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https://issues.apache.org/jira/browse/LUCENE-1997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12772809#action_12772809
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Mark Miller edited comment on LUCENE-1997 at 11/3/09 1:07 AM:
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Actually - while I cannot share any current info I have, I'll share an example
from my last job. I worked on a system that librarians used to maintain a
newspaper archive. The feed for the paper would come in daily and the
librarians would "enhance" the data - adding keywords, breaking up stories,
etc. Then reporters or end users could search this data. Librarians, who I
learned are odd in there requirements by nature, insisted on bringing in
thousands of results that they could scroll through at a time. This was
demanded at paper after paper. So we regularly fed back up to 5000 thousand
results at a time with our software (though they'd have preferred no limit -
"what are you talking about ! I want them all!" - we made them click more
buttons for that :) ). Thats just one small example, but I know for a fact
there are many, many more.
*edit*
We also actually ran into many situations were there were lots of segments in
this scenario as well - before I knew better, I'd regularly build the indexes
with a high merge factor for speed - and then be stuck, unable to optimize
because it killed performance and newspapers need to be up pretty much 24/7 -
without bringing there server to a crawl - so I would be unable to optimize
(this was before you could optimize down to n segments and work slowly over
time). Not the greatest example, but a situation I found myself in.
was (Author: markrmil...@gmail.com):
Actually - while I cannot share any current info I have, I'll share an
example from my last job. I worked on a system that librarians used to maintain
a newspaper archive. The feed for the paper would come in daily and the
librarians would "enhance" the data - adding keywords, breaking up stories,
etc. Then reporters or end users could search this data. Librarians, who I
learned are odd in there requirements by nature, insisted on bringing in
thousands of results that they could scroll through at a time. This was
demanded at paper after paper. So we regularly fed back up to 5000 thousand
results at a time with our software (though they'd have preferred no limit -
"what are you talking about ! I want them all!" - we made them click more
buttons for that :) ). Thats just one small example, but I know for a fact
there are many, many more.
> 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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