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From Stephan Ewen <se...@apache.org>
Subject Re: Hash join failing
Date Tue, 26 May 2015 20:26:25 GMT
If you have this case, giving more memory is fighting a symptom, rather
than a cause.

If you really have that many duplicates in the data set (and you have not
just a bad implementation of "hashCode()"), then try the following:

1) Reverse hash join sides. Duplicates hurt only on the build-side, not on
the probe side. This works if the other input has much fewer duplicate
keys. You can do this with a JoinHint.

2) Switch to a sort-merge join. This will be slow with very many duplicate
keys, but should not break.

Let me know how it works!

On Tue, May 26, 2015 at 10:22 PM, Sebastian <ssc@apache.org> wrote:

> Hi,
>
> What can I do to give Flink more memory when running it from my IDE? I'm
> getting the following exception:
>
> Caused by: java.lang.RuntimeException: Hash join exceeded maximum number
> of recursions, without reducing partitions enough to be memory resident.
> Probably cause: Too many duplicate keys.
>         at
> org.apache.flink.runtime.operators.hash.MutableHashTable.buildTableFromSpilledPartition(MutableHashTable.java:720)
>         at
> org.apache.flink.runtime.operators.hash.MutableHashTable.prepareNextPartition(MutableHashTable.java:508)
>         at
> org.apache.flink.runtime.operators.hash.MutableHashTable.nextRecord(MutableHashTable.java:541)
>         at
> org.apache.flink.runtime.operators.hash.NonReusingBuildFirstHashMatchIterator.callWithNextKey(NonReusingBuildFirstHashMatchIterator.java:104)
>         at
> org.apache.flink.runtime.operators.MatchDriver.run(MatchDriver.java:173)
>         at
> org.apache.flink.runtime.operators.RegularPactTask.run(RegularPactTask.java:494)
>         at
> org.apache.flink.runtime.operators.RegularPactTask.invoke(RegularPactTask.java:360)
>         at
> org.apache.flink.runtime.execution.RuntimeEnvironment.run(RuntimeEnvironment.java:223)
>         at java.lang.Thread.run(Thread.java:745)
>

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