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Exception During Insert

See original GitHub issue

Setup org.apache.hudi:hudi-spark-bundle_2.11:0.5.3,org.apache.spark:spark-avro_2.11:2.4.4 Client PySpark Storage S3: hudi_options = { | ‘hoodie.table.name’: self.table_name, | ‘hoodie.datasource.write.recordkey.field’: ‘column’, | ‘hoodie.datasource.write.table.name’: self.table_name, | ‘hoodie.datasource.write.precombine.field’: ‘column’, | ‘hoodie.datasource.write.partitionpath.field’: ‘dl_snapshot_date’, | ‘hoodie.upsert.shuffle.parallelism’: 2, | ‘hoodie.insert.shuffle.parallelism’: 2 | } Data get written and able to load with spark. But write produce exception

20/07/02 21:53:36 ERROR PriorityBasedFileSystemView: Got error running preferred function. Trying secondary
org.apache.hudi.exception.HoodieRemoteException: xx.xx.xx.xx:xxxx failed to respond
	at org.apache.hudi.common.table.view.RemoteHoodieTableFileSystemView.getPendingCompactionOperations(RemoteHoodieTableFileSystemView.java:376)
	at org.apache.hudi.common.table.view.PriorityBasedFileSystemView.execute(PriorityBasedFileSystemView.java:66)
	at org.apache.hudi.common.table.view.PriorityBasedFileSystemView.getPendingCompactionOperations(PriorityBasedFileSystemView.java:199)
	at org.apache.hudi.table.CleanHelper.<init>(CleanHelper.java:78)
	at org.apache.hudi.table.HoodieCopyOnWriteTable.scheduleClean(HoodieCopyOnWriteTable.java:288)
	at org.apache.hudi.client.HoodieCleanClient.scheduleClean(HoodieCleanClient.java:118)
	at org.apache.hudi.client.HoodieCleanClient.clean(HoodieCleanClient.java:95)
	at org.apache.hudi.client.HoodieWriteClient.clean(HoodieWriteClient.java:835)
	at org.apache.hudi.client.HoodieWriteClient.postCommit(HoodieWriteClient.java:512)
	at org.apache.hudi.client.AbstractHoodieWriteClient.commit(AbstractHoodieWriteClient.java:157)
	at org.apache.hudi.client.AbstractHoodieWriteClient.commit(AbstractHoodieWriteClient.java:101)
	at org.apache.hudi.client.AbstractHoodieWriteClient.commit(AbstractHoodieWriteClient.java:92)
	at org.apache.hudi.HoodieSparkSqlWriter$.checkWriteStatus(HoodieSparkSqlWriter.scala:268)
	at org.apache.hudi.HoodieSparkSqlWriter$.write(HoodieSparkSqlWriter.scala:188)
	at org.apache.hudi.DefaultSource.createRelation(DefaultSource.scala:108)
	at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:45)
	at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
	at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
	at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:86)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
	at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
	at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
	at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
	at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
	at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
	at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
	at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
	at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
	at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
	at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:229)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)

Issue Analytics

State:
Created 3 years ago
Comments:24 (14 by maintainers)

Top GitHub Comments

1reaction

bvaradarcommented, Jul 14, 2020

@asheeshgarg : If the table is represented as simple parquet table, presto queries will start showing duplicates when there are multiple file versions present or could fail when writes are happening (no snapshot isolation). Creating a table using hive sync would ensure only valid and single file versions are read.

0reactions

bvaradarcommented, Aug 4, 2020

Closing this issue. Please reopen if needed.