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From Cheng Su <chen...@fb.com.INVALID>
Subject Re: Avoiding unnnecessary sort in FileFormatWriter/DynamicPartitionDataWriter
Date Wed, 09 Sep 2020 06:57:06 GMT
Thanks, Ximo. On our side, we do see the similar cases in production as well and we added this
feature internally couple years ago. Let me submit new PR (which is mostly to rebase https://github.com/apache/spark/pull/23163
to latest master and try to have better code structure), if there’s no objection.

Thanks,
Cheng Su

From: XIMO GUANTER GONZALBEZ <joaquin.guantergonzalbez@telefonica.com>
Date: Sunday, September 6, 2020 at 10:55 PM
To: Cheng Su <chengsu@fb.com>, Reynold Xin <rxin@databricks.com>
Cc: Spark Dev List <dev@spark.apache.org>
Subject: RE: Avoiding unnnecessary sort in FileFormatWriter/DynamicPartitionDataWriter

> 1.        If number of writers exceeds a pre-defined threshold (controlled by a config),
we sort rest of input rows, and fallback to current mode for write.
> The config can be disabled by default to be consistent with current behavior, and users
can choose to opt-in to non-sort mode if they are benefitted with not sorting on large amount
of data.

With both of those points in place, I think the plan is super reasonable since it wouldn’t
affect anyone who isn’t actively tuning Spark, and enables those of us who are hitting this
sort to have the tools to improve performance in our scenario.

Cheers,
Ximo.

De: Cheng Su <chengsu@fb.com>
Enviado el: viernes, 4 de septiembre de 2020 20:38
Para: Reynold Xin <rxin@databricks.com>; XIMO GUANTER GONZALBEZ <joaquin.guantergonzalbez@telefonica.com>
CC: Spark Dev List <dev@spark.apache.org>
Asunto: Re: Avoiding unnnecessary sort in FileFormatWriter/DynamicPartitionDataWriter

Hi,

Just for context - I created the JIRA for this around 2 years ago (https://issues.apache.org/jira/browse/SPARK-26164<https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_SPARK-2D26164&d=DwMGaQ&c=5VD0RTtNlTh3ycd41b3MUw&r=-rGDw9b4dZIpgTn-Pa8RTw&m=j6D6nZ8BfwD7T12P4vv6q99RDiJgYha2RKgbn2xEYuM&s=y_1d0LwMdTnnze-EUi5IL5jSm-tFQvSYToFxyV3CvEc&e=>
and a stale PR not merged - https://github.com/apache/spark/pull/23163), and I recently discussed
with Wenchen again, it looks like it might be reasonable to:


  1.  Open multiple writers in parallel to write partitions/buckets.
  2.  If number of writers exceeds a pre-defined threshold (controlled by a config), we sort
rest of input rows, and fallback to current mode for write.

The approach uses number of writers to be proxy for memory usage here, I agree this is quite
rudimentary. But given memory usage from writers is non-visible to spark now, it seems to
me that there’s no other good way to model the memory usage for write. Internally we did
the thing in same way, but our internal ORC is customized to better work with internal Spark
for memory usage so we don’t see much issue for OOM (non-vectorization code path).

The config can be disabled by default to be consistent with current behavior, and users can
choose to opt-in to non-sort mode if they are benefitted with not sorting on large amount
of data.

Does it sound good as a plan? Would like to get more opinion on this. Thanks.

Cheng Su

From: Reynold Xin <rxin@databricks.com<mailto:rxin@databricks.com>>
Date: Friday, September 4, 2020 at 10:33 AM
To: XIMO GUANTER GONZALBEZ <joaquin.guantergonzalbez@telefonica.com<mailto:joaquin.guantergonzalbez@telefonica.com>>
Cc: Spark Dev List <dev@spark.apache.org<mailto:dev@spark.apache.org>>
Subject: Re: Avoiding unnnecessary sort in FileFormatWriter/DynamicPartitionDataWriter


The issue is memory overhead. Writing files create a lot of buffer (especially in columnar
formats like Parquet/ORC). Even a few file handlers and buffers per task can OOM the entire
process easily.


On Fri, Sep 04, 2020 at 5:51 AM, XIMO GUANTER GONZALBEZ <joaquin.guantergonzalbez@telefonica.com<mailto:joaquin.guantergonzalbez@telefonica.com>>
wrote:
Hello,

I have observed that if a DataFrame is saved with partitioning columns in Parquet, then a
sort is performed in FileFormatWriter (see https://github.com/apache/spark/blob/v3.0.0/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormatWriter.scala#L152)
because DynamicPartitionDataWriter only supports having a single file open at a time (see
https://github.com/apache/spark/blob/v3.0.0/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormatDataWriter.scala#L170-L171).
I think it would be possible to avoid this sort (which is a major bottleneck for some of my
scenarios) if DynamicPartitionDataWriter could have multiple files open at the same time,
and writing each piece of data to its corresponding file.

Would that change be a welcome PR for the project or is there any major problem that I am
not considering that would prevent removing this sort?

Thanks,
Ximo.




Some more detail about the problem, in case I didn’t explain myself correctly: suppose we
have a dataframe which we want to partition by column A:

Column A
Column B
4
A
1
B
2
C

The current behavior will first sort the dataframe:

Column A
Column B
1
B
2
C
4
A

So that DynamicPartitionDataWriter can have a single file open, since all the data for a single
partition will be adjacent and can be iterated over sequentially. In order to process the
first row, DynamicPartitionDataWriter will open a file in /columnA=1/part-r-00000-<uuid>.parquet
and write the data. When processing the second row it will see it belongs to a different partition,
closet he first file and open a new file in /columna=2/part-r-00000-<uuid>.parquet and
so on.

My proposed change would involve changing DynamicPartitionDataWriter to have as many open
files as partitions, and close them all once all data has been processed.

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y/o copia sin autorización puede estar prohibida en virtud de la legislación vigente. Si
ha recibido este mensaje por error, le rogamos que nos lo comunique inmediatamente por esta
misma vía y proceda a su destrucción.

The information contained in this transmission is privileged and confidential information
intended only for the use of the individual or entity named above. If the reader of this message
is not the intended recipient, you are hereby notified that any dissemination, distribution
or copying of this communication is strictly prohibited. If you have received this transmission
in error, do not read it. Please immediately reply to the sender that you have received this
communication in error and then delete it.

Esta mensagem e seus anexos se dirigem exclusivamente ao seu destinatário, pode conter informação
privilegiada ou confidencial e é para uso exclusivo da pessoa ou entidade de destino. Se
não é vossa senhoria o destinatário indicado, fica notificado de que a leitura, utilização,
divulgação e/ou cópia sem autorização pode estar proibida em virtude da legislação
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