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

["Prakhar Jain (Jira)" <ji...@apache.org>]

Prakhar Jain created SPARK-29938:
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             Summary: 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: 2.4.4, 2.3.4
            Reporter: Prakhar Jain


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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