[ https://issues.apache.org/jira/browse/SPARK-20331?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-20331: ------------------------------------ Assignee: Apache Spark > Broaden support for Hive partition pruning predicate pushdown > ------------------------------------------------------------- > > Key: SPARK-20331 > URL: https://issues.apache.org/jira/browse/SPARK-20331 > Project: Spark > Issue Type: Improvement > Components: SQL > Affects Versions: 2.1.0 > Reporter: Michael Allman > Assignee: Apache Spark > > Spark 2.1 introduced scalable support for Hive tables with huge numbers of > partitions. Key to leveraging this support is the ability to prune > unnecessary table partitions to answer queries. Spark supports a subset of > the class of partition pruning predicates that the Hive metastore supports. > If a user writes a query with a partition pruning predicate that is *not* > supported by Spark, Spark falls back to loading all partitions and pruning > client-side. We want to broaden Spark's current partition pruning predicate > pushdown capabilities. > One of the key missing capabilities is support for disjunctions. For example, > for a table partitioned by date, specifying with a predicate like > {code}date = 20161011 or date = 20161014{code} > will result in Spark fetching all partitions. For a table partitioned by date > and hour, querying a range of hours across dates can be quite difficult to > accomplish without fetching all partition metadata. > The current partition pruning support supports only comparisons against > literals. We can expand that to foldable expressions by evaluating them at > planning time. > We can also implement support for the "IN" comparison by expanding it to a > sequence of "OR"s. > This ticket covers those enhancements. -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org