There are plenty of Lucene users that do go 1000 in. We've been
calling it "deep paging" at LI. I like that name :)
- Mark
http://www.lucidimagination.com (mobile)
On Nov 2, 2009, at 6:04 PM, "Jake Mannix (JIRA)" <j...@apache.org>
wrote:
[ https://issues.apache.org/jira/browse/LUCENE-1997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12772741#action_12772741
]
Jake Mannix commented on LUCENE-1997:
-------------------------------------
The current concern is to do with the memory? I'm more concerned
with the weird "java ghosts" that are flying around, sometimes
swaying results by 20-40%... the memory could only be an issue on a
setup with hundreds of segments and sorting the top 1000 values (do
we really try to optimize for this performance case?). In the
normal case (no more than tens of segments, and the top 10 or 100
hits), we're talking about what, 100-1000 PQ entries?
Explore performance of multi-PQ vs single-PQ sorting API
--------------------------------------------------------
Key: LUCENE-1997
URL: https://issues.apache.org/jira/browse/LUCENE-1997
Project: Lucene - Java
Issue Type: Improvement
Components: Search
Affects Versions: 2.9
Reporter: Michael McCandless
Assignee: Michael McCandless
Attachments: LUCENE-1997.patch, LUCENE-1997.patch,
LUCENE-1997.patch, LUCENE-1997.patch, LUCENE-1997.patch,
LUCENE-1997.patch, LUCENE-1997.patch, LUCENE-1997.patch,
LUCENE-1997.patch
Spinoff from recent "lucene 2.9 sorting algorithm" thread on java-
dev,
where a simpler (non-segment-based) comparator API is proposed that
gathers results into multiple PQs (one per segment) and then merges
them in the end.
I started from John's multi-PQ code and worked it into
contrib/benchmark so that we could run perf tests. Then I generified
the Python script I use for running search benchmarks (in
contrib/benchmark/sortBench.py).
The script first creates indexes with 1M docs (based on
SortableSingleDocSource, and based on wikipedia, if available). Then
it runs various combinations:
* Index with 20 balanced segments vs index with the "normal" log
segment size
* Queries with different numbers of hits (only for wikipedia index)
* Different top N
* Different sorts (by title, for wikipedia, and by random string,
random int, and country for the random index)
For each test, 7 search rounds are run and the best QPS is kept. The
script runs singlePQ then multiPQ, and records the resulting best QPS
for each and produces table (in Jira format) as output.
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