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Posted to issues@spark.apache.org by "Enrico Minack (Jira)" <ji...@apache.org> on 2022/10/17 14:10:00 UTC
[jira] [Updated] (SPARK-38591) Add flatMapSortedGroups and cogroupSorted to KeyValueGroupedDataset
[ https://issues.apache.org/jira/browse/SPARK-38591?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Enrico Minack updated SPARK-38591:
----------------------------------
Description:
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.
was:
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 methods {{KeyValueGroupedDataset.flatMapSortedGroups}} and {{KeyValueGroupedDataset.cogroupSorted}} could allow to define order within the groups.
> Add flatMapSortedGroups and cogroupSorted 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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