When running Lakeflow jobs in a cloud environment, performance tuning and cost optimization often go hand in hand. Inefficient resource usage can lead to unnecessary expenses and slower job execution. To address this, here’s a systematic approach based on key metrics and actionable insights.
1. Monitor Worker CPU Utilization
Condition: Average Worker CPU < 80% (with multiple workers or large instance)
Why It Matters: Underutilized workers mean you’re paying for resources you don’t need.
Action: Downscale your cluster to reduce costs without impacting performance.
2. Watch for CPU Wait Time
Condition: Average Worker CPU Wait Time > 10%
Why It Matters: High wait time indicates I/O bottlenecks or insufficient memory.
Action: Add disk or memory to improve throughput.
3. Keep an Eye on Driver CPU
Condition: Average Driver CPU > 80%
Why It Matters: A stressed driver can slow down job orchestration.
Action: Upscale the driver to ensure smooth execution.
I am passionate about empowering, educating, and encouraging individuals pursuing a career in data engineering. Currently a Senior Data Engineer at Capgemini
