Dataproc Alert policy

[sharontmathew]

1. custom/cpu/utilization
2. custom/disk/percent_used

how are the above cloud dataproc batch metric useful when setting up the alerting policy?

[ms4446 · Accepted solution]

The custom/cpu/utilization and custom/disk/percent_used metrics are essential for effective alerting policies in Cloud Monitoring, targeting Dataproc batch jobs. These metrics offer critical insights into the health and performance of your clusters, empowering proactive issue resolution and resource optimization.

CPU Utilization (custom/cpu/utilization) :

• Performance Indicator: Elevated CPU usage may indicate that your job is under-resourced, running slower than expected.
• Scaling Decisions: Utilize CPU utilization alerts to justify scaling actions, accommodating increased demand.
• Troubleshooting: Sudden spikes could suggest inefficient code or resource conflicts, necessitating code audits or configuration reviews.

Disk Usage (custom/disk/percent_used) :

• Job Failure Prevention: Adequate disk space is pivotal; nearing capacity can cause job disruptions. Early alerts facilitate timely expansions or cleanup.
• Resource Planning: Continual monitoring aids in predicting storage requirements, preventing capacity surprises.

Setting Up Alerts:

1. Select a Metric: Navigate to "ALERT CONDITIONS" in Cloud Monitoring and click "Select a metric."
2. Expand Cloud Dataproc Batch: Access specific metrics for detailed monitoring.
3. Choose Metric: Opt for custom/cpu/utilization or custom/disk/percent_used.
4. Configure Trigger: Establish alerts based on your operational benchmarks—consider threshold levels, notification frequency, and alert severity.

Going Beyond the Basics: While CPU and disk metrics are fundamental, integrating additional metrics—such as Memory Usage, Network I/O, and Job Completion Time—enhances your monitoring framework. These metrics help track potential memory issues, identify network bottlenecks, and monitor job efficiency.

Proactive Monitoring: Beyond reactive alerts, regularly review metrics and logs to discern trends or imminent concerns. This holistic approach to monitoring ensures your Dataproc batch jobs are not only running smoothly but also optimized for performance and cost.

By strategically monitoring these metrics and setting up customized alerts, you ensure that your Dataproc batch jobs operate efficiently, with ideal resource utilization, thereby enhancing overall productivity and system health.