org.apache.lucene.index.memory

Class MemoryIndex

java.lang.Object

org.apache.lucene.index.memory.MemoryIndex

public class MemoryIndex
extends Object

High-performance single-document main memory Apache Lucene fulltext search index.

Overview

This class is a replacement/substitute for a large subset of RAMDirectory functionality. It is designed to enable maximum efficiency for on-the-fly matchmaking combining structured and fuzzy fulltext search in realtime streaming applications such as Nux XQuery based XML message queues, publish-subscribe systems for Blogs/newsfeeds, text chat, data acquisition and distribution systems, application level routers, firewalls, classifiers, etc. Rather than targeting fulltext search of infrequent queries over huge persistent data archives (historic search), this class targets fulltext search of huge numbers of queries over comparatively small transient realtime data (prospective search). For example as in

 float score = search(String text, Query query)
 

Each instance can hold at most one Lucene "document", with a document containing zero or more "fields", each field having a name and a fulltext value. The fulltext value is tokenized (split and transformed) into zero or more index terms (aka words) on addField(), according to the policy implemented by an Analyzer. For example, Lucene analyzers can split on whitespace, normalize to lower case for case insensitivity, ignore common terms with little discriminatory value such as "he", "in", "and" (stop words), reduce the terms to their natural linguistic root form such as "fishing" being reduced to "fish" (stemming), resolve synonyms/inflexions/thesauri (upon indexing and/or querying), etc. For details, see Lucene Analyzer Intro.

Arbitrary Lucene queries can be run against this class - see Lucene Query Syntax as well as Query Parser Rules. Note that a Lucene query selects on the field names and associated (indexed) tokenized terms, not on the original fulltext(s) - the latter are not stored but rather thrown away immediately after tokenization.

For some interesting background information on search technology, see Bob Wyman's Prospective Search, Jim Gray's A Call to Arms - Custom subscriptions, and Tim Bray's On Search, the Series.

Example Usage

 Analyzer analyzer = new SimpleAnalyzer(version);
 MemoryIndex index = new MemoryIndex();
 index.addField("content", "Readings about Salmons and other select Alaska fishing Manuals", analyzer);
 index.addField("author", "Tales of James", analyzer);
 QueryParser parser = new QueryParser(version, "content", analyzer);
 float score = index.search(parser.parse("+author:james +salmon~ +fish* manual~"));
 if (score > 0.0f) {
     System.out.println("it's a match");
 } else {
     System.out.println("no match found");
 }
 System.out.println("indexData=" + index.toString());
 

Example XQuery Usage

 (: An XQuery that finds all books authored by James that have something to do with "salmon fishing manuals", sorted by relevance :)
 declare namespace lucene = "java:nux.xom.pool.FullTextUtil";
 declare variable $query := "+salmon~ +fish* manual~"; (: any arbitrary Lucene query can go here :)
 
 for $book in /books/book[author="James" and lucene:match(abstract, $query) > 0.0]
 let $score := lucene:match($book/abstract, $query)
 order by $score descending
 return $book
 

Thread safety guarantees

MemoryIndex is not normally thread-safe for adds or queries. However, queries are thread-safe after freeze() has been called.

Performance Notes

Internally there's a new data structure geared towards efficient indexing and searching, plus the necessary support code to seamlessly plug into the Lucene framework.

This class performs very well for very small texts (e.g. 10 chars) as well as for large texts (e.g. 10 MB) and everything in between. Typically, it is about 10-100 times faster than RAMDirectory. Note that RAMDirectory has particularly large efficiency overheads for small to medium sized texts, both in time and space. Indexing a field with N tokens takes O(N) in the best case, and O(N logN) in the worst case. Memory consumption is probably larger than for RAMDirectory.

Example throughput of many simple term queries over a single MemoryIndex: ~500000 queries/sec on a MacBook Pro, jdk 1.5.0_06, server VM. As always, your mileage may vary.

If you're curious about the whereabouts of bottlenecks, run java 1.5 with the non-perturbing '-server -agentlib:hprof=cpu=samples,depth=10' flags, then study the trace log and correlate its hotspot trailer with its call stack headers (see hprof tracing ).

Constructor Summary

Constructors

Constructor and Description
MemoryIndex() Constructs an empty instance that will not store offsets or payloads.
MemoryIndex(boolean storeOffsets) Constructs an empty instance that can optionally store the start and end character offset of each token term in the text.
MemoryIndex(boolean storeOffsets, boolean storePayloads) Constructs an empty instance with the option of storing offsets and payloads.

Method Summary

Methods

Modifier and Type	Method and Description
void	addField(String fieldName, String text, Analyzer analyzer) Convenience method; Tokenizes the given field text and adds the resulting terms to the index; Equivalent to adding an indexed non-keyword Lucene Field that is tokenized, not stored, termVectorStored with positions (or termVectorStored with positions and offsets),
void	addField(String fieldName, TokenStream stream) Equivalent to addField(fieldName, stream, 1.0f).
void	addField(String fieldName, TokenStream stream, float boost) Iterates over the given token stream and adds the resulting terms to the index; Equivalent to adding a tokenized, indexed, termVectorStored, unstored, Lucene Field.
void	addField(String fieldName, TokenStream stream, float boost, int positionIncrementGap) Iterates over the given token stream and adds the resulting terms to the index; Equivalent to adding a tokenized, indexed, termVectorStored, unstored, Lucene Field.
void	addField(String fieldName, TokenStream tokenStream, float boost, int positionIncrementGap, int offsetGap) Iterates over the given token stream and adds the resulting terms to the index; Equivalent to adding a tokenized, indexed, termVectorStored, unstored, Lucene Field.
IndexSearcher	createSearcher() Creates and returns a searcher that can be used to execute arbitrary Lucene queries and to collect the resulting query results as hits.
void	freeze() Prepares the MemoryIndex for querying in a non-lazy way.
<T> TokenStream	keywordTokenStream(Collection<T> keywords) Convenience method; Creates and returns a token stream that generates a token for each keyword in the given collection, "as is", without any transforming text analysis.
void	reset() Resets the MemoryIndex to its initial state and recycles all internal buffers.
float	search(Query query) Convenience method that efficiently returns the relevance score by matching this index against the given Lucene query expression.
void	setSimilarity(Similarity similarity) Set the Similarity to be used for calculating field norms
String	toString() Returns a String representation of the index data for debugging purposes.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

MemoryIndex

public MemoryIndex()

Constructs an empty instance that will not store offsets or payloads.

MemoryIndex

public MemoryIndex(boolean storeOffsets)

Constructs an empty instance that can optionally store the start and end character offset of each token term in the text. This can be useful for highlighting of hit locations with the Lucene highlighter package. But it will not store payloads; use another constructor for that.

Parameters:

storeOffsets - whether or not to store the start and end character offset of each token term in the text

MemoryIndex

public MemoryIndex(boolean storeOffsets,
           boolean storePayloads)

Constructs an empty instance with the option of storing offsets and payloads.

Parameters:

storeOffsets - store term offsets at each position

storePayloads - store term payloads at each position

Method Detail

addField

public void addField(String fieldName,
            String text,
            Analyzer analyzer)

Convenience method; Tokenizes the given field text and adds the resulting terms to the index; Equivalent to adding an indexed non-keyword Lucene Field that is tokenized, not stored, termVectorStored with positions (or termVectorStored with positions and offsets),

Parameters:

fieldName - a name to be associated with the text

text - the text to tokenize and index.

analyzer - the analyzer to use for tokenization

keywordTokenStream

public <T> TokenStream keywordTokenStream(Collection<T> keywords)

Convenience method; Creates and returns a token stream that generates a token for each keyword in the given collection, "as is", without any transforming text analysis. The resulting token stream can be fed into addField(String, TokenStream), perhaps wrapped into another TokenFilter, as desired.

Parameters:

keywords - the keywords to generate tokens for

Returns:

the corresponding token stream

addField

public void addField(String fieldName,
            TokenStream stream)

Equivalent to addField(fieldName, stream, 1.0f).

Parameters:

fieldName - a name to be associated with the text

stream - the token stream to retrieve tokens from

addField

public void addField(String fieldName,
            TokenStream stream,
            float boost)

Iterates over the given token stream and adds the resulting terms to the index; Equivalent to adding a tokenized, indexed, termVectorStored, unstored, Lucene Field. Finally closes the token stream. Note that untokenized keywords can be added with this method via keywordTokenStream(Collection), the Lucene KeywordTokenizer or similar utilities.

Parameters:

fieldName - a name to be associated with the text

stream - the token stream to retrieve tokens from.

boost - the boost factor for hits for this field

See Also:

Field

addField

public void addField(String fieldName,
            TokenStream stream,
            float boost,
            int positionIncrementGap)

Iterates over the given token stream and adds the resulting terms to the index; Equivalent to adding a tokenized, indexed, termVectorStored, unstored, Lucene Field. Finally closes the token stream. Note that untokenized keywords can be added with this method via keywordTokenStream(Collection), the Lucene KeywordTokenizer or similar utilities.

Parameters:

fieldName - a name to be associated with the text

stream - the token stream to retrieve tokens from.

boost - the boost factor for hits for this field

positionIncrementGap - the position increment gap if fields with the same name are added more than once

See Also:

Field

addField

public void addField(String fieldName,
            TokenStream tokenStream,
            float boost,
            int positionIncrementGap,
            int offsetGap)

Iterates over the given token stream and adds the resulting terms to the index; Equivalent to adding a tokenized, indexed, termVectorStored, unstored, Lucene Field. Finally closes the token stream. Note that untokenized keywords can be added with this method via keywordTokenStream(Collection), the Lucene KeywordTokenizer or similar utilities.

Parameters:

fieldName - a name to be associated with the text

tokenStream - the token stream to retrieve tokens from. It's guaranteed to be closed no matter what.

boost - the boost factor for hits for this field

positionIncrementGap - the position increment gap if fields with the same name are added more than once

offsetGap - the offset gap if fields with the same name are added more than once

See Also:

Field

setSimilarity

public void setSimilarity(Similarity similarity)

Set the Similarity to be used for calculating field norms

createSearcher

public IndexSearcher createSearcher()

Creates and returns a searcher that can be used to execute arbitrary Lucene queries and to collect the resulting query results as hits.

Returns:

a searcher

freeze

public void freeze()

Prepares the MemoryIndex for querying in a non-lazy way.

After calling this you can query the MemoryIndex from multiple threads, but you cannot subsequently add new data.

search

public float search(Query query)

Convenience method that efficiently returns the relevance score by matching this index against the given Lucene query expression.

Parameters:

query - an arbitrary Lucene query to run against this index

Returns:

the relevance score of the matchmaking; A number in the range [0.0 .. 1.0], with 0.0 indicating no match. The higher the number the better the match.

toString

public String toString()

Returns a String representation of the index data for debugging purposes.

Overrides:

toString in class Object

Returns:

the string representation

reset

public void reset()

Resets the MemoryIndex to its initial state and recycles all internal buffers.