Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/15300#discussion_r81649882
--- Diff:
sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
---
@@ -198,6 +195,30 @@ case class InsertIntoHiveTable(
}
}
+ table.catalogTable.bucketSpec match {
+ case Some(bucketSpec) =>
+ // We can not populate bucketing information for Hive tables as
Spark SQL has a different
+ // implementation of hash function from Hive.
+ // Hive native hashing will be supported after SPARK-17495. Until
then, writes to bucketed
+ // tables are allowed only if user does not care about maintaining
table's bucketing
+ // ie. both "hive.enforce.bucketing" and "hive.enforce.sorting"
are set to false
+
+ val enforceBucketingConfig = "hive.enforce.bucketing"
+ val enforceSortingConfig = "hive.enforce.sorting"
+
+ val message = s"Output Hive table ${table.catalogTable.identifier}
is bucketed but Spark" +
+ "currently does NOT populate bucketed output which is compatible
with Hive."
+
+ if (hadoopConf.get(enforceBucketingConfig, "false").toBoolean ||
+ hadoopConf.get(enforceSortingConfig, "false").toBoolean) {
--- End diff --
@viirya : Even right now on trunk if you try to insert data into a bucketed
table, it will just work w/o producing bucketed output. I don't want to break
that for existing users by making these true. The eventual goal would be to not
have these configs and Spark should *always* produce data adhering to the
tables' bucketing spec (without breaking existing pipelines).
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