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From "LINZ, Arnaud" <AL...@bouyguestelecom.fr>
Subject RE: Left join with unbalanced dataset
Date Mon, 01 Feb 2016 08:40:29 GMT
Thanks, I can’t believe I missed the outer join operators… Will try them and will keep
you informed.
I use the “official” 0.10 release from the maven repo. The off-heap memory I use is the
one HDFS I/O uses (codec, DFSOutputstream threads…), but I don’t have many open files
at once, and doubling the amount of memory did not solve the problem.

De : ewenstephan@gmail.com [mailto:ewenstephan@gmail.com] De la part de Stephan Ewen
Envoyé : dimanche 31 janvier 2016 20:57
À : user@flink.apache.org
Objet : Re: Left join with unbalanced dataset


YARN killing the application seems strange. The memory use that YARN sees should not change
even when one node gets a lot or data.

Can you share what version of Flink (plus commit hash) you are using and whether you use off-heap
memory or not?


On Sun, Jan 31, 2016 at 10:47 AM, Till Rohrmann <trohrmann@apache.org<mailto:trohrmann@apache.org>>
Hi Arnaud,

the unmatched elements of A will only end up on the same worker node if they all share the
same key. Otherwise, they will be evenly spread out across your cluster. However, I would
also recommend you to use Flink's leftOuterJoin.


On Sun, Jan 31, 2016 at 5:27 AM, Chiwan Park <chiwanpark@apache.org<mailto:chiwanpark@apache.org>>
Hi Arnaud,

To join two datasets, the community recommends using join operation rather than cogroup operation.
For left join, you can use leftOuterJoin method. Flink’s optimizer decides distributed join
execution strategy using some statistics of the datasets such as size of the dataset. Additionally,
you can set join hint to help optimizer decide the strategy.

In transformations section [1] of Flink documentation, you can find about outer join operation
in detail.

I hope this helps.

[1]: https://ci.apache.org/projects/flink/flink-docs-release-0.10/apis/programming_guide.html#transformations

Chiwan Park

> On Jan 30, 2016, at 6:43 PM, LINZ, Arnaud <ALINZ@bouyguestelecom.fr<mailto:ALINZ@bouyguestelecom.fr>>
> Hello,
> I have a very big dataset A to left join with a dataset B that is half its size. That
is to say, half of A records will be matched with one record of B, and the other half with
null values.
> I used a CoGroup for that, but my batch fails because yarn kills the container due to
memory problems.
> I guess that’s because one worker will get half of A dataset (the unmatched ones),
and that’s too much for a single JVM
> Am I right in my diagnostic ? Is there a better way to left join unbalanced datasets
> Best regards,
> Arnaud
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