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https://issues.apache.org/jira/browse/LUCENE-8580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17509385#comment-17509385
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Vigya Sharma commented on LUCENE-8580:
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I started looking into this, and had some early questions... What would be the
right benchmark to look at, for measuring any improvements here? Would it be
the Indexing Throughput benchmark?
Should we add some benchmark specifically for merge related performance, like
triggering a force merge? I suppose it wont be a measure of qps, but rather,
the time to complete a merge on a given index? Do we have such benchmarks
already?
Apologies if these are obvious things, I've only recently started looking at
benchmarks. Will update here if I'm able to figure something out about these
myself..
> Make segment merging parallel in SegmentMerger
> ----------------------------------------------
>
> Key: LUCENE-8580
> URL: https://issues.apache.org/jira/browse/LUCENE-8580
> Project: Lucene - Core
> Issue Type: Task
> Reporter: Dawid Weiss
> Assignee: Dawid Weiss
> Priority: Minor
> Attachments: LUCENE-8580.patch
>
>
> A placeholder issue stemming from the discussion on the mailing list [1]. Not
> of any high priority.
> At the moment any merging from N segments into one will happen sequentially
> for each data structure involved in a segment (postings, norms, points,
> etc.). If the input segments are large, the CPU (and I/O) are mostly unused
> and the process takes a long time.
> Merging of these data structures is mostly independent of each other, so it'd
> be interesting to see if we can speed things up by allowing them to run
> concurrently. I investigated this on a 40GB index with 22 segments,
> force-merging this into 1 segment (of similar size). Quick and dirty patch
> attached.
> I see some improvement, although it's not by much; the largest component
> dominates everything else.
> Results from an 8-core CPU.
> Before:
> {code}
> SM 0 [2018-11-30T09:21:11.662Z; main]: 347237 msec to merge stored fields
> [41922110 docs]
> SM 0 [2018-11-30T09:21:18.236Z; main]: 6562 msec to merge norms [41922110
> docs]
> SM 0 [2018-11-30T09:33:53.746Z; main]: 755507 msec to merge postings
> [41922110 docs]
> SM 0 [2018-11-30T09:33:53.746Z; main]: 0 msec to merge doc values [41922110
> docs]
> SM 0 [2018-11-30T09:33:53.746Z; main]: 0 msec to merge points [41922110 docs]
> SM 0 [2018-11-30T09:33:53.746Z; main]: 7 msec to write field infos [41922110
> docs]
> IW 0 [2018-11-30T09:33:56.124Z; main]: merge time 1112238 msec for 41922110
> docs
> {code}
> After:
> {code}
> SM 0 [2018-11-30T10:16:42.179Z; ForkJoinPool.commonPool-worker-1]: 8189 msec
> to merge norms
> SM 0 [2018-11-30T10:16:42.195Z; ForkJoinPool.commonPool-worker-3]: 0 msec to
> merge doc values
> SM 0 [2018-11-30T10:16:42.195Z; ForkJoinPool.commonPool-worker-3]: 0 msec to
> merge points
> SM 0 [2018-11-30T10:16:42.211Z; ForkJoinPool.commonPool-worker-1]: merge
> store matchedCount=22 vs 22
> SM 0 [2018-11-30T10:23:24.574Z; ForkJoinPool.commonPool-worker-1]: 402381
> msec to merge stored fields [41922110 docs]
> SM 0 [2018-11-30T10:32:20.862Z; ForkJoinPool.commonPool-worker-2]: 938668
> msec to merge postings
> IW 0 [2018-11-30T10:32:23.513Z; main]: merge time 950249 msec for 41922110
> docs
> {code}
> Ideally, one would need to push forkjoin into individual subroutines so that,
> for example, postings utilize concurrency when merging (pulling blocks of
> terms concurrently from the input, calculating statistics, etc. and then
> pushing in an ordered fashion to the codec).
> [1] https://markmail.org/thread/dtejwq42qagykeac
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