Hi Cheng, I think there still isn’t a PR for this, right? Do you need any help? I am very interested in this feature getting into master, so I am happy to help or even move this feature forward based on the PR you opened, but I don’t want to “steal” the feature from you if you’re interested in pushing it forward yourself 😊 Let me know if you need an extra pair of hands!
Thanks, Ximo. De: Cheng Su <chen...@fb.com> Enviado el: miércoles, 9 de septiembre de 2020 8:57 Para: XIMO GUANTER GONZALBEZ <joaquin.guantergonzal...@telefonica.com>; Reynold Xin <r...@databricks.com> CC: Spark Dev List <dev@spark.apache.org> Asunto: Re: Avoiding unnnecessary sort in FileFormatWriter/DynamicPartitionDataWriter 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.guantergonzal...@telefonica.com<mailto:joaquin.guantergonzal...@telefonica.com>> Date: Sunday, September 6, 2020 at 10:55 PM To: Cheng Su <chen...@fb.com<mailto:chen...@fb.com>>, Reynold Xin <r...@databricks.com<mailto:r...@databricks.com>> Cc: Spark Dev List <dev@spark.apache.org<mailto: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 <chen...@fb.com<mailto:chen...@fb.com>> Enviado el: viernes, 4 de septiembre de 2020 20:38 Para: Reynold Xin <r...@databricks.com<mailto:r...@databricks.com>>; XIMO GUANTER GONZALBEZ <joaquin.guantergonzal...@telefonica.com<mailto:joaquin.guantergonzal...@telefonica.com>> CC: Spark Dev List <dev@spark.apache.org<mailto: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 <r...@databricks.com<mailto:r...@databricks.com>> Date: Friday, September 4, 2020 at 10:33 AM To: XIMO GUANTER GONZALBEZ <joaquin.guantergonzal...@telefonica.com<mailto:joaquin.guantergonzal...@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.guantergonzal...@telefonica.com<mailto:joaquin.guantergonzal...@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. 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