Class IndexSearcher

Creates a searcher searching the provided index.

Runs searches for each segment separately, using the provided . will not shutdown/awaitTermination this on dispose; you must do so, eventually, on your own.

@lucene.experimental

Creates a searcher searching the provided top-level IndexReaderContext.

@lucene.experimental

Creates a searcher searching the provided top-level IndexReaderContext.

Given a non-null this method runs searches for each segment separately, using the provided . will not shutdown/awaitTermination this on close; you must do so, eventually, on your own.

@lucene.experimental

Used with executor - each slice holds a set of leafs executed within one thread

Set to false to return an overall failure if the request would produce partial results. Set to true, which will allow partial results in the case of partial cluster.

Expert: returns a default Similarity instance. In general, this method is only called to initialize searchers and writers. User code and query implementations should respect .

@lucene.internal

Return the IndexReader this searches.

Expert: Set the Similarity implementation used by this IndexSearcher.

Returns this searchers the top-level IndexReaderContext.

Returns CollectionStatistics for a field.

This can be overridden for example, to return a field's statistics across a distributed collection.

@lucene.experimental

Creates a normalized weight for a top-level Query. The query is rewritten by this method and called, afterwards the Weight is normalized. The returned Weight can then directly be used to get a Scorer.

@lucene.internal

Sugar for

.IndexReader.Document(docID)

Sugar for

.IndexReader.Document(docID, fieldsToLoad)

Sugar for

.IndexReader.Document(docID, fieldVisitor)

Returns an Explanation that describes how doc scored against query.

This is intended to be used in developing Similarity implementations, and, for good performance, should not be displayed with every hit. Computing an explanation is as expensive as executing the query over the entire index.

Expert: low-level implementation method Returns an Explanation that describes how doc scored against weight.

This is intended to be used in developing Similarity implementations, and, for good performance, should not be displayed with every hit. Computing an explanation is as expensive as executing the query over the entire index.

Applications should call .

Expert: called to re-write queries into primitive queries.

Just like Search(Weight, Int32, Sort, Boolean, Boolean), but you choose whether or not the fields in the returned FieldDoc instances should be set by specifying fillFields.

Lower-level search API.

Collect(Int32) is called for every document.

NOTE: this method executes the searches on all given leaves exclusively. To search across all the searchers leaves use m_leafContexts.

Expert: Low-level search implementation. Finds the top

n

Applications should usually call or instead.

Lower-level search API.

Collect(Int32) is called for every matching document.

Finds the top n hits for query, applying filter if non-null.

Search implementation with arbitrary sorting. Finds the top n hits for query, applying filter if non-null, and sorting the hits by the criteria in sort.

NOTE: this does not compute scores by default; use to control scoring.

Search implementation with arbitrary sorting, plus control over whether hit scores and max score should be computed. Finds the top n hits for query, applying filter if non-null, and sorting the hits by the criteria in sort. If doDocScores is true then the score of each hit will be computed and returned. If doMaxScore is true then the maximum score over all collected hits will be computed.

Lower-level search API.

Collect(Int32) is called for every matching document.

Finds the top n hits for query.

Search implementation with arbitrary sorting and no filter.

Just like Search(Weight, Int32, Sort, Boolean, Boolean), but you choose whether or not the fields in the returned FieldDoc instances should be set by specifying fillFields.

Expert: Low-level search implementation. Finds the top nDocs hits for query, applying filter if non-null.

Applications should usually call or instead.

Expert: Low-level search implementation with arbitrary sorting and control over whether hit scores and max score should be computed. Finds the top nDocs hits for query and sorting the hits by the criteria in sort.

Applications should usually call instead.

Finds the top n hits for query, applying filter if non-null, where all results are after a previous result (after).

By passing the bottom result from a previous page as after, this method can be used for efficient 'deep-paging' across potentially large result sets.

Finds the top n hits for query, applying filter if non-null, where all results are after a previous result (after).

By passing the bottom result from a previous page as after, this method can be used for efficient 'deep-paging' across potentially large result sets.

Finds the top n hits for query where all results are after a previous result (after), allowing control over whether hit scores and max score should be computed.

By passing the bottom result from a previous page as after, this method can be used for efficient 'deep-paging' across potentially large result sets. If doDocScores is true then the score of each hit will be computed and returned. If doMaxScore is true then the maximum score over all collected hits will be computed.

Finds the top n hits for top query where all results are after a previous result (top after).

By passing the bottom result from a previous page as after, this method can be used for efficient 'deep-paging' across potentially large result sets.

Finds the top n hits for query where all results are after a previous result (after).

By passing the bottom result from a previous page as after, this method can be used for efficient 'deep-paging' across potentially large result sets.

Expert: Creates an array of leaf slices each holding a subset of the given leaves. Each IndexSearcher.LeafSlice is executed in a single thread. By default there will be one IndexSearcher.LeafSlice per leaf (AtomicReaderContext).

Returns TermStatistics for a term.

This can be overridden for example, to return a term's statistics across a distributed collection.

@lucene.experimental

@lucene.internal