Expert: Maintains caches of term values.
Interface to parse bytes from document fields.
Interface to parse doubles from document fields.
Interface to parse floats from document fields.
Interface to parse ints from document fields.
Interface to parse long from document fields.
Marker interface as super-interface to all parsers.
Interface to parse shorts from document fields.
NRTManager invokes this interface to notify it when a caller is waiting for a specific generation searcher to be visible.
In 4.0 this interface is removed/absorbed into IndexSearcher
A clause in a BooleanQuery.
A Query that matches documents matching boolean combinations of other queries, e.g.
Caches all docs, and optionally also scores, coming from a search, and is then able to replay them to another collector.
Wraps another SpanFilter's result and caches it.
Wraps another filter's result and caches it.
Expert: Collectors are primarily meant to be used to gather raw results from a search, and implement sorting or custom result filtering, collation, etc.
Expert: Describes the score computation for document and query, and can distinguish a match independent of a positive value.
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.
Expert: Default scoring implementation.
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.
A DocIdSet contains a set of doc ids.
This abstract class defines methods to iterate over a set of non-decreasing doc ids.
Expert: Describes the score computation for document and query.
Small Util class used to pass both an idf factor as well as an explanation for that factor.
EXPERT: A unique Identifier/Description for each item in the FieldCache.
Expert: Stores term text values and document ordering data.
Base class for DocIdSet to be used with FieldCache.
A range filter built on top of a cached single term field (in
Expert: a FieldComparator compares hits so as to determine their sort order when collecting the top results with
Parses field's values as byte (using
Sorts by ascending docID
Parses field's values as double (using
Parses field's values as float (using
Parses field's values as int (using
Parses field's values as long (using
|FieldComparator.NumericComparator<T extends Number>|
Sorts by descending relevance.
Parses field's values as short (using
Sorts by a field's value using the Collator for a given Locale.
Sorts by field's natural String sort order, using ordinals.
Sorts by field's natural String sort order.
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.
Abstract base class for restricting which documents may be returned during searching.
Abstract decorator class for a DocIdSet implementation that provides on-demand filtering/validation mechanism on a given DocIdSet.
Abstract decorator class of a DocIdSetIterator implementation that provides on-demand filter/validation mechanism on an underlying DocIdSetIterator.
A query that applies a filter to the results of another query.
Abstract class for enumerating a subset of all terms.
used by remote package which is deprecated as well.
Implements the fuzzy search query.
Subclass of FilteredTermEnum for enumerating all terms that are similar to the specified filter term.
Implements search over a single IndexReader.
A query that matches all documents.
MultiPhraseQuery is a generalized version of PhraseQuery, with an added method
If you are using MultiSearcher over IndexSearchers, please use MultiReader instead; this class does not properly handle certain kinds of queries (see LUCENE-2756).
A rewrite method that tries to pick the best constant-score rewrite method based on term and document counts from the query.
Abstract class that defines how the query is rewritten.
A rewrite method that first translates each term into
A rewrite method that first translates each term into
|MultiTermQueryWrapperFilter<Q extends MultiTermQuery>|
This is a
Utility class to manage sharing near-real-time searchers across multiple searching thread.
Class that tracks changes to a delegated IndexWriter.
Utility class that runs a reopen thread to periodically reopen the NRT searchers in the provided
|NumericRangeFilter<T extends Number>||
|NumericRangeQuery<T extends Number>||
Please pass an ExecutorService to
A Query that matches documents containing a particular sequence of terms.
A Filter that restricts search results to values that have a matching prefix in a given field.
A Query that matches documents containing terms with a specified prefix.
Subclass of FilteredTermEnum for enumerating all terms that match the specified prefix filter term.
The abstract base class for queries.
Constrains search results to only match those which also match a provided query.
Utility class to safely share instances of a certain type across multiple threads, while periodically refreshing them.
Holds one hit in
Expert: Common scoring functionality for different types of queries.
|Scorer.ScorerVisitor<P extends Query,C extends Query,S extends Scorer>||
A callback to gather information from a scorer and its sub-scorers.
|ScoringRewrite<Q extends Query>|
In 4.0 this abstract class is removed/absorbed into IndexSearcher
Keeps track of current plus old IndexSearchers, closing the old ones once they have timed out.
Simple pruner that drops any searcher older by more than the specified seconds, than the newest searcher.
Utility class to safely share
Expert: Scoring API.
this class will be removed in 4.0.
Subclass of FilteredTermEnum for enumerating a single term.
Encapsulates sort criteria for returned hits.
Stores information about how to sort documents by terms in an individual field.
Abstract base class providing a mechanism to restrict searches to a subset of an index and also maintains and returns position information.
The results of a SpanQueryFilter.
Constrains search results to only match those which also match a provided query.
A Query that matches documents containing a term.
A Filter that restricts search results to a range of term values in a given field.
A Query that matches documents within an range of terms.
Subclass of FilteredTermEnum for enumerating all terms that match the specified range parameters.
|TopDocsCollector<T extends ScoreDoc>||
A base class for all collectors that return a
Represents hits returned by
|TopTermsRewrite<Q extends Query>||
Base rewrite method for collecting only the top terms via a priority queue.
Just counts the total number of hits.
Expert: Calculate query weights and build query scorers.
Implements the wildcard search query.
Subclass of FilteredTermEnum for enumerating all terms that match the specified wildcard filter term.
Specifies how clauses are to occur in matching documents.
Expert: Specifies how new deletions against a reopened reader should be handled.
Thrown when an attempt is made to add more than
Thrown when elapsed search time exceeds allowed search time.
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 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 Query identifies all Documents that have the Field named "fieldName" containing the word "term".
Things start to get interesting when one combines multiple TermQuery instances into a BooleanQuery. 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. 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.
Another common search is to find documents containing certain phrases. This is handled two different ways:
SpanNearQuery — Matches a sequence of other SpanQuery instances. SpanNearQuery allows for much more complicated phrase queries since it is constructed from other SpanQuery instances, instead of only TermQuery instances.
matches all documents that occur in the
exclusive range of a lower
and an upper
String.compareTo(String). 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. This type of Query is frequently used to
documents that occur in a specific date range.
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. To remove this protection and allow a wildcard at the beginning of a term, see method setAllowLeadingWildcard in QueryParser.
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.
Chances are DefaultSimilarity is sufficient for all your searching needs. However, in some applications it may be necessary to customize your Similarity implementation. For instance, some applications do not need to distinguish between shorter and longer documents (see a "fair" similarity).
To change Similarity, one must do so for both indexing and searching, and the changes must happen before either of these actions take place. Although in theory there is nothing stopping you from changing mid-stream, it just isn't well-defined what is going to happen.
To make this change, implement your own Similarity (likely you'll want to simply subclass DefaultSimilarity) and then use the new class by calling IndexWriter.setSimilarity before indexing and Searcher.setSimilarity before searching.
If you are interested in use cases for changing your similarity, see the Lucene users's mailing list at Overriding Similarity. In summary, here are a few use cases:
SweetSpotSimilarity — SweetSpotSimilarity gives small increases as the frequency increases a small amount and then greater increases when you hit the "sweet spot", i.e. where you think the frequency of terms is more significant.
Overriding tf — In some applications, it doesn't matter what the score of a document is as long as a matching term occurs. In these cases people have overridden Similarity to return 1 from the tf() method.
Changing Length Normalization — By overriding lengthNorm, it is possible to discount how the length of a field contributes to a score. In DefaultSimilarity, lengthNorm = 1 / (numTerms in field)^0.5, but if one changes this to be 1 / (numTerms in field), all fields will be treated "fairly".
[One would override the Similarity in] ... any situation where you know more about your data then just that it's "text" is a situation where it *might* make sense to to override your Similarity method.
Changing scoring is 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 scoring in Lucene. Lucene's scoring is a complex mechanism that is grounded by three main classes:
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:
The Weight interface provides an internal representation of the Query so that it can be reused. Any Searcher dependent state should be stored in the Weight implementation, not in the Query class. The interface defines six methods that must be implemented:
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 ):
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).