A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?
To evaluate the timeline for cluster resizing events, one should review the Cluster Event Log. The Cluster Event Log provides detailed information about various events affecting the cluster, including resizing events. It displays the timestamp, event type (e.g., CLUSTER_RESIZED), and relevant details for each event, which is essential for understanding the causes behind autoscaling events.
Cluster Event Log
The Cluster Event Log provides detailed information about various events affecting the cluster throughout its lifecycle, including cluster creation, restarts, termination, and resizing events. It displays the timestamp, event type (e.g., "CLUSTER_RESIZED"), and relevant details for each event, allowing the administrator to review the timeline for cluster scaling behavior and identify potential patterns related to user activity or resource-intensive queries.
Cluster event log
The Cluster Event Log in Databricks will show the timeline for cluster resizing events, including details about when and why a cluster was resized (scaled up or down). This log would help the workspace administrator determine the causes of cluster scaling, whether due to many concurrent users submitting jobs or a few users running resource-intensive queries. less suitable: C. Ganglia provides metrics on system-level performance, such as CPU and memory usage, but does not log specific cluster scaling events.
cluster event log. D
The timeline for cluster resizing events can be reviewed in the Cluster Event Log. This log provides information about cluster scaling events, including when the cluster is scaled up or down. You can access this information to understand the reasons behind autoscaling events and whether they are triggered by many concurrent users or resource-intensive queries.