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Can one of the admins verify this patch?
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Github user ctslater commented on the issue:
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@cloud-fan, could you point us at the papers that describe rewriting range
join into equi-join? That would be a very convenient solution, but I've been
digging through the literature and haven't
Github user zecevicp commented on the issue:
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Sorry, I don't quite understand your question. This already applies (only)
to equijoins if there are additional range conditions on secondary columns. So
if Spark rewrites those range conditions
Github user cloud-fan commented on the issue:
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What's the advantage of this feature when Spark can rewrite range join to
equal join logically?
BTW I also hesitate to merge such a big patch to the SQL engine since we
are close to code
Github user zecevicp commented on the issue:
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I implemented spill over to disk, I added tests for spilling over and I
tested it on live data. Everything works well (actually it was easier than I
thought at first). I also removed the
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Github user zecevicp commented on the issue:
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That is a restriction if you regard this as an implementation of a range
join, which it is not. This is an *optimization of an equi join*. The intention
never was for it to work as a range join.
Github user cloud-fan commented on the issue:
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> Regarding the second point, this is not an ordinary range join, but an
equi-join with a secondary range condition.
"an equi-join with a secondary range condition" is a restriction isn't
Github user zecevicp commented on the issue:
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Implementing spilling over seems a lot of work because this is a queue. If
data is spilled over to disk and you need to pop from the queue, it is not
clear to me what is the best way to do that.
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I think range join is a very important feature, but the current
implementation is not robust enough(no spilling to disk). Have you explored
other simpler solutions? I vaguely remember that some
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test this please
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Can one of the admins verify this patch?
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There's no design doc. I didn't feel the change was big enough to warrant
one.
1. Currently there is no spill-over to disk. If the range is too big, users
can switch this off and use the
Github user cloud-fan commented on the issue:
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do we have a design doc? I have a couple of high-level questions:
1. what if the range is big and the queue OOM?
2. can't we apply it with cartesian join if there is no equal condition?
Github user zecevicp commented on the issue:
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@davies pointed out that `InMemoryUnsafeRowQueue` was throwing an exception
when `numRowsInMemoryBufferThreshold` was reached. That is now fixed and only a
warning is logged.
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@tedyu @gatorsmile Can you please trigger the build again? It failed for
some unrelated reason. Thanks
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Retest this please
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