraseQuery.PostingsAndFreqTerm postings and position information for phrase matching PhraseWeightExpert: Weight class for phrase matching PointInSetQueryAbstract query class to find all documents whose single or multi-dimensional point values, previously indexed with e.g. PointInSetQuery.StreamIterator of encoded point values. PointRangeQueryAbstract class for range queries against single or multidimensional points such as
PositiveScoresOnlyCollector PrefixQueryA Query that matches documents containing terms with a specified prefix. QueryThe abstract base class for queries. QueryRescorerA
Rescorerthat uses a provided Query to assign scores to the first-pass hits.
QueryVisitorAllows recursion through a query tree ReferenceManager<G>Utility class to safely share instances of a certain type across multiple threads, while periodically refreshing them. RegexpQueryA fast regular expression query based on the
RescorerRe-scores the topN results (
TopDocs) from an original query.
ScorableAllows access to the score of a Query Scorable.ChildScorableA child Scorer and its relationship to its parent. ScoreCachingWrappingScorerA
Scorerwhich wraps another scorer and caches the score of the current document.
ScoreDocHolds one hit in
ScorerExpert: Common scoring functionality for different types of queries. ScorerSupplierA supplier of
ScoringRewrite<B>Base rewrite method that translates each term into a query, and keeps the scores as computed by the query. SearcherFactoryFactory class used by
SearcherManagerto create new IndexSearchers.
SearcherLifetimeManagerKeeps track of current plus old IndexSearchers, closing the old ones once they have timed out. SearcherLifetimeManager.PruneByAgeSimple pruner that drops any searcher older by more than the specified seconds, than the newest searcher. SearcherManagerUtility class to safely share
IndexSearcherinstances across multiple threads, while periodically reopening.
Collectorimplementation that is used to collect all contexts.
FieldComparatorimplementation that is used for all contexts.
SloppyPhraseMatcherFind all slop-valid position-combinations (matches) encountered while traversing/hopping the PhrasePositions. SortEncapsulates sort criteria for returned hits. SortedNumericSelectorSelects a value from the document's list to use as the representative value SortedNumericSortFieldSortField for
SortedNumericSortField.ProviderA SortFieldProvider for this sort field SortedSetSelectorSelects a value from the document's set to use as the representative value SortedSetSortFieldSortField for
SortedSetSortField.ProviderA SortFieldProvider for this sort SortFieldStores information about how to sort documents by terms in an individual field. SortField.ProviderA SortFieldProvider for field sorts SortRescorerA
Rescorerthat re-sorts according to a provided Sort.
SynonymQueryA query that treats multiple terms as synonyms. SynonymQuery.BuilderA builder for
TermInSetQuery TermQueryA Query that matches documents containing a term. TermRangeQueryA Query that matches documents within an range of terms. TermScorerExpert: A
Scorerfor documents matching a
TermStatisticsContains statistics for a specific term TimeLimitingCollectorThe
TimeLimitingCollectoris used to timeout search requests that take longer than the maximum allowed search time limit.
TimeLimitingCollector.TimerThreadThread used to timeout search requests. TopDocsRepresents hits returned by
TopDocsCollector<T extends ScoreDoc>A base class for all collectors that return a
TopFieldCollector TopFieldDocsRepresents hits returned by
TopScoreDocCollector TopScoreDocCollector.ScorerLeafCollectorScorable leaf collector TopTermsRewrite<B>Base rewrite method for collecting only the top terms via a priority queue. TotalHitCountCollectorJust counts the total number of hits. TotalHitCountCollectorManager TotalHitsDescription of the total number of hits of a query. TwoPhaseIterator UsageTrackingQueryCachingPolicyA
QueryCachingPolicythat tracks usage statistics of recently-used filters in order to decide on which filters are worth caching.
WeightExpert: Calculate query weights and build query scorers. Weight.DefaultBulkScorerJust wraps a Scorer and performs top scoring using it. WildcardQueryImplements the wildcard search query.
Enum Summary Enum Description BooleanClause.OccurSpecifies how clauses are to occur in matching documents. ScoreModeDifferent modes of search. SortedNumericSelector.TypeType of selection to perform. SortedSetSelector.TypeType of selection to perform. SortField.TypeSpecifies the type of the terms to be sorted, or special types such as CUSTOM TotalHits.RelationHow the
TotalHits.valueshould be interpreted.
Exception Summary Exception Description BooleanQuery.TooManyClauses Deprecated. CollectionTerminatedExceptionThrow this exception in
LeafCollector.collect(int)to prematurely terminate collection of the current leaf.
FuzzyTermsEnum.FuzzyTermsExceptionThrown to indicate that there was an issue creating a fuzzy query for a given term. IndexSearcher.TooManyClausesThrown when an attempt is made to add more than
IndexSearcher.TooManyNestedClausesThrown when a client attempts to execute a Query that has more than
IndexSearcher.TooManyClauses.getMaxClauseCount()total clauses cumulatively in all of it's children.
TimeLimitingCollector.TimeExceededExceptionThrown when elapsed search time exceeds allowed search time.