Your batch processing implementation needs comprehensive token lifecycle management and robust error handling. Here’s how to address all three aspects:
API Token Lifetime Configuration:
The default 30-minute token lifetime is configured in the FactoryTalk MES API Gateway settings. For service accounts running batch operations, you can extend this without compromising security. Access the API Gateway admin console (typically at https://your-mes-server:8443/api-admin) and navigate to Security > Token Policies. Create a dedicated token policy for your integration service account with these settings:
- Access Token Lifetime: 7200 seconds (2 hours)
- Refresh Token Lifetime: 28800 seconds (8 hours)
- Enable Sliding Refresh (extends refresh token lifetime with each use)
This gives your batch jobs sufficient time to complete while maintaining reasonable security boundaries. Apply this policy specifically to your integration service account, not globally.
Token Refresh Logic Implementation:
Modify your batch job to implement proactive token refresh with error recovery:
TokenManager tokenMgr = new TokenManager(authClient);
long tokenRefreshInterval = 6600000; // 110 minutes
long lastRefresh = System.currentTimeMillis();
for (WorkOrder wo : workOrders) {
if (System.currentTimeMillis() - lastRefresh > tokenRefreshInterval) {
tokenMgr.refreshAccessToken();
lastRefresh = System.currentTimeMillis();
}
// Retry logic with token refresh on 401
}
Implement a TokenManager class that stores both access and refresh tokens, handles proactive refresh at 90% of token lifetime, and provides retry logic when API calls fail with 401 errors. The refresh endpoint is POST /api/auth/refresh with the refresh token in the request body.
Batch Job Error Handling for Mid-Process Failures:
To prevent data inconsistency, implement transactional batch processing with checkpoint/restart capability:
-
Checkpoint Mechanism: After every 50 work orders, persist the current batch position to a database table or file. Include the work order ID of the last successfully processed item and the current token state.
-
Idempotent Updates: Ensure your work order update logic is idempotent - if a work order was already updated, the API call should succeed without creating duplicates or conflicts. Use conditional updates based on version numbers or timestamps.
-
Structured Error Recovery: When token expiry occurs mid-batch:
- Attempt token refresh using the refresh token
- If refresh succeeds, retry the current work order update
- If refresh fails (refresh token also expired), re-authenticate and resume from the last checkpoint
- Log all retry attempts with work order IDs for audit trail
-
Dead Letter Queue: Implement a failed work order queue. If a work order fails after 3 retry attempts (even with fresh tokens), move it to a separate queue for manual review. This prevents one problematic work order from blocking the entire batch.
Enhanced Implementation Pattern:
Consider implementing a batch processor with these components:
- Token manager with automatic refresh (monitors expiry time from JWT claims)
- Checkpoint manager (persists progress every N records)
- Retry handler with exponential backoff (handles transient failures)
- Transaction coordinator (ensures atomic updates or rollback for related work orders)
Monitoring and Alerting:
Add instrumentation to track:
- Token refresh frequency and success rate
- Batch job duration and throughput (work orders per minute)
- Authentication failure rates and recovery success
- Checkpoint frequency and restart events
Set up alerts if token refresh fails more than twice in a single batch job, or if batch processing time exceeds expected duration by 50%.
With these changes, your batch jobs will handle token expiry gracefully, maintain data consistency through checkpointing, and provide clear audit trails for troubleshooting.