Kryo serialization exception

Hi Nape - Thanks for using Fabric Community,

As I understand when working with spark you are getting an Error: org.apache.spark.SparkException: Kryo serialization failed: Buffer overflow.

Here are some additional suggestions on how to resolve the Kryo serialization buffer overflow error:

• Reduce the size of the object you are trying to serialize. This may mean breaking it down into smaller pieces, or using a more efficient serialization format. For example, instead of serializing a DataFrame, you could serialize the underlying RDD.
• Use a different serializer. Spark also supports other serializers, such as Java serialization and the built-in serializer. You can try switching to one of these serializers to see if it resolves the issue.
• Increase the amount of memory available to Spark executors. This will give Kryo more room to buffer the object it is serializing.

In your case, you have already tried to increase the value of spark.kryoserializer.buffer.max, but this has not resolved the issue. This suggests that the object you are trying to serialize is very large, or that you are not using the Kryo serialization library efficiently.

Here are some specific suggestions:

• Try reducing the size of the object you are trying to serialize. For example, you could break it down into smaller pieces, or you could filter out unnecessary data.
• Try using a more efficient serialization format. For example, instead of serializing a DataFrame, you could serialize the underlying RDD.
• Try using a different serializer, such as Java serialization or the built-in serializer.
• Try enabling Kryo serialization tracing to get more information about the object that is causing the problem.