Facing spark.kryoserializer.buffer.max overflow error in Azure Synapse Notebook. How to overcome this?

Hi @Dravya Jain
Welcome to Microsoft Q&A platform and thanks for posting your question here.

You are facing a serialization buffer overflow error while working with a large volume of data in your Synapse Notebook. This error typically occurs when the size of the data to be serialized exceeds the maximum buffer size, even though you’ve set spark.kryoserializer.buffer.max to its upper limit of 2GB.

To resolve this issue, you can try optimizing joins and data processing by using broadcast joins for smaller tables, applying filtering early in the transformation steps to reduce the data size before joins, and using .repartition() or .coalesce() to manage the partitioning of data effectively.

You can also tune Spark configurations by adjusting spark.executor.memory and spark.driver.memory to ensure there’s enough memory for the operations, using spark.sql.shuffle.partitions to control the number of shuffle partitions and avoid large shuffles, and leveraging Dataframe/Dataset optimizations by preferring DataFrames/Datasets over RDDs for better optimization by Spark’s Catalyst optimizer and caching intermediate Data Frames in memory when reused multiple times to avoid recomputation.

Consider using different serialization formats that might be more space-efficient for your particular data structure and process the data in smaller batches, if possible, to avoid hitting the serialization buffer limit. Utilize Spark UI to monitor the execution and identify stages that may be causing the overflow error.

Reference
https://spark.apache.org/docs/latest/sql-performance-tuning.html
https://www.sparkcodehub.com/spark-handle-large-dataset-join-operation
https://stackoverflow.com/questions/78479644/pyspark-azure-synapse-kryoserializer-buffer-overflow
I hope this information helps you. Let me know if you have any further questions or concerns.