Kryo serialization exception

[Nape]

We are using the following code in his synapse notebook:

spark = SparkSession.builder.appName("dynamicdatamerge").config("spark.serializer", "org.apache.spark.serializer.KryoSerializer" ).config("spark.kryoserializer.buffer.max", "250m").getOrCreate()

However, we are getting the following error:

Job aborted due to stage failure: Task 5 in stage 1469.0 failed 4 times, most recent failure: Lost task 5.3 in stage 1469.0 (TID 10452) (vm-1d956521 executor 1): org.apache.spark.SparkException: Kryo serialization failed: Buffer overflow. Available: 0, required: 7658431. To avoid this, increase spark.kryoserializer.buffer.max value.

We tried to change the value of spark.kryoserializer.buffer.max with no success.

Do you have any suggestion on how to solve this error?

[Nape]

We tried to reduce the size of the object but it did not work. Do you have any examples on serializing the underlying RDD or any other serializer?

[Anonymous · Accepted solution]

Hi @Nape ,

I understand that you have tried reducing the size of the object you are trying to serialize, but this has not resolved the issue.
FYI: For faster serialization and deserialization spark itself recommends to use Kryo serialization.

I have a few additional suggestions for you, can you please try to refer the below links -
Kryo Serialization in Spark - Knoldus Blogs
Apache Spark: All about Serialization | by Jay | SelectFrom
8 Performance Optimization Techniques Using Spark - Syntelli Solutions Inc.
Troubleshooting Spark Issues — Qubole Data Service documentation

Hope this helps in resolving your issue.

Thank you

[Anonymous]

Hi @Nape ,

Glad to know that your query got resolved.

Please continue using Fabric Community for help regarding your issues.

[Anonymous]

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.

[Anonymous]

Hello @Nape ,
We haven’t heard from you on the last response and was just checking back to see if you have a resolution yet .
In case if you have any resolution please do share that same with the community as it can be helpful to others .
Otherwise, will respond back with the more details and we will try to help .