Yes, Even I tried the same first. Then I moved to join method because shuffle spill was happening because row num without partition happens on single task. Instead of processinf entire dataframe on single task. I have broken down that into df1 and df2 and joining. Because df2 is having very less data set since it has 2 cols only.
Thanks Sachit On Wed, 7 Oct 2020, 01:04 Eve Liao, <evelia...@gmail.com> wrote: > Try to avoid broadcast. Thought this: > https://towardsdatascience.com/adding-sequential-ids-to-a-spark-dataframe-fa0df5566ff6 > could be helpful. > > On Tue, Oct 6, 2020 at 12:18 PM Sachit Murarka <connectsac...@gmail.com> > wrote: > >> Thanks Eve for response. >> >> Yes I know we can use broadcast for smaller datasets,I increased the >> threshold (4Gb) for the same then also it did not work. and the df3 is >> somewhat greater than 2gb. >> >> Trying by removing broadcast as well.. Job is running since 1 hour. Will >> let you know. >> >> >> Thanks >> Sachit >> >> On Wed, 7 Oct 2020, 00:41 Eve Liao, <evelia...@gmail.com> wrote: >> >>> How many rows does df3 have? Broadcast joins are a great way to append >>> data stored in relatively *small* single source of truth data files to >>> large DataFrames. DataFrames up to 2GB can be broadcasted so a data file >>> with tens or even hundreds of thousands of rows is a broadcast candidate. >>> Your broadcast variable is probably too large. >>> >>> On Tue, Oct 6, 2020 at 11:37 AM Sachit Murarka <connectsac...@gmail.com> >>> wrote: >>> >>>> Hello Users, >>>> >>>> I am facing an issue in spark job where I am doing row number() without >>>> partition by clause because I need to add sequential increasing IDs. >>>> But to avoid the large spill I am not doing row number() over the >>>> complete data frame. >>>> >>>> Instead I am applying monotically_increasing id on actual data set , >>>> then create a new data frame from original data frame which will have >>>> just monotically_increasing id. >>>> >>>> So DF1 = All columns + monotically_increasing_id >>>> DF2 = Monotically_increasingID >>>> >>>> Now I am applying row number() on DF2 since this is a smaller >>>> dataframe. >>>> >>>> DF3 = Monotically_increasingID + Row_Number_ID >>>> >>>> Df.join(broadcast(DF3)) >>>> >>>> This will give me sequential increment id in the original Dataframe. >>>> >>>> But below is the stack trace. >>>> >>>> py4j.protocol.Py4JJavaError: An error occurred while calling >>>> o180.parquet. >>>> : org.apache.spark.SparkException: Job aborted. >>>> at >>>> org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:198) >>>> at >>>> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159) >>>> at >>>> org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104) >>>> at >>>> org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102) >>>> at >>>> org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122) >>>> 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 >>>> org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:566) >>>> 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) >>>> Caused by: org.apache.spark.SparkException: Could not execute broadcast >>>> in 1000 secs. You can increase the timeout for broadcasts via >>>> spark.sql.broadcastTimeout or disable broadcast join by setting >>>> spark.sql.autoBroadcastJoinThreshold to -1 >>>> >>>> Initially this threshold was 300. I already increased it. >>>> >>>> >>>> Kind Regards, >>>> Sachit Murarka >>>> >>>
