循环通过大型DynamicFrame输出到S3以解决'maxResultSize'错误

时间：2018-06-15 11:12:58

标签： amazon-web-services pyspark etl aws-glue

我在AWS Glue ETL作业中有一个大型DynamicFrame。当尝试将此数据输出到S3时，由于任务太大，它会失败。

错误：

  

引起：org.apache.spark.SparkException：作业因阶段失败而中止：3225个任务的序列化结果总大小（1024.0 MB）大于spark.driver.maxResultSize（1024.0 MB）

我相信一个很好的解决方案是按日期分隔我的DynamicFrame，并循环显示每个日期的数据并以较小的块输出。也许是这样的事情：

for eventDateParam in mapped_datasource0_general.eventDate:
    partitioned_dataframe_general = mapped_datasource0_general.where(eventDate = eventDateParam)
    dataoutput_general = glueContext.write_dynamic_frame.from_options(frame = partitioned_dataframe_general, connection_type = "s3", connection_options = {"path": glue_relationalize_output_s3_path_general, "partitionKeys": ["eventDate"]}, format = "parquet", transformation_ctx = "dataoutput_general")

我对AWS Glue相对较新，并且遇到了各种错误，试图找到解决方法。任何建议表示赞赏。

干杯！

= - = - = - = - = - = - = - = - = - = - = - = - = - = - = - = - = - = - =

编辑：

更长的tracestack：

Traceback (most recent call last):
File "script_2018-06-19-22-36-11.py", line 63, in <module>
glueContext.write_dynamic_frame.from_options(frame = partitioned_mapped_personal_DF, connection_type = "s3", connection_options = {"path": glue_relationalize_output_s3_path_personal, "partitionKeys": ["eventDate"]}, format = "parquet", transformation_ctx = "dataoutput_personal")
File "/mnt/yarn/usercache/root/appcache/application_1529446917701_0002/container_1529446917701_0002_01_000001/PyGlue.zip/awsglue/dynamicframe.py", line 572, in from_options
File "/mnt/yarn/usercache/root/appcache/application_1529446917701_0002/container_1529446917701_0002_01_000001/PyGlue.zip/awsglue/context.py", line 191, in write_dynamic_frame_from_options
File "/mnt/yarn/usercache/root/appcache/application_1529446917701_0002/container_1529446917701_0002_01_000001/PyGlue.zip/awsglue/context.py", line 214, in write_from_options
File "/mnt/yarn/usercache/root/appcache/application_1529446917701_0002/container_1529446917701_0002_01_000001/PyGlue.zip/awsglue/data_sink.py", line 32, in write
File "/mnt/yarn/usercache/root/appcache/application_1529446917701_0002/container_1529446917701_0002_01_000001/PyGlue.zip/awsglue/data_sink.py", line 28, in writeFrame
File "/mnt/yarn/usercache/root/appcache/application_1529446917701_0002/container_1529446917701_0002_01_000001/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
File "/mnt/yarn/usercache/root/appcache/application_1529446917701_0002/container_1529446917701_0002_01_000001/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
File "/mnt/yarn/usercache/root/appcache/application_1529446917701_0002/container_1529446917701_0002_01_000001/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o1198.pyWriteDynamicFrame.
: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:213)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:166)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:166)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:166)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:145)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.execution.datasources.DataSource.writeInFileFormat(DataSource.scala:435)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:471)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:50)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$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$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:609)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:233)
at com.amazonaws.services.glue.SparkSQLDataSink.writeDynamicFrame(DataSink.scala:123)
at com.amazonaws.services.glue.DataSink.pyWriteDynamicFrame(DataSink.scala:38)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Total size of serialized results of 3109 tasks (1024.3 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1517)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1505)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1504)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1504)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1732)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1676)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:186)
... 45 more

以及相关的代码行：

listOfDistinctsPersonal = mapped_personal.select("eventDate").distinct()

#LOOP WRITE PERSONAL
for eventDateParam in listOfDistinctsPersonal:
    partitioned_mapped_personal = mapped_personal.where(col("eventDate") == eventDateParam)
    partitioned_mapped_personal_DF = DynamicFrame.fromDF(partitioned_mapped_personal, glueContext, "partitioned_mapped_personal_DF")
    glueContext.write_dynamic_frame.from_options(frame = partitioned_mapped_personal_DF, connection_type = "s3", connection_options = {"path": glue_relationalize_output_s3_path_personal, "partitionKeys": ["eventDate"]}, format = "parquet", transformation_ctx = "dataoutput_personal")

1 个答案:

答案 0 :(得分：0)

您说要将数据输出到S3。如果您没有进行大量聚合（groupBy或加入），DataFrame / DynamicFrame的大小无关紧要 - 移动数据只需要更长时间。

您收到的错误表示您正在尝试将DataFrame流式传输到驱动程序：

spark.driver.maxResultSize是一个配置控制现在可以从执行程序将大量数据流回驱动程序。这通常在您致电DataFrame.collect或DataFrame.collectAsList时发生。查看Spark docs了解更多详情。

你说你得到了各种各样的错误 - 也许你的工作在其他地方引发了错误？你能分享一下所有胶水代码吗？也许还有错误的堆栈跟踪？

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