[GitHub] [arrow-datafusion] Dandandan commented on pull request #1500: Add support for PartitionBy functionality

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Dandandan commented on pull request #1500:
URL: https://github.com/apache/arrow-datafusion/pull/1500#issuecomment-1002310491


   Thanks for showing interest in this functionality!
   
   I believe this functionality shouldn't be a part of the `RepartitionExec`. The partitions (confusingly) mentioned there are a unit of parallelism and distributing data, not a way to aggregate data like in the example for writing to different partitions. The partitions in `PartitionExec` are equivalent to Spark partitions and mirror that functionality / design.
   
   We already have some pieces that we could use to implement the write support:
   * hash aggregation, i.e. `group by`. This could be used to group the same rows and append them to the output directories / files. `HashAggregateExec` has different modes, local and global. For writing, only local mode is needed, as we can write the rows to multiple files within the same directory (just like Spark does). 
   * `RepartitionExec::Hash`, allowing the parallelize the operations on more CPUs and nodes in Ballista. This is already utilized by using the hash aggregate code.
   
   The missing piece here is the support/wiring to *write* the final partitions to directories / files using a partitioning scheme.
   
   I don't have to much time ATM to write up a proposal myself, but if you see which direction I am going to, I support to write a design for this to collect feedback on it.
   
   It would be wise to look after some similar engines, like Spark/Hive/Trino/etc. for some inspiration.
   
   
   


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