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In Wherobots Cloud, Workload History allows you to monitor the progress of workloads (notebooks, SQL sessions, and job runs), understand resource consumption, review the execution timeline, and verify configuration settings.

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Benefits

The Workload History section of Wherobots Cloud allows you to view all executed workloads within your Wherobots Organization and manage job runs.
Monitor Notebooks, Job Runs, and SQL Sessions to understand how resources are being used across your organization.
Stay aware of your organization’s quota consumption to avoid hitting computational limits.
Pinpoint the source of unexpected resource usage and analyze spikes in activity.
Review activity by user or service principal to understand contributions and workload distribution.
Use workload insights to guide resource allocation and optimize costs for your organization.
You can review and filter any workloads executed within your Wherobots Organization.

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Before you start

Before using this feature, ensure that you have the following required resources:
  • An Admin or User Account within a Professional or Enterprise Edition Organization. For more information, see Create a Wherobots Account.

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View Notebook, Job Run, and SQL Session workloads

For insight into your workload trends and individual Job Runs, go to Workload History in the left sidebar of Wherobots Cloud.
Workload History overview
Hover for details — Place your cursor over any point on the chart to display a tooltip with the exact quota usage breakdown at that moment. Use this to investigate any spikes or trends in resource consumption.

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Workload history overview

Workload History provides two main views to analyze your workloads: Chart and Table.

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Chart view

In chart view, you can see an overview of all your workloads, including Notebooks, Job Runs, and SQL Sessions. The chart displays quota usage as a percentage of your organization’s total computing power over time. In Wherobots Cloud, go to Workload History. The chart displays Quota Usage as a percentage on the y-axis over the selected time range on the x-axis. Each area on the chart is color-coded by workload type: The legend on the right side of the chart identifies each workload type by color.
View workloads from the last 24 hours, 7 days, or 30 days
Filter workloads by the Organization member or service principal that started them
Filter workloads by the AWS region where they ran (e.g., aws-us-west-2, aws-eu-west-1, aws-us-east-1)
Filter workloads by type: Notebooks, Job Runs, or SQL Sessions
If you hover over the chart, you can see a breakdown of quota usage over time for Notebooks, Job Runs, and SQL Sessions. Each workload type is color-coded for easy identification:
  • Notebooks (blue)
  • Job Runs (purple)
  • SQL Sessions (green)

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Table view

The table view provides a detailed list of all workloads executed within your Wherobots Organization, along with key information about each workload.
Workload History table view
The Table view includes the following elements:
The name or ID of the workload.
The entity that triggered the workload (Service Principal or User).
If your account is configured to the User role, and you use a service principal-managed API key generated by an Admin in your Wherobots Organization, actions are attributed to the service principal in Audit Logs, with the Admin shown as responsible solely for the API key creation.
The total duration of the workload.
The date and time when the workload completed, or “Running” status.
The AWS region where the workload executed.
The runtime size used (e.g., Tiny, Small, Medium, Large).
The estimated cost of the workload.
Spatial Units consumed by the workload so far. This information can be delayed by up to 20 minutes.

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Filter workloads

To locate specific workloads, you can use multiple filters:
  1. Navigate to Workload History.
  2. Use the Filter by name or id field to search for specific workloads.
  3. Apply filters for Time Range, Owner, Region, or Type as needed.

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Manage Job Runs

In the Workload History view, click on any Job Run workload row to view more information about that specific execution. The detail page includes two tabs: Details and Logs.

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Details tab

The Details tab provides an overview of the Job Run’s configuration, runtime environment, and execution metadata so you can:
  • Review Spark version, runtime size, and environment details
  • Verify scheduling, trigger source, and timeout configuration
  • Understand when the Job Run started, ended, and how long it ran
Each Job Run has a Details page that provides comprehensive information about the workload execution. The Details tab includes the following information about the Job Run execution:
The name of the Job Run, which is either user-defined or auto-generated.
Unique identifier for this specific Job Run.
Indicates if the Job Run is initializing (Starting), in progress (Running), finished (Completed), or unable to be executed (Failed).
Indicates when the Job Run was initiated.
Indicates when the Job Run terminated.
Indicates how much time elapsed between the Start Time and End Time.
The entity that triggered the Job Run (e.g., Airflow, user email).
The runtime size used for this Job Run (e.g., Tiny, Small, Medium, Large).
The version of the runtime environment used for this Job Run.
The maximum duration the Job Run is allowed to run before being automatically stopped, in seconds.
The AWS region where the Job Run executed. Different regions may have different performance characteristics and costs.
The URI of the Python file executed in this Job Run.
The Spark configuration details for this Job Run.
Allows you to stop an ongoing Job Run.

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Consumption metrics

Each Job Run Details page specifies the consumption metrics and details associated with a specific workload execution, including the following:
Consumption metrics
The estimated cost of the workload execution.
Indicates the accumulated Spatial Unit Consumption for this workload.
Indicates the accumulated CPU usage for this workload.
Indicates the accumulated memory used for this workload.

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Configuration information

Each Job Run Details page specifies the Configuration information associated with a specific workload execution, including the following:
Indicates the size of the Runtime associated with the workload.
Specifies the maximum duration the workload is allowed to run before being automatically stopped, in seconds. The default is 3600.
Specifies the compute region where the workload is being executed.
The location or path of the Python script being executed.

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Logs tab

The Logs tab provides real-time access to execution logs for your Job Run including information about:
  • File downloads and uploads
  • Spark submit commands
  • Spark version and environment details
  • Warnings and error messages
  • Application execution progress
The Logs tab includes the following information about the Job Run execution:
The name of the Job Run, which is either user-defined or auto-generated.
Unique identifier for this specific Job Run.
Indicates if the Job Run is initializing (Starting), in progress (Running), finished (Completed), or unable to be executed (Failed).
A link to the Spark UI for this Job Run.
Search through log entries using Cmd/Ctrl + F.
View timestamped log entries showing the execution progress.
Copy all log entries to clipboard.
Download the complete log file for offline analysis.

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Compare workload performance

To compare workload performance with different runtime types:
  1. Run a workload. For more information on initializing a job and runtime selection, see WherobotsRunOperator.
  2. Run that same workload again with either a larger or smaller runtime type.
  3. Review the Detail pages for each executed Job Run to compare performance metrics, specifically:
    • Duration
    • Max CPU Utilization
    • Max Memory Utilization
    • Cost($)
    • SUs (Spatial Units)

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Cancel a workload

Wherobots cancels workloads based on your specification of timeoutseconds in the Job Runs REST API Schema, which defaults to 3600, but you can also cancel a workload in Wherobots Cloud. To cancel a workload within Wherobots Cloud, do the following:
  1. In Workload History, locate the Job Run you wish to cancel.
  2. Click on the Job Run to open its detail page.
  3. Click the Cancel button.
You can only cancel workloads that are currently in the Running state.

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Limitations

The following limitations apply to the Workload History feature:
Workload history data, including Spatial Unit Consumption, Max CPU Utilization, and Max Memory Utilization metrics, is only kept for 90 days.
Spatial Unit Consumption and Cost can take several minutes to display on a Job Run’s Detail page.
The chart displays quota usage as a percentage of your organization’s total computing power.