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https://issues.apache.org/jira/browse/SPARK-17436?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15611483#comment-15611483
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Ran Haim edited comment on SPARK-17436 at 10/27/16 10:35 AM:
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Usually you partition the data, and then you order it - this way you preserve
ordering.
The problem here occurs in the writer itself, the DataFrame itself is
partitioned and ordered correctly.
I would have some time to work on it next week or something like that, can I
just do a pull request and put it here?
was (Author: ran.h...@optimalplus.com):
usually you partition the data, and then you order it - this way you preserve
ordering.
The problem here occurs in the writer itself, the DataFrame itself is
partitioned and ordered correctly.
I would have some time to work on it next week or something like that, can I
just do a pull request and put it here?
> dataframe.write sometimes does not keep sorting
> -----------------------------------------------
>
> Key: SPARK-17436
> URL: https://issues.apache.org/jira/browse/SPARK-17436
> Project: Spark
> Issue Type: Bug
> Affects Versions: 1.6.1, 1.6.2, 2.0.0
> Reporter: Ran Haim
>
> When using partition by, datawriter can sometimes mess up an ordered
> dataframe.
> The problem originates in
> org.apache.spark.sql.execution.datasources.DynamicPartitionWriterContainer.
> In the writeRows method when too many files are opened (configurable), it
> starts inserting rows to UnsafeKVExternalSorter, then it reads all the rows
> again from the sorter and writes them to the corresponding files.
> The problem is that the sorter actually sorts the rows using the partition
> key, and that can sometimes mess up the original sort (or secondary sort if
> you will).
> I think the best way to fix it is to stop using a sorter, and just put the
> rows in a map using key as partition key and value as an arraylist, and then
> just walk through all the keys and write it in the original order - this will
> probably be faster as there no need for ordering.
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