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https://issues.apache.org/jira/browse/SPARK-23442?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16494806#comment-16494806
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Apache Spark commented on SPARK-23442:
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User 'wangyum' has created a pull request for this issue:
https://github.com/apache/spark/pull/21460
> Reading from partitioned and bucketed table uses only bucketSpec.numBuckets
> partitions in all cases
> ---------------------------------------------------------------------------------------------------
>
> Key: SPARK-23442
> URL: https://issues.apache.org/jira/browse/SPARK-23442
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
> Affects Versions: 2.2.1
> Reporter: Pranav Rao
> Priority: Major
>
> Through the DataFrameWriter[T] interface I have created a external HIVE table
> with 5000 (horizontal) partitions and 50 buckets in each partition. Overall
> the dataset is 600GB and the provider is Parquet.
> Now this works great when joining with a similarly bucketed dataset - it's
> able to avoid a shuffle.
> But any action on this Dataframe(from _spark.table("tablename")_), works with
> only 50 RDD partitions. This is happening because of
> [createBucketedReadRDD|https://github.com/apachttps:/github.com/apache/spark/blob/branch-2.3/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.she/spark/blob/branch-2.3/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.sc].
> So the 600GB dataset is only read through 50 tasks, which makes this
> partitioning + bucketing scheme not useful.
> I cannot expose the base directory of the parquet folder for reading the
> dataset, because the partition locations don't follow a (basePath + partSpec)
> format.
> Meanwhile, are there workarounds to use higher parallelism while reading such
> a table?
> Let me know if I can help in any way.
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