GCP Dataproc class not found error

[milky_way]

Hi, 

I have a pipeline set up, it creates a cluster perform an ingestion and delete a cluster. In success logs it starts with downloading the required dependencies. 

But when for some reason, if the job fails and cluster is not deleted. When we trigger the pipeline it uses the same cluster as it was not deleted, but when writing to kafka it throws an error that class not found for byteserializer. 

My question is as the cluster is same why is this issue coming ? Checking the success logs i can see that dependencies are downloaded and jobs executes files, but in failure scenario dependencies don't gets downldoaded and it  says 0 artifacts copied 1 received and finally the jobs fails due to class error. 

why is it so? i have to delete the cluster and run the job again. in that case it works. 

[ms4446]

It appears you're encountering a dependency management issue in Google Cloud Dataproc, particularly when a cluster unexpectedly persists after a job failure. This can lead to a "class not found" error, such as with the ByteSerializer when writing to Kafka. 

Dataproc optimizes job execution by caching dependencies. However, if a job modifies its dependencies or if there's a change in the code that alters dependency requirements, the cached dependencies on a reused cluster might not align with the new requirements, potentially leading to class loading errors.

The observation of "0 artifacts copied" suggests that the necessary dependencies aren't being correctly downloaded on subsequent runs using the existing, failed cluster. This could be due to several factors:

• Network Issues: Temporary network problems could prevent successful dependency downloads.

• Permissions: The Dataproc service account might lack sufficient permissions to fetch dependencies from your specified artifact repository.

• Repository Availability: The repository hosting your dependencies may have been temporarily unavailable.

How to Troubleshoot

• Check Logs: Investigate the Dataproc job logs and the cluster's YARN logs for detailed error messages related to dependency download failures or class loading issues.

• Verify Dependencies: Ensure that all necessary dependencies, including the ByteSerializer class, are correctly packaged with your job artifact (e.g., a JAR file) and that this artifact is accessible to the Dataproc cluster.

• Check Permissions and Network Connectivity: Confirm that the Dataproc service account has appropriate permissions and that there are no network restrictions or firewall rules blocking dependency downloads.

How to Solve

1. Force Fresh Dependency Download:

   • Initialization Actions: Utilize initialization actions to download dependencies before job execution, ensuring a clean setup even after a job failure.

   • Scripting within Job Submission: Incorporate a script in your job submission logic to freshly download and set up dependencies, circumventing potential caching issues.

2. Cluster Recreation:

   • Adjust your pipeline to ensure clusters are deleted after job completion, regardless of success or failure, to guarantee a fresh environment for each run.

3. Address Dependency Conflicts:

   • Use virtual environments or containerization (e.g., Docker) to isolate job dependencies, minimizing conflict risks.

Additional Considerations

• Explicit Dependency Declaration: When submitting jobs, explicitly specify dependencies using options like --packages or --jars to ensure all necessary libraries are available.

• Dataproc Image Versions: Use a Dataproc image version compatible with your job's dependencies to avoid incompatibilities.

• Use of Containers: For enhanced isolation, consider running Dataproc jobs on GKE, which can help manage dependencies more effectively.

The issue you're facing with dependency management in Dataproc is complex, involving several potential factors like caching, network or permission issues, and the specifics of dependency handling in persistent clusters. By ensuring clean cluster states, explicitly managing dependencies, and considering containerization for isolation, you can mitigate these issues and achieve more reliable job executions.