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Version: v2.9.1

Monitoring

The cluster dashboard contains 5 main windows:

  1. Events
  2. Configuration
  3. Dependencies
  4. JDBC
  5. Access Management

1. Events Window

Basic Configuration Display

Shows:

  • ID
  • Cloud Provider
  • Cluster Type
  • Runtime Version
  • Instance Size
  • Min & Max Instances
  • Auto Shutdown
  • Parallel Execution
  • Workers per Node

Job Statistics

Includes job counts by status:

  • Submitted
  • Running
  • Done
  • Error
  • Terminated
  • Stopping
  • Stopped
  • Total Jobs

Event Logs

Tabs:

  • Stdout
  • Stderr

Actions:

  • Refresh
  • Copy
  • Download (.log)

2. Configuration Window

Allows editing cluster settings.

Important Note

  • Cluster must be destroyed to edit.
  • Cluster Type cannot be edited after creation.
  • All other fields are editable.

3. Dependencies

  • Manage dependency repositories.
  • Upload new files using + File.
  • Delete via 3-dot action menu.

4. JDBC Tab

Provides:

  • JDBC driver
  • Connection URL
  • Username
  • Auth token
  • Workspaces attached to this cluster

Note: Thrift SQL not supported for current runtime.


5. Access Management

  • Manage which workspaces can attach to this cluster.
  • Add/remove by searching or selecting checkbox.
  • Can Attach To option links workspace to cluster.

Additional Features

Auto Shutdown

  • Cluster stops automatically after idle timeout.
  • Minimum timeout: 1 minute
  • Minimizes compute cost.

Warm Start/Stop

  • Reduces cluster startup from 6–7 minutes → ~1 minute
  • Improves responsiveness and resource utilization.

Start/Stop a Cluster

Start:

  • Go to cluster dashboard → click Start

Stop:

  • Click Stop to halt jobs & free resources

Cluster Details (Once Running)

Displays:

  • Cloud provider
  • Compute type
  • Runtime version
  • Disk configuration
  • Instance size
  • Execution slots

Job Management

Shows:

  • Submitted jobs
  • Running jobs
  • Completed & stopped jobs
  • Errors

Users can click View Logs to troubleshoot.


Graviton (ARM64) Support

  • ARM-based compute
  • Higher memory/disk throughput
  • Ideal for Spark, Python pipelines, ML workloads

Enable during cluster creation: