[jira] [Assigned] (SPARK-29938) Add batching in alter table add partition flow

["Sean R. Owen (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-29938?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean R. Owen reassigned SPARK-29938:
------------------------------------

    Assignee: Prakhar Jain

> Add batching in alter table add partition flow
> ----------------------------------------------
>
>                 Key: SPARK-29938
>                 URL: https://issues.apache.org/jira/browse/SPARK-29938
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.3.4, 2.4.4
>            Reporter: Prakhar Jain
>            Assignee: Prakhar Jain
>            Priority: Major
>
> When lot of new partitions are added by an Insert query on a partitioned datasource table, sometimes the query fails with -
> {noformat}
> An error was encountered: org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: org.apache.thrift.transport.TTransportException: java.net.SocketTimeoutException: Read timed out; at
> org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:106) at
> org.apache.spark.sql.hive.HiveExternalCatalog.createPartitions(HiveExternalCatalog.scala:928) at
> org.apache.spark.sql.catalyst.catalog.SessionCatalog.createPartitions(SessionCatalog.scala:798) at
> org.apache.spark.sql.execution.command.AlterTableAddPartitionCommand.run(ddl.scala:448) at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.refreshUpdatedPartitions$1(InsertIntoHadoopFsRelationCommand.scala:137)
> {noformat}
> This happens because adding thousands of partition in a single call takes lot of time and the client eventually timesout.
> Also adding lot of partitions can lead to OOM in Hive Metastore (similar issue in [recover partition flow|https://github.com/apache/spark/pull/14607] fixed).
> Steps to reproduce -
> {noformat}
> case class Partition(data: Int, partition_key: Int)
> val df = sc.parallelize(1 to 15000, 15000).map(x => Partition(x,x)).toDF
> df.registerTempTable("temp_table")
> spark.sql("""CREATE TABLE `test_table` (`data` INT, `partition_key` INT) USING parquet PARTITIONED BY (partition_key) """)
> spark.sql("INSERT OVERWRITE TABLE test_table select * from temp_table").collect()
> {noformat}



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