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 >>> >>
