After adding the sequential ids you might need a repartition? I've found using monotically increasing id before that the df goes to a single partition. Usually becomes clear in the spark ui though
On Tue, 6 Oct 2020, 20:38 Sachit Murarka, <connectsac...@gmail.com> wrote: > 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 >>>>> >>>>
