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https://issues.apache.org/jira/browse/LUCENE-1536?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12672005#action_12672005
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Michael McCandless commented on LUCENE-1536:
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OK I tested a different approach for matching when the filter is
relatively sparse filter (<= 10% of index size), by implementing a
simplistic prototype "random access" scorer API (JumpScorer), which
only exposes the method "boolean jump(int docID)" that returns true if
that doc matches, else false (and no next() under the hood).
I only implemented JumpScorer for pure AND/OR queries (ie no excluded
terms), so I only test for these queries.
I convert the filter to SortedVIntList up front, so iteration is fast.
Then during matching I iterate through each doc in the filter, and ask
the random-access scoring API to test whether it accepts the doc, and
collect it if so.
This only performs better when the filter is sparse relative to the
query. Once we merge Query/Filter, I think this optimization can more
generally be used whenever one sub-query is very restrictive compared
to the rest of the sub-queries.
This is basically the reverse of what I first tested, which was to
take a filter that can support random access API and "distribute" it
down to each TermQuery, which gives very good gains especially when
filter is in the middle of the sparse/dense range. Whereas this test
keeps the iterator API on the filter, but switches to a random access
API on the scorer.
Results:
||%tg Filter||Query||Hits||QPS||QPSNew||%tg change||
|1%|1-2| 5363| 47.4| 66.7| 40.7%|
|2%|1-2| 10675| 37.6| 50.6| 34.6%|
|5%|1-2| 26880| 28.6| 37.0| 29.4%|
|10%|1-2| 53673| 23.8| 26.2| 10.1%|
|1%|1-4| 6544| 26.9| 37.2| 38.3%|
|2%|1-4| 13062| 21.2| 29.2| 37.7%|
|5%|1-4| 32815| 16.1| 21.2| 31.7%|
|10%|1-4| 65491| 13.2| 15.2| 15.2%|
|1%|1-10| 8406| 13.0| 17.6| 35.4%|
|2%|1-10| 16756| 10.2| 14.2| 39.2%|
|5%|1-10| 41937| 7.7| 10.3| 33.8%|
|10%|1-10| 83828| 6.3| 7.8| 23.8%|
|1%|+1-2| 2308| 63.6| 82.9| 30.3%|
|2%|+1-2| 4621| 49.9| 60.7| 21.6%|
|5%|+1-2| 11706| 35.8| 47.6| 33.0%|
|10%|+1-2| 23272| 28.3| 35.5| 25.4%|
|1%|+1-4| 923| 34.4| 58.0| 68.6%|
|2%|+1-4| 1849| 28.5| 44.9| 57.5%|
|5%|+1-4| 4794| 22.0| 33.7| 53.2%|
|10%|+1-4| 9595| 19.8| 25.4| 28.3%|
|1%|+1-10| 292| 17.0| 36.6|115.3%|
|2%|+1-10| 579| 15.2| 30.2| 98.7%|
|5%|+1-10| 1517| 13.5| 22.2| 64.4%|
|10%|+1-10| 2999| 12.4| 17.4| 40.3%|
> if a filter can support random access API, we should use it
> -----------------------------------------------------------
>
> Key: LUCENE-1536
> URL: https://issues.apache.org/jira/browse/LUCENE-1536
> Project: Lucene - Java
> Issue Type: Improvement
> Components: Search
> Affects Versions: 2.4
> Reporter: Michael McCandless
> Assignee: Michael McCandless
> Priority: Minor
> Attachments: LUCENE-1536.patch
>
>
> I ran some performance tests, comparing applying a filter via
> random-access API instead of current trunk's iterator API.
> This was inspired by LUCENE-1476, where we realized deletions should
> really be implemented just like a filter, but then in testing found
> that switching deletions to iterator was a very sizable performance
> hit.
> Some notes on the test:
> * Index is first 2M docs of Wikipedia. Test machine is Mac OS X
> 10.5.6, quad core Intel CPU, 6 GB RAM, java 1.6.0_07-b06-153.
> * I test across multiple queries. 1-X means an OR query, eg 1-4
> means 1 OR 2 OR 3 OR 4, whereas +1-4 is an AND query, ie 1 AND 2
> AND 3 AND 4. "u s" means "united states" (phrase search).
> * I test with multiple filter densities (0, 1, 2, 5, 10, 25, 75, 90,
> 95, 98, 99, 99.99999 (filter is non-null but all bits are set),
> 100 (filter=null, control)).
> * Method high means I use random-access filter API in
> IndexSearcher's main loop. Method low means I use random-access
> filter API down in SegmentTermDocs (just like deleted docs
> today).
> * Baseline (QPS) is current trunk, where filter is applied as iterator up
> "high" (ie in IndexSearcher's search loop).
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