Fixing Azure SQL Connection Errors in Scheduled Azure Functions or Synapse Jobs

As a Data Analyst working in Germany, I recently faced an issue while automating a daily data processing task in Azure. The goal was simple: run a scheduled job every morning to process data and sync it to Azure SQL Database using Python.

The Problem

When I ran the code manually, everything worked perfectly. However, when the same code executed as a scheduled job (via Azure Function or Synapse pipeline), it failed with the following error:

(pyodbc.Error) ('HY000', "[HY000] [Microsoft][ODBC Driver 18 for SQL Server][SQL Server]Database 'xxxxxxx' on server 'xxxxxxxxxxxxxxxxxx' is not currently available. Please retry the connection later. If the problem persists, contact customer support, and provide them the session tracing ID of '{...}'. (40613) (SQLDriverConnect)") (Background on this error at: https://sqlalche.me/e/20/dbapi)

Key points:

- Error code: HY000 (generic ODBC error) - SQL error code: 40613 (Database unavailable) - Happens only on scheduled runs, not manual runs.

What I Tried

- Added retry logic (3 attempts) - Increased LoginTimeout in the connection string - Verified credentials and network settings

None of these solved the issue.

The Root Cause

The problem was Azure SQL Database Auto-Pause. When the scheduled job triggered early in the morning, the database was in a paused state to save costs. The first connection attempt failed because the database was still waking up.

The Fix

Disable Auto-pause for your Azure SQL Database: 1. Go to Azure Portal → SQL Database → Overview → Essentials. 2. Find Auto-pause delay and turn it off. 3. Save changes.

After disabling auto-pause, the scheduled job ran successfully every time.

Why This Happens

Auto-pause is part of serverless tier in Azure SQL to reduce costs. When paused, the database needs time to resume, which can cause connection timeouts. Scheduled jobs often hit this cold-start scenario.

Best Practices

If cost is a concern, consider: - Setting a longer auto-pause delay instead of disabling it completely. - Adding retry with exponential backoff in your code. - For production workloads, avoid auto-pause to ensure reliability.