Showing results for 
Search instead for 
Did you mean: 
Data Engineering
Join discussions on data engineering best practices, architectures, and optimization strategies within the Databricks Community. Exchange insights and solutions with fellow data engineers.
Showing results for 
Search instead for 
Did you mean: 

Whitelisting GraphFrame Jar files does not work for shared compute.

New Contributor


I'm encountering a Py4JSecurityException while using the GraphFrames jar library in a job task with shared compute. Despite following all documentation to whitelist my jar libraries in Volumes and ensuring compatibility with my Spark and Scala versions, the issue persists.

I've confirmed that my Volumes directory is in the Unity Catalog's allowlist and Table ACLs are not enabled.

Given the size of my input data (~500 million records), using a non-distributed package like NetworkX isn't feasible due to OutofMemoryErrors. Disabling Py4JSecurityException isn't an option, and neither is continuing with single user clusters.

Any guidance on this issue would be greatly appreciated.



Community Manager
Community Manager

Hi @spark_user1I understand that you’re facing a Py4JSecurityException while working with the GraphFrames jar library in a job task with shared compute.

Let’s tackle this issue step by step:

  1. Whitelisting JAR Libraries:

    • You mentioned that you’ve followed the documentation to whitelist your JAR libraries in Volumes. However, the issue persists.
    • Let’s revisit this step:
      • Ensure that the GraphFrames JAR is correctly added to your Spark job. You can do this using the --jars option in your spark-submit command.
      • Here’s an example of how to include the GraphFrames JAR:
        spark-submit \
            --master yarn-cluster \
            --jars path_to_your_jars/graphframes-0.7.0-spark2.4-s_2.11.jar \
      • Replace path_to_your_jars with the actual path to your JAR file.
  2. Environment Variables and Compatibility:

    • Verify that your environment variables are correctly set. Sometimes, issues like this can arise due to incorrect environment configurations.
    • Ensure that your Spark version, Scala version, and GraphFrames version are compatible. Mismatched versions can lead to unexpected errors.
  3. Shared Compute and Security Settings:

    • Since you’re using shared compute, check if there are any specific security settings or restrictions that might be causing the issue.
    • Confirm that your Unity Catalog’s allowlist includes the necessary directories and permissions for your job.
  4. Memory Constraints and Alternatives:

    • You mentioned that using a non-distributed package like NetworkX isn’t feasible due to OutofMemoryErrors. In that case:
      • Consider optimizing your Spark job to handle large data efficiently. Tune memory settings, partition sizes, and caching strategies.
      • If possible, break down your data into smaller chunks or use sampling techniques.
      • Explore distributed algorithms within Spark (such as GraphFrames) that can handle large-scale graph processing efficiently.
  5. Cluster Configuration:

    • Single user clusters might not be sufficient for your workload. Consider using larger clusters or autoscaling options to accommodate your data size.

If you encounter any specific error messages or need further assistance, feel free to share more details, and I’ll be happy to assist! 🚀

Join 100K+ Data Experts: Register Now & Grow with Us!

Excited to expand your horizons with us? Click here to Register and begin your journey to success!

Already a member? Login and join your local regional user group! If there isn’t one near you, fill out this form and we’ll create one for you to join!