The drawback is that it requires a fair amount of understanding of intricate Lucene internals, pulling those pieces together and adapting them as required for the seemingly simple "float match(String text, Query query)".
I might give it a shot but I'm not sure I'll be able to pull this off! Is there any similar code I could look at as a starting point?
Wolfgang.
On Apr 14, 2005, at 1:13 PM, Robert Engels wrote:
I think you are not approaching this the correct way.
Pseudo code:
Subclass IndexReader.
Get tokens from String 'document' using Lucene analyzers.
Build simple hash-map based data structures using tokens for terms, and term
positions.
reimplement termDocs() and termPositions() to use the structures from above.
run searches.
start again with next document.
-----Original Message----- From: Wolfgang Hoschek [mailto:[EMAIL PROTECTED] Sent: Thursday, April 14, 2005 2:56 PM To: java-dev@lucene.apache.org Subject: Re: [Performance] Streaming main memory indexing of single strings
Otis, this might be a misunderstanding.
- I'm not calling optimize(). That piece is commented out you if look again at the code. - The *streaming* use case requires that for each query I add one (and only one) document (aka string) to an empty index:
repeat N times (where N is millions or billions): add a single string (aka document) to an empty index query the index drop index (or delete it's document)
with the following API being called N times: float match(String text, Query query)
So there's no possibility of adding many documents and thereafter running the query. This in turn seems to mean that the IndexWriter can't be kept open - unless I manually delete each document after each query to repeatedly reuse the RAMDirectory, which I've also tried before without any significant performance gain - deletion seems to have substantial overhead in itself. Perhaps it would be better if there were a Directory.deleteAllDocuments() or similar. Did you have some other approach in mind?
As I said, Lucene's design doesn't seem to fit this streaming use case pattern well. In *this* scenario one could easily do without any locking, and without byte level organization in RAMDirectory and RAMFile, etc because a single small string isn't a large persistent multi-document index.
For some background, here's a small example for the kind of XQuery functionality Nux/Lucene integration enables:
(: An XQuery that finds all books authored by James that have something to do with "fish", sorted by relevance :) declare namespace lucene = "java:nux.xom.xquery.XQueryUtil"; declare variable $query := "fish*~";
for $book in /books/book[author="James" and lucene:match(string(.), $query) > 0.0] let $score := lucene:match(string($book), $query) order by $score descending return (<score>{$score}</score>, $book)
More interestingly one can use this for classifying and routing XML messages based on rules (i.e. queries) inspecting their content...
Any other clues about potential improvements would be greatly appreciated.
Wolfgang.
On Apr 13, 2005, at 10:09 PM, Otis Gospodnetic wrote:
It looks like you are calling that IndexWriter code in some loops,
opening it and closing it in every iteration of the loop and also
calling optimize. All of those things could be improved.
Keep your IndexWriter open, don't close it, and optimize the index only
once you are done adding documents to it.
See the highlights and the snipets in the first hit: http://www.lucenebook.com/search?query=when+to+optimize
Otis
--- Wolfgang Hoschek <[EMAIL PROTECTED]> wrote:
---------------------------------------------------------------------Hi,
I'm wondering if anyone could let me know how to improve Lucene performance for "streaming main memory indexing of single strings". This would help to effectively integrate Lucene with the Nux XQuery engine.
Below is a small microbenchmark simulating STREAMING XQuery fulltext search as typical for XML network routers, message queuing system, P2P networks, etc. In this on-the-fly main memory indexing scenario, each
individual string is immediately matched as soon as it becomes available without any persistance involved. This usage scenario and corresponding performance profile is quite different in comparison to
fulltext search over persistent (read-mostly) indexes.
The benchmark runs at some 3000 lucene queries/sec (lucene-1.4.3) which is unfortunate news considering the XQuery engine can easily walk hundreds of thousands of XML nodes per second. Ideally I'd like to run at some 100000 queries/sec. Runnning this through the JDK 1.5 profiler it seems that most time is spent in and below the following calls:
writer = new IndexWriter(dir, analyzer, true); writer.addDocument(...); writer.close();
I tried quite a few variants of the benchmark with various options, unfortunately with little or no effect. Lucene just does not seem to designed to do this sort of "transient single string index" thing. All code paths related to opening, closing, reading, writing, querying and object creation seem to be designed for large persistent indexes.
Any advice on what I'm missing or what could be done about it would be greatly appreciated.
Wolfgang.
P.S. the benchmark code is attached as a file below:
package nux.xom.pool;
import java.io.IOException; //import java.io.Reader;
import org.apache.lucene.analysis.Analyzer; //import org.apache.lucene.analysis.LowerCaseTokenizer; //import org.apache.lucene.analysis.PorterStemFilter; //import org.apache.lucene.analysis.SimpleAnalyzer; //import org.apache.lucene.analysis.TokenStream; import org.apache.lucene.analysis.standard.StandardAnalyzer; import org.apache.lucene.document.Document; import org.apache.lucene.document.Field; //import org.apache.lucene.index.IndexReader; import org.apache.lucene.index.IndexWriter; import org.apache.lucene.queryParser.ParseException; import org.apache.lucene.queryParser.QueryParser; import org.apache.lucene.search.Hits; import org.apache.lucene.search.IndexSearcher; import org.apache.lucene.search.Query; import org.apache.lucene.search.Searcher; import org.apache.lucene.store.Directory; import org.apache.lucene.store.RAMDirectory;
public final class LuceneMatcher { // TODO: make non-public
private final Analyzer analyzer; // private final Directory dir = new RAMDirectory();
public LuceneMatcher() { this(new StandardAnalyzer()); // this(new SimpleAnalyzer()); // this(new StopAnalyzer()); // this(new Analyzer() { // public final TokenStream tokenStream(String fieldName, Reader reader) { // return new PorterStemFilter(new LowerCaseTokenizer(reader)); // } // }); }
public LuceneMatcher(Analyzer analyzer) { if (analyzer == null) throw new IllegalArgumentException("analyzer must not be null"); this.analyzer = analyzer; }
public Query parseQuery(String expression) throws ParseException { QueryParser parser = new QueryParser("content", analyzer); // parser.setPhraseSlop(0); return parser.parse(expression); }
/** * Returns the relevance score by matching the given index against the given * Lucene query expression. The index must not contain more than one Lucene * "document" (aka string to be searched). */ public float match(Directory index, Query query) { Searcher searcher = null; try { searcher = new IndexSearcher(index); Hits hits = searcher.search(query); float score = hits.length() > 0 ? hits.score(0) : 0.0f; return score; } catch (IOException e) { // should never happen (RAMDirectory) throw new RuntimeException(e); } finally { try { if (searcher != null) searcher.close(); } catch (IOException e) { // should never happen (RAMDirectory) throw new RuntimeException(e); } } }
// public float match(String text, Query query) { // return match(createIndex(text), query); // }
public Directory createIndex(String text) { Directory dir = new RAMDirectory(); IndexWriter writer = null; try { writer = new IndexWriter(dir, analyzer, true); // writer.setUseCompoundFile(false); // writer.mergeFactor = 2; // writer.minMergeDocs = 1; // writer.maxMergeDocs = 1;
writer.addDocument(createDocument(text)); // writer.optimize(); return dir; } catch (IOException e) { // should never happen (RAMDirectory) throw new RuntimeException(e); } finally { try { if (writer != null) writer.close(); } catch (IOException e) { // should never happen (RAMDirectory) throw new RuntimeException(e); } } }
private Document createDocument(String content) { Document doc = new Document(); doc.add(Field.UnStored("content", content)); // doc.add(Field.Text("x", content)); return doc; }
/** * Lucene microbenchmark simulating STREAMING XQuery fulltext search as * typical for XML network routers, message queuing system, P2P networks, * etc. In this on-the-fly main memory indexing scenario, each individual * string is immediately matched as soon as it becomes available without any * persistance involved. This usage scenario and corresponding performance * profile is quite different in comparison to fulltext search over * persistent (read-mostly) indexes. * * Example XPath: count(/table/row[lucene:match(string(./firstname), * "James") > 0.0]) */ public static void main(String[] args) throws Exception { int k = -1; int runs = 5; if (args.length > ++k) runs = Integer.parseInt(args[k]);
int nodes = 10000; if (args.length > ++k) nodes = Integer.parseInt(args[k]);
String content = "James is out in the woods"; if (args.length > ++k) content = args[k];
String expression = "James"; if (args.length > ++k) expression = args[k];
LuceneMatcher matcher = new LuceneMatcher(); Query query = matcher.parseQuery(expression); // to be reused N times
for (int r = 0; r < runs; r++) { long start = System.currentTimeMillis(); int matches = 0;
for (int i = 0; i < nodes; i++) { // if (LuceneUtil.match(content + i, expression) > 0.0f) { if (matcher.match(matcher.createIndex(content + i), query) > 0.0f) { matches++; } }
long end = System.currentTimeMillis(); System.out.println("matches=" + matches); System.out.println("secs=" + ((end-start) / 1000.0f)); System.out.println("queries/sec=" + (nodes / ((end-start) / 1000.0f))); System.out.println(); } } }
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