Monitoring Memory Usage During Databricks Jobs
To monitor memory usage during Databricks jobs, you can leverage several tools and techniques:
- Spark UI: The Spark UI provides a native graph that depicts cluster memory usage over time. This is useful for visual inspection but may not be sufficient for systematic monitoring.
- Ganglia API: For Databricks runtime versions less than 13.0, you can query the Ganglia API to obtain raw memory usage data. This involves accessing metrics like bmem_used and bmem_total to compute memory allocated and used by specific jobs.
- Compute Metrics UI: Databricks offers a compute metrics UI that provides a comprehensive view of cluster resource usage, including Spark consumption and internal Databricks processes. This tool is available for all-purpose and jobs compute.
These methods allow you to monitor and optimize memory usage effectively during Databricks jobs.
Frequently Asked Questions
- Q: How do I access the Spark UI in Databricks?
A: You can access the Spark UI by navigating to the Jobs tab in your Databricks workspace, selecting the job you’re interested in, and clicking on the Spark UI link.
- Q: What is the difference between Ganglia and the Compute Metrics UI?
A: The Compute Metrics UI offers a more comprehensive view of resource usage, including both Spark and internal Databricks processes, whereas Ganglia only measures Spark container consumption.
- Q: How often are metrics collected in the Compute Metrics UI?
A: Metrics are collected every minute, allowing for real-time monitoring with a delay of less than one minute.
- Q: Can I use the Compute Metrics UI for serverless compute?
A: No, serverless compute uses query insights instead of the metrics UI. For serverless, you should refer to the query insights for metrics.
- Q: How do I reduce unnecessary memory usage in Databricks?
A: You can reduce memory usage by enabling dynamic allocation, setting a lower memory fraction, and optimizing Spark configurations.
- Q: Can I monitor memory usage programmatically?
A: Yes, you can monitor memory usage programmatically by querying the Ganglia API or using the Databricks API to fetch job metrics.
- Q: Are there any limitations to using the Ganglia API?
A: Yes, Databricks is deprecating Ganglia, so it’s only applicable for runtime versions less than 13.0.
Bottom Line
Monitoring memory usage during Databricks jobs is crucial for optimizing performance and reducing costs. By leveraging tools like the Spark UI, Ganglia API, and Compute Metrics UI, you can effectively manage and optimize your cluster’s memory allocation.