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https://issues.apache.org/jira/browse/LUCENE-10069?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17404690#comment-17404690
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Michael Sokolov edited comment on LUCENE-10069 at 8/25/21, 8:01 PM:
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also, I found it is easy to reproduce this several times by running 
{{TestKnnVectorQuery }}with -Ptests.iters=1000


was (Author: sokolov):
also, I found it is easy to reproduce this several times by running  
{TestKnnVectorQuery} with {-Ptests.iters=1000}

> HNSW can miss results with very large k
> ---------------------------------------
>
>                 Key: LUCENE-10069
>                 URL: https://issues.apache.org/jira/browse/LUCENE-10069
>             Project: Lucene - Core
>          Issue Type: Bug
>            Reporter: Julie Tibshirani
>            Priority: Minor
>
> Performing a kNN search with very large k (where k is close to the total 
> number of live docs) can return fewer than k documents. This occurs because 
> searches aren't guaranteed to be able to visit every node in the graph. 
> Specifically, when choosing entry points for the search, we make k random 
> draws _with replacement_, and sometimes end up with fewer than k entry 
> points. These entry points may not provide a connection to all other nodes in 
> the graph.
> This is an unusual case, but I think it'd be nice if we could always return k 
> docs when they are available (or at least document the behavior?) We're 
> currently working on adding graph layers (LUCENE-10054), which will change 
> how entry points are selected. Maybe we can revisit this issue once that work 
> is further along to see if a fix is still needed.
> Here's an example test failure showing the problem. We happen to select 
> {{numDocs=101}} and {{k=100}}.
> {code:java}
> ./gradlew test --tests TestKnnVectorQuery.testRandom 
> -Dtests.seed=3B6CE0E105431E18
> {code}



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