The memory issue is just one example of something that's somewhat
worse - I don't see it as a deciding faster. If things were clarified
to be decidedly faster with multi queue, and not 50% worse at 1000
hits, I'd be for the change, more memory or not.
- Mark
http://www.lucidimagination.com (mobile)
On Nov 3, 2009, at 12:42 PM, Jake Mannix <jake.man...@gmail.com> wrote:
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 <markrmil...@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)" <j...@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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