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https://issues.apache.org/jira/browse/LUCENE-10590?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17541185#comment-17541185
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Julie Tibshirani edited comment on LUCENE-10590 at 5/23/22 10:26 PM:
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I don't have a deep understanding of what's happening, but wanted to share this
discussion from hnswlib:
[https://github.com/nmslib/hnswlib/issues/263#issuecomment-739549454]. It looks
like HNSW can really fall apart if there are a lot of duplicate vectors. The
duplicates all link to each other, creating a highly disconnected graph. I've
often seen libraries recommend that users deduplicate vectors before indexing
them
([https://github.com/facebookresearch/faiss/wiki/FAQ#searching-duplicate-vectors-is-slow).]
I guess indexing all zero vectors is an extreme version of this!
was (Author: julietibs):
I don't have a deep understanding of what's happening, but wanted to share this
discussion from hnswlib:
[https://github.com/nmslib/hnswlib/issues/263#issuecomment-739549454.] It looks
like HNSW can really fall apart if there are a lot of duplicate vectors. The
duplicates all link to each other, creating a highly disconnected graph. I've
often seen libraries recommend that users deduplicate vectors before indexing
them
([https://github.com/facebookresearch/faiss/wiki/FAQ#searching-duplicate-vectors-is-slow).]
I guess indexing all zero vectors is an extreme version of this!
> Indexing all zero vectors leads to heat death of the universe
> -------------------------------------------------------------
>
> Key: LUCENE-10590
> URL: https://issues.apache.org/jira/browse/LUCENE-10590
> Project: Lucene - Core
> Issue Type: Bug
> Reporter: Michael Sokolov
> Priority: Major
>
> By accident while testing something else, I ran a luceneutil test indexing 1M
> 100d vectors where all the vectors were all zeroes. This caused indexing to
> take a very long time (~40x normal - it did eventually complete) and the
> search performance was similarly bad. We should not degrade by orders of
> magnitude with even the worst data though.
> I'm not entirely sure what the issue is, but perhaps as long as we keep
> finding hits that are "better" we keep exploring the graph, where better
> means (score, -docid) >= (lowest score, -docid). If that's right and all docs
> have the same score, then we probably need to either switch to > (but this
> could lead to poorer recall in normal cases) or introduce some kind of
> minimum score threshold?
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