Performance enhancements for Indicator nightly job

  • Release version: Australia
  • Updated March 12, 2026
  • 2 minutes to read
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    Summary of Performance enhancements for Indicator nightly job

    The Performance enhancements for the Indicator nightly job in the Australia release improve the processing efficiency of indicators within the Governance, Risk, and Compliance (GRC) module. The enhancements introduce parallel processing through new custom queues and an optimized multi-step architecture that accelerates indicator execution and supporting data collection.

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    Key Features

    • New Custom Queues: Two additional queues—Indicator Data Queue and Supporting Data Queue—enable parallel processing. The Indicator Data Queue handles indicator processing, while the Supporting Data Queue manages events related to control, risk, and issue updates.
    • Three-Step Processing Architecture:
      • Step 1: Indicators requiring processing are queued in the Batch Indicator Data Queue. The Batch Indicator Data Processor runs indicators, updates statuses, and generates results or tasks.
      • Step 2: The Indicator Supporting Data Processor updates related controls, risks, and issues, then pushes indicators to the Indicator Statistics Table.
      • Step 3: The Supporting Data Collection Job collects supporting data from the Indicator Statistics Table, using both default and custom queues for efficient processing.
    • Business Rule Optimization: Indicator result business rules are not executed during the nightly job to improve performance; instead, these rules run when indicator tasks are completed.
    • Round-Robin Queue Assignment: The Supporting Data Collection Job dynamically assigns batches to available queues in a round-robin manner, optimizing resource usage and reducing processing time.

    Data Model Changes

    • Sample Collection Type Field: Added to the Indicator template form with options “Count” (default) or “Percentage,” allowing configuration of how supporting data is collected.
    • Due Date Duration Field: Added to manual indicators to specify task due dates, enabling reminder emails to be sent to indicator task owners.
    • New JSON Field: Added to the indicator supporting data table to enhance data management efficiency.

    Practical Impact for ServiceNow Customers

    These enhancements enable ServiceNow GRC customers to process a larger volume of indicators more quickly and efficiently, improving the accuracy and timeliness of risk, control, and issue updates. The improved architecture and queue management reduce execution time and improve resource utilization during nightly indicator jobs. Additionally, configurable data collection and task due dates enhance flexibility and task management within indicator workflows.

    To support parallel processing capabilities, two additional custom queues such as the Indicator Data Queue for processing indicators, and the Supporting Data Queue for handling events related to control, risk, and issue updates and to collect the supporting data, have been introduced.

    Architecture for effective processing of indicators

    The execution of indicators has been optimized by implementing a three-step process, utilizing two custom queues for efficient processing.

    In the initial step, a set of indicators requiring processing is queued up in the Batch Indicator Data Queue. The Batch Indicator Data Processor then runs the indicators, sets the status, and creates the indicator results or tasks. The indicators are then moved to another custom queue for supporting data collection and updating dependent objects like controls, risks, and issues.

    In the second step, the Indicator Supporting Data Processor picks up the indicator records from the Indicator Supporting Data Queue and updates the control or risk status, also creating or updating issues based on the configuration. Then the indicator is pushed to the Indicator Statistics Table for support data collection.

    Finally, the Supporting Data Collection Job picks up the indicator records from the Supporting Data Statistics Table, starts collecting supporting data for the indicators, and leverages both the default queue and additional custom queues for efficient processing.

    Note:
    Indicator result business rules are not executed during the indicator job run. However, the same business rules are used while completing the indicator task. The indicator business rules are moved to the indicator job for achieving better performance during the execution.

    This architecture supports execution of a large number of indicators in significant time. The Supporting Data Collection Job introduced to collect supporting data improves the data handling process considerably. Moreover, the time taken to execute the data is considerably less.

    Note:
    The Supporting Data Collection Job assigns the batches of indicator records to the default queue initially for supporting data collection. Depending on the availability of the batch processing queue and supporting data queue, the scheduled job assigns the subsequent batches of indicator records to these queues in a round-robin fashion, that is, only if these queues are free to process them.

    Data model changes

    The Sample collection type field added in the Indicator template form, has options either Count or Percentage. The default value is Count. If you select Percentage, you can provide the percentage value to collect the supporting data.

    The Due date duration (days) field is added for manual indicator in the Indicator template and Indicator forms to capture the indicator task due date. Based on this due date, reminder emails are sent to the indicator task owners.

    A new field introduced in the indicator supporting data table, which is of type JSON, helps in efficient data management.