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https://issues.apache.org/jira/browse/LUCENE-10216?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17437255#comment-17437255
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Adrien Grand commented on LUCENE-10216:
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One bit from this proposal I'm not fully comfortable with is the fact that
Lucene would merge each reader independently when the flag is set. While I
understand why this makes sense for Amazon product search since you want to
have many segments to make search as concurrent as possible, this is also a bit
wasteful since you'd be paying the cost of doing a merge but without getting
the benefits of reducing the segment count.
What about a slightly different proposal where merges would always be executed
via the configured merge scheduler, and addIndexes(CodecReader[]) would get an
additional {{boolean doWait}} parameter like {{IndexWriter#forceMerge}}?
Then you could still do what you want by using {{ConcurrentMergeScheduler}},
passing codec readers one by one to {{CodecReader#addIndexes}} with
{{doWait=false}} and get resulting remapped segments as quickly as possible.
Yet Lucene would retain the current behaviour where {{addIndexes}} also tries
to avoid doing wasteful singleton merges?
> Add concurrency to addIndexes(CodecReader…) API
> -----------------------------------------------
>
> Key: LUCENE-10216
> URL: https://issues.apache.org/jira/browse/LUCENE-10216
> Project: Lucene - Core
> Issue Type: Improvement
> Components: core/index
> Reporter: Vigya Sharma
> Priority: Major
>
> I work at Amazon Product Search, and we use Lucene to power search for the
> e-commerce platform. I’m working on a project that involves applying
> metadata+ETL transforms and indexing documents on n different _indexing_
> boxes, combining them into a single index on a separate _reducer_ box, and
> making it available for queries on m different _search_ boxes (replicas).
> Segments are asynchronously copied from indexers to reducers to searchers as
> they become available for the next layer to consume.
> I am using the addIndexes API to combine multiple indexes into one on the
> reducer boxes. Since we also have taxonomy data, we need to remap facet field
> ordinals, which means I need to use the {{addIndexes(CodecReader…)}} version
> of this API. The API leverages {{SegmentMerger.merge()}} to create segments
> with new ordinal values while also merging all provided segments in the
> process.
> _This is however a blocking call that runs in a single thread._ Until we have
> written segments with new ordinal values, we cannot copy them to searcher
> boxes, which increases the time to make documents available for search.
> I was playing around with the API by creating multiple concurrent merges,
> each with only a single reader, creating a concurrently running 1:1
> conversion from old segments to new ones (with new ordinal values). We follow
> this up with non-blocking background merges. This lets us copy the segments
> to searchers and replicas as soon as they are available, and later replace
> them with merged segments as background jobs complete. On the Amazon dataset
> I profiled, this gave us around 2.5 to 3x improvement in addIndexes() time.
> Each call was given about 5 readers to add on average.
> This might be useful add to Lucene. We could create another {{addIndexes()}}
> API with a {{boolean}} flag for concurrency, that internally submits multiple
> merge jobs (each with a single reader) to the {{ConcurrentMergeScheduler}},
> and waits for them to complete before returning.
> While this is doable from outside Lucene by using your thread pool, starting
> multiple addIndexes() calls and waiting for them to complete, I felt it needs
> some understanding of what addIndexes does, why you need to wait on the merge
> and why it makes sense to pass a single reader in the addIndexes API.
> Out of box support in Lucene could simplify this for folks a similar use case.
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