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https://issues.apache.org/jira/browse/CALCITE-2979?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16839542#comment-16839542
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Stamatis Zampetakis commented on CALCITE-2979:
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Thanks for the analysis [~rubenql]!
I haven't figured out all the details of what is the best way to do it and I
guess there is not only one choice. It would be nice if [~khawlamhb], who is
working on it right now, outlines some possible alternatives with
advantages/disadvantages. Just a quick thought (that I guess could work) would
be to generate a plan like the following:
{noformat}
Filter(A.id > B.id)
Correlate(blockSize=3)
Scan(A)
Filter(OR(>(cor0_0,B.id), >(cor0_1,B.id), >(cor0_2,B.id))
Scan(B)
{noformat}
so the implementation of correlate basically does a cartesian product and the
filter on top eliminates the tuples that shouldn't be there.
> Add a block-based nested loop join algorithm
> --------------------------------------------
>
> Key: CALCITE-2979
> URL: https://issues.apache.org/jira/browse/CALCITE-2979
> Project: Calcite
> Issue Type: Improvement
> Components: core
> Affects Versions: 1.19.0
> Reporter: Stamatis Zampetakis
> Assignee: Khawla Mouhoubi
> Priority: Major
> Labels: performance
>
> Currently, Calcite provides a tuple-based nested loop join algorithm
> implemented through EnumerableCorrelate and EnumerableDefaults.correlateJoin.
> This means that for each tuple of the outer relation we probe (set variables)
> in the inner relation.
> The goal of this issue is to add new algorithm (or extend the correlateJoin
> method) which first gathers blocks (batches) of tuples from the outer
> relation and then probes the inner relation once per block.
> There are cases (eg., indexes) where the inner relation can be accessed by
> more than one value which can greatly improve the performance in particular
> when the outer relation is big.
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