Exploring MetricBase

  • Release version: Yokohama
  • Updated January 30, 2025
  • 2 minutes to read
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    Summary of Exploring MetricBase

    MetricBase enables ServiceNow customers to efficiently collect, retain, analyze, and act on large volumes of time-series data, particularly machine-generated data. It stores summarized time-series data in a dedicated MetricBase database, facilitating integration with IoT-based applications and enabling data-driven automation and insights.

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    Key Features

    • Data Collection and Storage: Administrators define metrics and sampling intervals to collect time-series data, which is then stored in MetricBase until its configured expiration.
    • Integration and Automation: MetricBase supports triggering Workflow Studio flows based on time-series data conditions such as CPU usage thresholds, data gaps, or memory usage forecasts.
    • Visualization: Time-series data can be visualized using native time-series charts and Reporting applications to monitor trends and anomalies.
    • Anomaly Detection: Administrators can configure and train predictive machine learning models within MetricBase to detect anomalies and automatically trigger workflows when unusual data patterns occur.
    • APIs for Data Interaction: Data insertion and retrieval are supported through REST and JavaScript APIs, enabling seamless integration with other systems and custom applications.

    Practical Use and Benefits

    • Efficient Data Handling: By storing summarized data rather than raw large datasets, MetricBase optimizes storage and performance for time-series data analysis.
    • Proactive Monitoring and Alerts: Automated triggers based on time-series data enable proactive incident management, alerting, and workflow execution.
    • Improved Decision-Making: Visualization and anomaly detection empower administrators to make informed decisions based on real-time and historical data trends.
    • Extensive User Roles: Primarily suited for administrators who manage data definitions, triggers, and predictive models, ensuring robust control over data workflows.

    Next Steps for ServiceNow Customers

    To effectively implement and leverage MetricBase capabilities, customers should explore documentation on configuring MetricBase, defining and collecting data, triggering flows based on MetricBase data, and managing MetricBase environments.

    Collect, retain, analyze, and act on time-series data using MetricBase.

    MetricBase overview

    MetricBase helps you work with large amounts of data by using a smaller summary of that data that is stored in the MetricBase database.

    • Integrate MetricBase seamlessly with ServiceNow IoT-based applications that monitor or act on large amounts of machine-generated data.
    • Trigger flows in Workflow Studio based on time-series data in MetricBase.
      • Generate an email if the average CPU usage is more than 85% in the last 5 minutes.
      • Generate an email if MetricBase detects a gap in data submitted for 10 minutes or more.
      • Generate an alert if the average of the collected data is less than 10 or greater than 500 in the last 5 minutes.
      • Generate an alert if memory usage is likely to exceed 90% in the next 10 minutes.
    • Visualize MetricBase data using time-series charts.
    • Use the Reporting application to graph the time-series data that is stored in MetricBase.
    • Detect anomalies by training a machine language model and execute a Workflow Studio trigger when an anomaly is detected.
    MetricBase works with:
    • An instance that stores machine-generated data
    • A server that has the MetricBase application and database

    MetricBase users

    Table 1. Users
    User Description
    Administrator An administrator collects, retains, analyzes, and acts on time-series data using MetricBase.

    MetricBase workflow

    The following figure shows that machine-generated data is sampled every 4 seconds. You send the average of the values in each sampling period to the MetricBase database, which stores the data until its expiration date.

    Figure 1. Storing time series data
    Infographic showing how machine-generated data is sampled at regular intervals and sent to the MetricBase database by the API. For details, refer to the following description.
    1. The administrator specifies a metric to store and how often to collect it by creating a time-series definition in MetricBase.
    2. The administrator sends data from the instance to the MetricBase server using the MetricBase REST or JavaScript APIs.
    3. The administrator configures trigger definitions that execute flows based on time-series data in MetricBase.
    4. The administrator configures and trains predictive models in MetricBase to detect anomalies and execute flows when new data is significantly different than the trained data.
    5. The administrator monitors collected data using time-series charts in MetricBase. Time-series data remains in the MetricBase database for a prescribed amount of time, after which MetricBase deletes the data.

    MetricBase benefits

    Table 2. MetricBase benefits
    Benefit Feature Users
    Store time series summary of a large collection of data Create a time-series definition in MetricBase Administrator
    Insert and retrieve time-series data from the MetricBase database Developer resources Administrator
    Access and visualize time-series data in the MetricBase database Accessing MetricBase data Administrator
    Trigger flows when new data is significantly different than the trained data Detecting anomalies in MetricBase data using predictive models Administrator
    Trigger flows that can log incidents, send emails, and create other alerts Triggering flows using MetricBase data Administrator

    What to explore next

    To learn more about configuring and using MetricBase, see: