Optimize the core tokenizers/analyzers & deprecate Token.termText
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Key: LUCENE-969
URL: https://issues.apache.org/jira/browse/LUCENE-969
Project: Lucene - Java
Issue Type: Improvement
Components: Analysis
Affects Versions: 2.3
Reporter: Michael McCandless
Assignee: Michael McCandless
Priority: Minor
Fix For: 2.3
There is some "low hanging fruit" for optimizing the core tokenizers
and analyzers:
- Re-use a single Token instance during indexing instead of creating
a new one for every term. To do this, I added a new method "Token
next(Token result)" (Doron's suggestion) which means TokenStream
may use the "Token result" as the returned Token, but is not
required to (ie, can still return an entirely different Token if
that is more convenient). I added default implementations for
both next() methods in TokenStream.java so that a TokenStream can
choose to implement only one of the next() methods.
- Use "char[] termBuffer" in Token instead of the "String
termText".
Token now maintains a char[] termBuffer for holding the term's
text. Tokenizers & filters should retrieve this buffer and
directly alter it to put the term text in or change the term
text.
I only deprecated the termText() method. I still allow the ctors
that pass in String termText, as well as setTermText(String), but
added a NOTE about performance cost of using these methods. I
think it's OK to keep these as convenience methods?
After the next release, when we can remove the deprecated API, we
should clean up Token.java to no longer maintain "either String or
char[]" (and the initTermBuffer() private method) and always use
the char[] termBuffer instead.
- Re-use TokenStream instances across Fields & Documents instead of
creating a new one for each doc. To do this I added an optional
"reusableTokenStream(...)" to Analyzer which just defaults to
calling tokenStream(...), and then I implemented this for the core
analyzers.
I'm using the patch from LUCENE-967 for benchmarking just
tokenization.
The changes above give 21% speedup (742 seconds -> 585 seconds) for
LowerCaseTokenizer -> StopFilter -> PorterStemFilter chain, tokenizing
all of Wikipedia, on JDK 1.6 -server -Xmx1024M, Debian Linux, RAID 5
IO system (best of 2 runs).
If I pre-break Wikipedia docs into 100 token docs then it's 37% faster
(1236 sec -> 774 sec), I think because of re-using TokenStreams across
docs.
I'm just running with this alg and recording the elapsed time:
analyzer=org.apache.lucene.analysis.LowercaseStopPorterAnalyzer
doc.tokenize.log.step=50000
docs.file=/lucene/wikifull.txt
doc.maker=org.apache.lucene.benchmark.byTask.feeds.LineDocMaker
doc.tokenized=true
doc.maker.forever=false
{ReadTokens > : *
See this thread for discussion leading up to this:
http://www.gossamer-threads.com/lists/lucene/java-dev/51283
I also fixed Token.toString() to work correctly when termBuffer is
used (and added unit test).
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