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https://issues.apache.org/jira/browse/SPARK-52160?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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John Smith updated SPARK-52160:
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Description:
We have a hive partitioned dataset, partitioned on year, month, day. When doing
a broadcast hash join with that table, with the partition keys as the join
keys, dynamic partition pruning is not being used. It's reading all partitions.
The query would be a lot faster with DPP.
When filtering on year, month, and day (no join), Spark does read only the
partitions that match the filter.
Code:
https://community.palantir.com/t/dynamic-partition-pruning-of-hive-partitioned-datasets-with-a-broadcast-join/3545
was:
We have a hive partitioned dataset, partitioned on year, month, day. When doing
a broadcast hash join with that table, with the partition keys as the join
keys, dynamic partition pruning is not being used. It's reading all partitions.
The query would be a lot faster with DPP.
When filtering on year, month, and day (no join), Spark does read only the
partitions that match the filter.
> Dynamic pruning not being used with broadcast hash join
> -------------------------------------------------------
>
> Key: SPARK-52160
> URL: https://issues.apache.org/jira/browse/SPARK-52160
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 3.5.5
> Reporter: John Smith
> Priority: Major
>
> We have a hive partitioned dataset, partitioned on year, month, day. When
> doing a broadcast hash join with that table, with the partition keys as the
> join keys, dynamic partition pruning is not being used. It's reading all
> partitions. The query would be a lot faster with DPP.
>
> When filtering on year, month, and day (no join), Spark does read only the
> partitions that match the filter.
>
> Code:
> https://community.palantir.com/t/dynamic-partition-pruning-of-hive-partitioned-datasets-with-a-broadcast-join/3545
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