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https://issues.apache.org/jira/browse/SPARK-22913?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-22913:
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Assignee: Apache Spark
> Hive Partition Pruning, Fractional and Timestamp types
> ------------------------------------------------------
>
> Key: SPARK-22913
> URL: https://issues.apache.org/jira/browse/SPARK-22913
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Ameen Tayyebi
> Assignee: Apache Spark
> Fix For: 2.3.0
>
>
> Spark currently pushes the predicates it has in the SQL query to Hive
> Metastore. This only applies to predicates that are placed on top of
> partitioning columns. As more and more hive metastore implementations come
> around, this is an important optimization to allow data to be prefiltered to
> only relevant partitions. Consider the following example:
> Table:
> create external table data (key string, quantity long)
> partitioned by (processing-date timestamp)
> Query:
> select * from data where processing-date = '2017-10-23 00:00:00'
> Currently, no filters will be pushed to the hive metastore for the above
> query. The reason is that the code that tries to compute predicates to be
> sent to hive metastore, only deals with integral and string column types. It
> doesn't know how to handle fractional and timestamp columns.
> I have tables in my metastore (AWS Glue) with millions of partitions of type
> timestamp and double. In my specific case, it takes Spark's master node about
> 6.5 minutes to download all partitions for the table, and then filter the
> partitions client-side. The actual processing time of my query is only 6
> seconds. In other words, without partition pruning, I'm looking at 6.5
> minutes of processing and with partition pruning, I'm looking at 6 seconds
> only.
> I have a fix for this developed locally that I'll provide shortly as a pull
> request.
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