What causes Py4JJavaError?

Running Delta table write operations (including VACUUM and OPTIMIZE) on Fabric Runtime 1.3 (Spark 3), which contained a known bug causing Py4J bridge errors between Python and the JVM. Using the Delta table maintenance pattern (vacuum/optimize) via PySpark on an affected Fabric Spark runtime version before the fix was deployed. Incompatibility between the Delta Lake library version bundled with Fabric Runtime 1.3 and specific write or maintenance operations. Attempting to write to a Delta table in a Lakehouse where the underlying Delta protocol version conflicts with the Spark runtime's Delta client

How do I fix Py4JJavaError?

Step 1: Verify your Fabric Spark environment is running Runtime 1.3 — if so, check whether your workspace has received the platform fix deployed after 12/15/2025 by reviewing Fabric release notes or the known issues tracker.. Step 2: As an immediate workaround, switch your Spark pool to Fabric Runtime 1.2 or the latest available runtime if Runtime 1.3 is confirmed as the source of the error and the fix has not yet been applied to your region.. Step 3: Wrap Delta write and maintenance operations in try/except blocks (as shown in the official sample code) to catch Py4JJavaError gracefully, log the failure, and trigger alerting rather than silently failing.. Step 4: If running table maintenance (OPTIMIZE/VACUUM), test running operations individually (optimize only, then vacuum separately) to isolate which operation triggers the error in your specific environment.. Step 5: If the error persists after the platform fix date (post 12/15/2025), open a Microsoft Fabric support ticket referencing Known Issue ID 1184 and provide your workspace ID, Lakehouse ID, and the full Py4JJavaError stack trace.

High severitydata formatMicrosoft Fabric →

Power BI Refresh Error:
Py4JJavaError

What does this error mean?

A Py4JJavaError occurs when attempting to save or write a Delta table in Microsoft Fabric using Spark 3 (Fabric Runtime 1.3), causing the write operation to fail. This is a known Fabric platform issue (Issue ID 1184) that has been closed as of December 2025.

Common causes

1Running Delta table write operations (including VACUUM and OPTIMIZE) on Fabric Runtime 1.3 (Spark 3), which contained a known bug causing Py4J bridge errors between Python and the JVM
2Using the Delta table maintenance pattern (vacuum/optimize) via PySpark on an affected Fabric Spark runtime version before the fix was deployed
3Incompatibility between the Delta Lake library version bundled with Fabric Runtime 1.3 and specific write or maintenance operations
4Attempting to write to a Delta table in a Lakehouse where the underlying Delta protocol version conflicts with the Spark runtime's Delta client

How to fix it

1Step 1: Verify your Fabric Spark environment is running Runtime 1.3 — if so, check whether your workspace has received the platform fix deployed after 12/15/2025 by reviewing Fabric release notes or the known issues tracker.
2Step 2: As an immediate workaround, switch your Spark pool to Fabric Runtime 1.2 or the latest available runtime if Runtime 1.3 is confirmed as the source of the error and the fix has not yet been applied to your region.
3Step 3: Wrap Delta write and maintenance operations in try/except blocks (as shown in the official sample code) to catch Py4JJavaError gracefully, log the failure, and trigger alerting rather than silently failing.
4Step 4: If running table maintenance (OPTIMIZE/VACUUM), test running operations individually (optimize only, then vacuum separately) to isolate which operation triggers the error in your specific environment.
5Step 5: If the error persists after the platform fix date (post 12/15/2025), open a Microsoft Fabric support ticket referencing Known Issue ID 1184 and provide your workspace ID, Lakehouse ID, and the full Py4JJavaError stack trace.

Frequently asked questions

Is this error specific to VACUUM operations or does it affect all Delta table writes?

Based on the known issue documentation (ID 1184), the error was observed during Delta table save operations on Fabric Runtime 1.3 broadly, with table maintenance operations (VACUUM/OPTIMIZE) being a documented trigger. However, other write patterns may also be affected depending on the specific Delta operation and schema.

How do I check which Fabric Spark runtime my notebook or Spark job is using?

In the Fabric portal, navigate to your workspace, open the Spark pool or environment settings associated with your notebook or Spark job, and look for the 'Runtime version' field. You can also check programmatically at the start of a notebook using 'spark.version' or by inspecting the environment configuration in Data Engineering settings.