fabric-udf-perf-remediate

To fix Fabric UDF cold starts and high capacity usage, run the interactive diagnostic checklist to identify symptoms and apply the provided step-by-step remediation workflow and Python optimization templates.

Fabric User Data Function timeouts are caused by cold starts, library load problems, slow data source processing, and oversized responses, which you can diagnose using the interactive checklist and remediate with the provided playbooks.

To add performance logging to Fabric UDF notebooks, apply the included perf_logging.py templates, which implement logging patterns that capture capacity metrics and diagnostic data across development pipelines.

Diagnosing Fabric UDF reliability issues requires Az.Accounts installed as a dependency, without which the diagnostic playbooks and remediation workflows for cold starts, timeouts, and high CU usage cannot execute properly.

The best way to optimize Python code in Fabric UDFs is to follow the performance-optimization.md patterns alongside the diagnostic workflow, which systematically targets slow processing, library load problems, and oversized responses.