Comments (6)
Joe29
commented on July 22, 2024
6
+1
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phaniharish
commented on July 22, 2024
4
+1
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sumit-saurabh
commented on July 22, 2024
Job aborted due to stage failure: Total size of serialized results of 3385 tasks (1024.1 MB) is bigger than spark.driver.maxResultSize (1024.0 MB). Don't know how to resolve this issue. please help!
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sumit-saurabh
commented on July 22, 2024
I also tried this solution but got the same issue. https://stackoverflow.com/a/31058669/3957916
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sumit-saurabh
commented on July 22, 2024
I am converting CSV data on s3 in parquet format using AWS glue ETL job. Snappy compressed parquet data is stored back to s3.
Complete Architecture: As data is uploaded to s3, a lambda function triggers glue ETL job if it's not already running. A job continuously uploads glue input data on s3. Glue successfully processes 100GB data but as input data piles up to 0.5 to 1TB, Glue job throws an error after running for a long time, say 10 hours.
Traceback (most recent call last):
File "script_2018-01-08-23-01-55.py", line 60, in <module>
partitioned_dataframe.write.partitionBy(['part_date']).format("parquet").save(output_lg_partitioned_dir, mode="append")
File "/mnt/yarn/usercache/root/appcache/application_1515414270379_0004/container_1515414270379_0004_02_000001/pyspark.zip/pyspark/sql/readwriter.py", line 550, in save
File "/mnt/yarn/usercache/root/appcache/application_1515414270379_0004/container_1515414270379_0004_02_000001/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
File "/mnt/yarn/usercache/root/appcache/application_1515414270379_0004/container_1515414270379_0004_02_000001/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
File "/mnt/yarn/usercache/root/appcache/application_1515414270379_0004/container_1515414270379_0004_02_000001/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o193.save.
: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:147)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:121)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:121)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:121)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:101)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:87)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:87)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:492)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:215)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:198)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Total size of serialized results of 3228 tasks (1024.0 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1951)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:127)
... 30 more
End of LogType:stdout
I worked a lot to resolve this error but got no clue. Though I tried some suggested approach like -
setting SparkConf: conf.set("spark.driver.maxResultSize", "3g")
The above setting didn't work. I would appreciate it if you could provide any guidance to resolve this issue.
https://stackoverflow.com/questions/48164955/aws-glue-is-throwing-error-while-processing-data-in-tbs
https://stackoverflow.com/questions/47467349/aws-glue-job-is-failing-for-large-input-csv-data-on-s3
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mohitsax
commented on July 22, 2024
The stack trace with the exception "Total size of serialized results of 3385 tasks (1024.1 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)" indicates that the Spark driver is running OOM. This may happen in a variety of scenarios, such as: (1) Your job collect rdd at driver or broadcast large variables to executors, (2) you have a large number of input files (~10s of thousands) resulting in a large state on driver for keeping track of tasks processing each of those input files.
In your case, it seems that the job is processing 3385 or fewer CSV files, which should not ideally OOM out the driver. However, you may have another a prior stage in your job that may have resulted in a large number of tasks or resulted in large memory footprint for the driver.
For scenario 1, avoid collect'ing rdds at driver or large broadcast. For scenario 2, use Grouping feature in AWS Glue to read a large number of input files and enable Job Bookmarks to avoid re-processing old input data.
More documentation on how to use Grouping feature here:
https://docs.aws.amazon.com/glue/latest/dg/grouping-input-files.html
More documentation on how to enable Job Bookmarks here:
https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html
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