Refresh SQL Endpoint using semantic link labs: Intermittent failures

Hi alfBI ,

Thank you for reaching out to the Microsoft Community Forum.

The intermittent failures when using the refresh_sql_endpoint_metadata function from the semantic-link-labs library in Microsoft Fabric, particularly encountering a KeyError related to missing DataFrame columns.

Please refer below workarounds.

1. Validate DataFrame Columns Before Access. Add a check before accessing the columns

expected_cols = ['Table Name', 'Status', 'Start Time', 'End Time', 'Last Successful Time']
if all(col in x.columns for col in expected_cols):
    display(x[expected_cols])
else:
    print("Expected columns not found. DataFrame is likely empty.")
expected_cols = ['Table Name', 'Status', 'Start Time', 'End Time', 'Last Successful Sync

Note: This prevents the notebook from failing when the DataFrame is empty.

2. Make sure at least one table exists in the Lakehouse before triggering the refresh. You can add a pre-check using the semantic-link-labs API to list tables and confirm presence.

3. Wrap the refresh logic in a try-except block

try:
    x = labs.refresh_sql_endpoint_metadata(item=item, type=type, workspace=workspace, tables=tables)
    display(x)
except KeyError as e:
    print(f"KeyError encountered: {e}")

Note: This helps log errors and optionally retry or skip execution

4. If you are using a multi-step ETL/ELT pipeline, consider forcing a sync of the T-SQL endpoint using Semantic link.

I hope this information helps. Please do let us know if you have any further queries.

Regards,

Dinesh