Performance enhancements for Indicator nightly job

  • Release version: Zurich
  • Updated July 31, 2025
  • 2 minutes to read
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    Summary of Performance enhancements for Indicator nightly job

    The Zurich release introduces significant performance improvements for the Indicator nightly job in ServiceNow Governance, Risk, and Compliance (GRC). These enhancements enable efficient, parallel processing of a large volume of indicators through a redesigned architecture that uses multiple custom queues and a streamlined three-step processing approach.

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    Key Features

    • New Custom Queues: Two additional queues—the Indicator Data Queue and the Supporting Data Queue—are introduced to separate processing of indicator results and related supporting data such as control, risk, and issue updates.
    • Three-Step Processing Architecture:
      • Step 1: Indicators are queued in the Batch Indicator Data Queue. The Batch Indicator Data Processor runs the indicators, sets their status, and creates results or tasks.
      • Step 2: The Indicator Supporting Data Processor updates related records (controls, risks, issues) based on indicator output and moves indicators to the Indicator Statistics Table.
      • Step 3: The Supporting Data Collection Job retrieves indicators from the statistics table and collects additional supporting data using a combination of default and custom queues for optimized throughput.
    • Improved Business Rule Execution: Indicator result business rules are not run during the nightly job but are executed when completing indicator tasks, which enhances performance.
    • Load Balancing: The Supporting Data Collection Job assigns indicator batches to queues in a round-robin manner only if the queues are available, ensuring efficient resource use without overloading queues.
    • Data Model Enhancements:
      • New Sample collection type field on Indicator templates allows choosing between Count or Percentage for supporting data collection, with Count as default.
      • Due date duration (days) field added for manual indicators to manage task due dates and trigger reminder emails.
      • A JSON-type field added to the indicator supporting data table for better data management.

    Key Outcomes

    • Significantly faster execution of large numbers of indicators due to parallel processing and optimized job architecture.
    • Improved efficiency in collecting and updating supporting data related to indicators, controls, risks, and issues.
    • Better resource utilization through intelligent queue management and batch processing.
    • Enhanced flexibility in configuring data collection parameters and task management within indicators.

    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.

    New 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.
    Figure 1. Processing indicator jobs using additional queues
    Workflow illustrating the processing of indicator jobs using additional queues

    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.