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https://issues.apache.org/jira/browse/LUCENE-10404?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17572080#comment-17572080
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Julie Tibshirani commented on LUCENE-10404:
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Checking I understand the numbers: in addition to indexing slowing down, it
looks like search latency is a bit worse too. The main difference is that the
graph is better connected (higher maxConn) and we explore more nodes during
index (beamWidth). The slowdown is not huge but significant (~5% slower for
both index + search).
This would support the theory that for larger numbers of 'visited' nodes, the
IntIntHashMap solution doesn't perform as well. Maybe we could consider a more
custom approach as Adrien suggests ?
> Use hash set for visited nodes in HNSW search?
> ----------------------------------------------
>
> Key: LUCENE-10404
> URL: https://issues.apache.org/jira/browse/LUCENE-10404
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Julie Tibshirani
> Priority: Minor
>
> While searching each layer, HNSW tracks the nodes it has already visited
> using a BitSet. We could look into using something like IntHashSet instead. I
> tried out the idea quickly by switching to IntIntHashMap (which has already
> been copied from hppc) and saw an improvement in index performance.
> *Baseline:* 760896 msec to write vectors
> *Using IntIntHashMap:* 733017 msec to write vectors
> I noticed search performance actually got a little bit worse with the change
> -- that is something to look into.
> For background, it's good to be aware that HNSW can visit a lot of nodes. For
> example, on the glove-100-angular dataset with ~1.2 million docs, HNSW search
> visits ~1000 - 15,000 docs depending on the recall. This number can increase
> when searching with deleted docs, especially if you hit a "pathological" case
> where the deleted docs happen to be closest to the query vector.
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