Mark, I'm not stuck on single examples, I'm thinking about all of lucene land: what tiny fraction of people need the combined intersection of

  a) many many segments

AND

  b) deep paging

AND

  c) high QPS

AND

  e) can't handle another 40MB of RAM usage.

Only people in the intersection of all of those bitsets would possibly have a problem with the memory requirements of multiPQ. 

On Tue, Nov 3, 2009 at 12:32 PM, Mark Miller <markrmiller@gmail.com> wrote:
Your obviously too stuck on single examples. We have to consider everyone in lucene land.

I'm against 2 Apis. A custom search is advanced - it's not worth the baggage of maintaining two APIs or be limited by the APIs and back compat when moving forward.

If the advantage of the second API is just going to be it's simpler, I'm not for it currently.

- Mark

http://www.lucidimagination.com (mobile)

On Nov 3, 2009, at 10:51 AM, "Jake Mannix (JIRA)" <jira@apache.org> wrote:


  [ https://issues.apache.org/jira/browse/LUCENE-1997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12773114#action_12773114 ]

Jake Mannix commented on LUCENE-1997:
-------------------------------------

bq. Since each approach has distinct advantages, why not offer both ("simple" and "expert") comparator extensions APIs?

+1 from me on this one, as long as the simpler one is around.  I'll bet we'll find that we regret keeping the "expert" one by 3.2 or so though, but I'll take any compromise which gets the simpler API in there.

bq. Don't forget that this is multiplied by however many queries are currently in flight.

Sure, so if you're running with 100 queries per second on a single shard (pretty fast!), with 100 segments, and you want to do sorting by value on the top 1000 values (how far down the long tail of extreme cases are we at now?  Do librarians hit their search servers with 100 QPS and have indices poorly built with hundreds of segments and can't take downtime to *ever* optimize?), we're now talking about 40MB.

*Forty megabytes*.  On a beefy machine which is supposed to be handling 100QPS across an index big enough to need 100 segments.  How much heap would such a machine already be allocating?  4GB?  6?  More?

We're talking about less than 1% of the heap is being used by the multiPQ approach in comparison to singlePQ.

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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