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Posted to issues@iceberg.apache.org by GitBox <gi...@apache.org> on 2022/02/25 10:38:42 UTC
[GitHub] [iceberg] Ranjith-AR commented on issue #1894: Spark writes the Iceberg dual partition table to report an error

Ranjith-AR commented on issue #1894:
URL: https://github.com/apache/iceberg/issues/1894#issuecomment-1050738332


   I tried replicating the dual partition example provided in the docs
   https://iceberg.apache.org/docs/latest/spark-writes/
   
   ![image](https://user-images.githubusercontent.com/98068359/155699118-af66b0e6-67cd-43d0-9588-ad6610849873.png)
   
   Created the below table:
   -------
   "CREATE TABLE nyc.green_taxis_by_days_vendorid (
       VendorID string,
       lpep_pickup_datetime timestamp,
       lpep_dropoff_datetime string,
       store_and_fwd_flag string,RatecodeID string,PULocationID string,DOLocationID string,
       passenger_count string,trip_distance string,fare_amount string,extra string,mta_tax string,
       tip_amount string,tolls_amount string,ehail_fee string,improvement_surcharge string,
       total_amount string,
       payment_type string,trip_type string,
       congestion_surcharge string)
   USING iceberg
   PARTITIONED BY (
       days(lpep_pickup_datetime),VendorID
   )"
   -----
   Trying to write into the table via the dataframe api.
   
   df_green_stage1.sortWithinPartitions(("lpep_pickup_datetime"),"VendorID") \
       .writeTo("nyc.green_taxis_by_days_vendorid") \
       .append()
   
   ------
   However, I run into the below error
   The partitions are defined as days(timestamp) and secondary as vendor id.
   since the dataframe is sorted by "timestamp, vendor id" and not as "days(timestamp), vendor id" as defined in the iceberg table , we get the file already closed error.
   
   Is this a bug ? any work arounds (other than adding an additional date column and using that in the sort) ?
   
   --------
   An error was encountered:
   An error occurred while calling o294.append.
   : org.apache.spark.SparkException: Writing job aborted.
   	at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:388)
   	at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2$(WriteToDataSourceV2Exec.scala:336)
   	at org.apache.spark.sql.execution.datasources.v2.AppendDataExec.writeWithV2(WriteToDataSourceV2Exec.scala:218)
   	at org.apache.spark.sql.execution.datasources.v2.AppendDataExec.run(WriteToDataSourceV2Exec.scala:225)
   	at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:40)
   	at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:40)
   	at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.doExecute(V2CommandExec.scala:55)
   	at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:194)
   	at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
   	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
   	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
   	at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:190)
   	at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:134)
   	at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:133)
   	at org.apache.spark.sql.DataFrameWriterV2.$anonfun$runCommand$1(DataFrameWriterV2.scala:196)
   	at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107)
   	at org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:232)
   	at org.apache.spark.sql.execution.SQLExecution$.executeQuery$1(SQLExecution.scala:110)
   	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:135)
   	at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107)
   	at org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:232)
   	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:135)
   	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:253)
   	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:134)
   	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
   	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:68)
   	at org.apache.spark.sql.DataFrameWriterV2.runCommand(DataFrameWriterV2.scala:196)
   	at org.apache.spark.sql.DataFrameWriterV2.append(DataFrameWriterV2.scala:149)
   	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:750)
   Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 32.0 failed 4 times, most recent failure: Lost task 0.3 in stage 32.0 (TID 103) (ip-172-31-21-243.ec2.internal executor 18): java.lang.IllegalStateException: Incoming records violate the writer assumption that records are clustered by spec and by partition within each spec. Either cluster the incoming records or switch to fanout writers.
   Encountered records that belong to already closed files:
   partition 'lpep_pickup_datetime_day=2020-04-01/VendorID=2' in spec [
     1000: lpep_pickup_datetime_day: day(2)
     1001: VendorID: identity(1)
   ]
   	at org.apache.iceberg.io.ClusteredWriter.write(ClusteredWriter.java:95)
   	at org.apache.iceberg.io.ClusteredDataWriter.write(ClusteredDataWriter.java:34)
   	at org.apache.iceberg.spark.source.SparkWrite$PartitionedDataWriter.write(SparkWrite.java:629)
   	at org.apache.iceberg.spark.source.SparkWrite$PartitionedDataWriter.write(SparkWrite.java:604)
   	at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.$anonfun$run$1(WriteToDataSourceV2Exec.scala:416)
   	at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1473)
   	at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:452)
   	at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.$anonfun$writeWithV2$2(WriteToDataSourceV2Exec.scala:360)
   	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
   	at org.apache.spark.scheduler.Task.run(Task.scala:131)
   	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
   	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
   	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
   	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
   	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
   	at java.lang.Thread.run(Thread.java:750)
   
   Driver stacktrace:
   	at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2470)
   	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2419)
   	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2418)
   	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
   	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
   	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
   	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2418)
   	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1125)
   	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1125)
   	at scala.Option.foreach(Option.scala:407)
   	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1125)
   	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2684)
   	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2626)
   	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2615)
   	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
   	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:914)
   	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2241)
   	at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:357)
   	... 38 more
   Caused by: java.lang.IllegalStateException: Incoming records violate the writer assumption that records are clustered by spec and by partition within each spec. Either cluster the incoming records or switch to fanout writers.
   Encountered records that belong to already closed files:
   partition 'lpep_pickup_datetime_day=2020-04-01/VendorID=2' in spec [
     1000: lpep_pickup_datetime_day: day(2)
     1001: VendorID: identity(1)
   ]
   	at org.apache.iceberg.io.ClusteredWriter.write(ClusteredWriter.java:95)
   	at org.apache.iceberg.io.ClusteredDataWriter.write(ClusteredDataWriter.java:34)
   	at org.apache.iceberg.spark.source.SparkWrite$PartitionedDataWriter.write(SparkWrite.java:629)
   	at org.apache.iceberg.spark.source.SparkWrite$PartitionedDataWriter.write(SparkWrite.java:604)
   	at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.$anonfun$run$1(WriteToDataSourceV2Exec.scala:416)
   	at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1473)
   	at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:452)
   	at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.$anonfun$writeWithV2$2(WriteToDataSourceV2Exec.scala:360)
   	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
   	at org.apache.spark.scheduler.Task.run(Task.scala:131)
   	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
   	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
   	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
   	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
   	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
   	... 1 more
   
   Traceback (most recent call last):
     File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/readwriter.py", line 1564, in append
       self._jwriter.append()
     File "/usr/lib/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
       answer, self.gateway_client, self.target_id, self.name)
     File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 111, in deco
       return f(*a, **kw)
     File "/usr/lib/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
       format(target_id, ".", name), value)
   py4j.protocol.Py4JJavaError: An error occurred while calling o294.append.
   : org.apache.spark.SparkException: Writing job aborted.
   	at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:388)
   	at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2$(WriteToDataSourceV2Exec.scala:336)
   	at org.apache.spark.sql.execution.datasources.v2.AppendDataExec.writeWithV2(WriteToDataSourceV2Exec.scala:218)
   	at org.apache.spark.sql.execution.datasources.v2.AppendDataExec.run(WriteToDataSourceV2Exec.scala:225)
   	at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:40)
   	at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:40)
   	at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.doExecute(V2CommandExec.scala:55)
   	at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:194)
   	at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
   	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
   	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
   	at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:190)
   	at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:134)
   	at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:133)
   	at org.apache.spark.sql.DataFrameWriterV2.$anonfun$runCommand$1(DataFrameWriterV2.scala:196)
   	at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107)
   	at org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:232)
   	at org.apache.spark.sql.execution.SQLExecution$.executeQuery$1(SQLExecution.scala:110)
   	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:135)
   	at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107)
   	at org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:232)
   	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:135)
   	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:253)
   	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:134)
   	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
   	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:68)
   	at org.apache.spark.sql.DataFrameWriterV2.runCommand(DataFrameWriterV2.scala:196)
   	at org.apache.spark.sql.DataFrameWriterV2.append(DataFrameWriterV2.scala:149)
   	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:750)
   Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 32.0 failed 4 times, most recent failure: Lost task 0.3 in stage 32.0 (TID 103) (ip-172-31-21-243.ec2.internal executor 18): java.lang.IllegalStateException: Incoming records violate the writer assumption that records are clustered by spec and by partition within each spec. Either cluster the incoming records or switch to fanout writers.
   Encountered records that belong to already closed files:
   partition 'lpep_pickup_datetime_day=2020-04-01/VendorID=2' in spec [
     1000: lpep_pickup_datetime_day: day(2)
     1001: VendorID: identity(1)
   ]
   	at org.apache.iceberg.io.ClusteredWriter.write(ClusteredWriter.java:95)
   	at org.apache.iceberg.io.ClusteredDataWriter.write(ClusteredDataWriter.java:34)
   	at org.apache.iceberg.spark.source.SparkWrite$PartitionedDataWriter.write(SparkWrite.java:629)
   	at org.apache.iceberg.spark.source.SparkWrite$PartitionedDataWriter.write(SparkWrite.java:604)
   	at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.$anonfun$run$1(WriteToDataSourceV2Exec.scala:416)
   	at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1473)
   	at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:452)
   	at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.$anonfun$writeWithV2$2(WriteToDataSourceV2Exec.scala:360)
   	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
   	at org.apache.spark.scheduler.Task.run(Task.scala:131)
   	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
   	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
   	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
   	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
   	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
   	at java.lang.Thread.run(Thread.java:750)
   
   Driver stacktrace:
   	at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2470)
   	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2419)
   	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2418)
   	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
   	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
   	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
   	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2418)
   	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1125)
   	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1125)
   	at scala.Option.foreach(Option.scala:407)
   	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1125)
   	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2684)
   	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2626)
   	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2615)
   	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
   	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:914)
   	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2241)
   	at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:357)
   	... 38 more
   Caused by: java.lang.IllegalStateException: Incoming records violate the writer assumption that records are clustered by spec and by partition within each spec. Either cluster the incoming records or switch to fanout writers.
   Encountered records that belong to already closed files:
   partition 'lpep_pickup_datetime_day=2020-04-01/VendorID=2' in spec [
     1000: lpep_pickup_datetime_day: day(2)
     1001: VendorID: identity(1)
   ]
   	at org.apache.iceberg.io.ClusteredWriter.write(ClusteredWriter.java:95)
   	at org.apache.iceberg.io.ClusteredDataWriter.write(ClusteredDataWriter.java:34)
   	at org.apache.iceberg.spark.source.SparkWrite$PartitionedDataWriter.write(SparkWrite.java:629)
   	at org.apache.iceberg.spark.source.SparkWrite$PartitionedDataWriter.write(SparkWrite.java:604)
   	at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.$anonfun$run$1(WriteToDataSourceV2Exec.scala:416)
   	at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1473)
   	at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:452)
   	at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.$anonfun$writeWithV2$2(WriteToDataSourceV2Exec.scala:360)
   	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
   	at org.apache.spark.scheduler.Task.run(Task.scala:131)
   	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
   	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
   	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
   	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
   	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
   	... 1 more
   
   -------
   
   
   


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