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