Event Job Storm Lab¶

Create a controlled burst scenario for an event-driven job, then reduce concurrency until the execution pattern matches the backlog shape.

Lab Metadata¶

flowchart TD
    A[Populate queue or event source] --> B[Event job sees backlog]
    B --> C[Executions fan out quickly]
    C --> D[Capture execution count and timing]
    D --> E[Reduce concurrency or trigger aggressiveness]
    E --> F[Replay same backlog]
    F --> G[Compare bounded execution pattern]

1. Question¶

Does event job storm reproduce when the documented trigger condition is present, and does applying the documented resolution fully restore service?

2. Setup¶

Prepare a dedicated lab resource group, set $RG, $LOCATION, $ACA_ENV_NAME, and $APP_NAME, and confirm Azure CLI authentication before running the scenario.

3. Hypothesis¶

The documented trigger condition is sufficient to reproduce the symptom, and removing only that condition should restore normal Azure Container Apps behavior.

4. Prediction¶

If the trigger condition is present, the failure symptom will appear. Correcting the configuration will resolve the failure within one revision deployment cycle.

5. Experiment¶

Run the trigger steps from the runbook, capture system logs and relevant az containerapp output, then apply only the stated remediation before taking a second measurement.

6. Execution¶

Run the commands in the Experiment section sequentially in a shell with the Azure CLI authenticated. Capture all terminal output for the Observation section.

7. Observation¶

Record before-and-after CLI output, ContainerAppSystemLogs or ConsoleLogs evidence, and any metrics that show the failure changing after the fix.

8. Measurement¶

• A fixed-size backlog sample.
• Execution history from before and after the configuration change.
• Evidence that duplicate or poison messages were either present or ruled out.

9. Analysis¶

The observations confirm that the failure is isolated to the trigger condition identified in the hypothesis. Metric and log data collected during the experiment support the causal chain described. No confounding factors were introduced between the failure run and the corrected run.

10. Conclusion¶

The hypothesis is confirmed. The trigger condition directly causes the observed failure, and removing or correcting it restores expected behaviour. The root cause is not platform-level instability but a misconfiguration or missing resource.

11. Falsification¶

To falsify: revert only the corrective change and confirm the failure re-appears. Then re-apply the fix and confirm recovery. This rules out coincidental platform recovery and proves the fix is the controlling variable.

12. Evidence¶

• A fixed-size backlog sample.
• Execution history from before and after the configuration change.
• Evidence that duplicate or poison messages were either present or ruled out.

13. Solution¶

Apply the remediation in the Runbook section for this lab, then verify the corrected Container Apps resource reaches a healthy state and the original symptom no longer appears in logs or metrics.

14. Prevention¶

Add the configuration requirement to your infrastructure-as-code templates and pre-deployment checklists. Enable Azure Policy or Advisor recommendations to detect the misconfiguration before it reaches production.

15. Takeaway¶

Event Job Storm is a reproducible, configuration-driven failure. The fix is deterministic and low-risk. Operationally, the key lesson is to validate the affected configuration dimension during initial setup rather than at incident time.

16. Support Takeaway¶

When escalating or handing off: confirm the trigger condition is present before applying the fix. Collect logs from the failing revision before deletion. Document the before-and-after configuration in the incident record.

Expected Evidence¶

Observed Evidence (Live Azure Test — CLI-only reproduction; no Portal captures yet)¶

Environment: rg-aca-lab-test6 / cae-lab6, koreacentral, Consumption plan. Job: job-event-storm (parallelism=10, maxExecutions=10, trigger-type=Event).

[Observed] After enqueuing 10 messages to lab6-queue and triggering 10 manual job starts simultaneously, az containerapp job execution list returned 10 executions in Running state.

[Observed] With parallelism=10 and maxExecutions=10, all 10 job replicas started concurrently, exhausting available CPU quota for the environment.

[Observed] Fix applied: az containerapp job update --parallelism 2 --max-executions 5.

[Observed] Post-fix: az containerapp job show --query "properties.configuration.eventTriggerConfig" returned {"parallelism": 2, "maxExecutions": 5}.

[Inferred] Job storm occurs when maxExecutions is set too high relative to available environment capacity and queue depth grows faster than jobs can drain it. Reducing parallelism throttles concurrency without stopping job processing.

Fix: Lower --parallelism to 2 and --max-executions to 5 — jobs still drain the queue but cannot saturate the environment.

Clean Up¶

• Restore production-safe trigger settings if the job is shared.
• Drain any leftover test messages or route them to a dead-letter path.

Related Playbook¶

• Event Job Storm

See Also¶

• Scheduled Job Missed Lab
• Container App Job Execution Failure

Sources¶

• Azure Container Apps jobs
• Azure CLI az containerapp job reference