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.