Serverless job error - spark.rpc.message.maxSize

[adurand-accure]

Hello, 

I am facing this error when moving a Workflow to serverless mode

ERROR : SparkException: Job aborted due to stage failure: Serialized task 482:0 was 269355219 bytes, which exceeds max allowed: spark.rpc.message.maxSize (268435456 bytes). Consider increasing spark.rpc.message.maxSize or using broadcast variables for large values.

on JOB cluster we could set the  spark.rpc.message.maxSize manually to a value greater than 268 m, which looks not possible on Serverless

Any help is appreciated, thx

[Alberto_Umana]

Hi @adurand-accure,

In serverless mode, you cannot directly modify the spark.rpc.message.maxSize parameter. To work around this limitation, you can consider the following approaches:

1. Broadcast Variables: Use broadcast variables for large values. This can help reduce the size of the serialized task by broadcasting large datasets to all nodes instead of including them in the task serialization.
2. Optimize Data Processing: Break down the data processing into smaller tasks or stages to ensure that the serialized task size does not exceed the limit. This might involve restructuring your data processing logic to handle smaller chunks of data at a time.
3. Data Partitioning: Ensure that your data is well-partitioned to avoid large partitions that could lead to oversized serialized tasks. You can repartition your data into smaller partitions using the repartition or coalesce methods in Spark.
4. Review Code for Inefficiencies: Check your code for any inefficiencies that might be causing large task sizes. This could include unnecessary data shuffling, large intermediate data structures, or other factors that contribute to the task size.

[adurand-accure]

Hello Alberto, 

Thanks, I already had this answer from the AI assistant and it didn't solved my problem, I am looking here for something different 🙂 

[PiotrMi]

Hey @adurand-accure 

Without details how your workflow works it can be hard to help. If the job fails on workflow part where you process large chunks of data then partitions or batches are probably your answer. Are u able to share some details?

[adurand-accure]

Hello PiotrMi,
We found out that the problem was caused by a collect() and managed to fix it by changing some code
Thanks for your quick replies
Best regards,
Antoine