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https://issues.apache.org/jira/browse/LUCENE-8580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17528360#comment-17528360
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Michael Sokolov commented on LUCENE-8580:
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It seems we have generally stuck with one or a handful of files per format.
Presumably we don't want to fragment into many small files in order to enable
the OS to handle I/O more optimally? I think the key is not to impact search
performance. But maybe start by writing many files in parallel just to see what
is the best we can achieve for parallel merging with this approach and worry
about connecting them together later? It shouldn't be hard to do the stitching,
just block copies + rewriting the metadata?
> 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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