[
https://issues.apache.org/jira/browse/SPARK-29938?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Prakhar Jain updated SPARK-29938:
---------------------------------
Description:
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}
was:
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}
> 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
> 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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