- Verify that scheduled jobs are running on time.
- Identify failed or unusually long-running jobs.
- Drill into logs only when something looks wrong.
How to view Cron job executions
- Open your project in the Console.
- Select your chosen Environment.
- Navigate to:
- Services —> Apps & Services —> Apps —> app
- Services —> Apps & Services —> Apps —> Workers (for background jobs).
- Click the Crons tab to view your scheduled jobs.
Understanding the visualization
Each execution appears as a vertical bar in the visualization:| Visual element | Meaning | Example |
|---|---|---|
| Color | Indicates success or failure. | Green = success, Red = failure |
| Height | Proportional to the job’s execution time. | Taller bars indicate longer runtimes |
| Click a bar | Opens detailed logs for that specific run. | Review stdout/stderr for debugging |
- A sudden tall red bar indicates a failed job that took longer than usual.
- Consistently tall green bars suggest a job that may need optimization.
- Execution date and time
- Duration
- Status (success/failure)
Troubleshooting and best practices
- Monitor for patterns: Look for recurring red (failed) bars or progressively longer runtimes.
- Drill into logs: Clicking a bar takes you directly to logs for that specific execution.
- Correlate with metrics: Combine this view with application metrics to identify performance bottlenecks.
- Adjust Cron timing: If jobs overlap or run too frequently, adjust their schedule in your
cronsconfiguration.
Investigate consistently high durations
If you notice that your Cron job runtimes are consistently increasing, this may indicate:- Growing data volumes,
- Inefficient queries
- Resource limits being reached.
When to check Cron execution history
| Use case | Why it’s useful |
|---|---|
| Autoscaling investigations | Understand whether jobs are driving unexpected CPU or memory spikes. |
| Deployment monitoring | Verify that scheduled jobs resume normally after deployments. |
| Job failure analysis | Quickly locate the time and context of a failed execution. |
| Performance tuning | Spot trends in runtime duration and optimize resource usage. |