Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/17644#discussion_r116384127
--- Diff:
sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
---
@@ -307,6 +307,27 @@ case class InsertIntoHiveTable(
}
}
+ table.bucketSpec match {
+ case Some(bucketSpec) =>
+ // Writes to bucketed hive 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.identifier} is bucketed
but Spark" +
+ "currently does NOT populate bucketed output which is compatible
with Hive."
+
+ if (hadoopConf.get(enforceBucketingConfig, "true").toBoolean ||
+ hadoopConf.get(enforceSortingConfig, "true").toBoolean) {
+ throw new AnalysisException(message)
+ } else {
+ logWarning(message + s" Inserting data anyways since both
$enforceBucketingConfig and " +
+ s"$enforceSortingConfig are set to false.")
--- End diff --
- In Hive 1.x:
- If enforcing configs are set: data is populated respecting the tables'
bucketing spec
- If enforcing configs are NOT set: data is populated and not conformant
with the tables' bucketing spec
- In Hive 2.x, these configs are not there and hive would always populate
data conformant with table's bucketing.
With Spark, currently the data would be written out in a non conformant way
despite of that config being set or not. This PR will go to the model of Hive
1.x. I am working on a next PR which would make things like Hive 2.0.
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