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From Eve Liao <evelia...@gmail.com>
Subject Re: Job is not able to perform Broadcast Join
Date Tue, 06 Oct 2020 19:34:44 GMT
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 <connectsachit@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, <eveliaocc@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 <connectsachit@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
>>>
>>

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