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https://issues.apache.org/jira/browse/SPARK-38591?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Enrico Minack updated SPARK-38591:
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Summary: Add sortWithinGroups to KeyValueGroupedDataset (was: Add
flatMapSortedGroups and cogroupSorted to KeyValueGroupedDataset)
> Add sortWithinGroups to KeyValueGroupedDataset
> ----------------------------------------------
>
> Key: SPARK-38591
> URL: https://issues.apache.org/jira/browse/SPARK-38591
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 3.4.0
> Reporter: Enrico Minack
> Assignee: Apache Spark
> Priority: Major
>
> The existing methods {{KeyValueGroupedDataset.flatMapGroups}} and
> {{KeyValueGroupedDataset.cogroup}} provide an iterator of rows for each group
> key. If user code requires those rows in a particular order, that iterator
> would have to be sorted first, which is against the idea of an iterator in
> the first place. Methods {{flatMapGroups}} and {{cogroup}} have the great
> advantage that they work with groups that are too large to fit into memory of
> one executor. Sorting them in the user function breaks this property.
> [org.apache.spark.sql.KeyValueGroupedDataset|https://github.com/apache/spark/blob/47485a3c2df3201c838b939e82d5b26332e2d858/sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala#L134-L137]:
> {noformat}
> Internally, the implementation will spill to disk if any given group is too
> large to fit into
> memory. However, users must take care to avoid materializing the whole
> iterator for a group
> (for example, by calling `toList`) unless they are sure that this is possible
> given the memory
> constraints of their cluster.
> {noformat}
> The implementations of {{KeyValueGroupedDataset.flatMapGroups}} and
> {{KeyValueGroupedDataset.cogroup}} already sort each partition according to
> the group key. By additionally sorting by some data columns, the iterator can
> be guaranteed to provide some order.
> New method {{KeyValueGroupedDataset.sortWithinGroups}} (similar to
> {{{}Dataset.sortWithinPartitions){}}}would allow to define order within the
> groups.
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