Source type structure adjustment in Health Log Analytics

  • Release version: Zurich
  • Updated July 31, 2025
  • 2 minutes to read
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    Summary of Source Type Structure Adjustment in Health Log Analytics

    Health Log Analytics (HLA) allows you to reclassify auto-classified properties and change auto-mapped labels to enhance machine learning’s understanding of your log priorities. This process is crucial for ensuring that the HLA engine accurately analyzes and processes log fields.

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    Key Features

    • Automatic Processing: HLA separates transport headers from inner log messages, automatically extracting properties and mapping labels to source type fields.
    • Classification Types: Classifications include METER, GAUGE, ARCONLY, HISTOGRAM, and INVALID, which determine how log fields are analyzed.
    • Label Types: Labels such as MESSAGE, HOST, TIMESTAMP, SEVERITY, and EVENT-ID indicate the role of each field in the log structure.
    • AI-Powered Suggestions: Access AI suggestions for classifications and labels based on dynamic log analysis, simplifying the adjustment process.
    • Custom JavaScript Modifications: Modify extracted properties using JavaScript for more accurate log representation and reclassification.
    • Relearn Functionality: Reactivate learning mode to refresh parsing and classification when the initial learning period is outdated.

    Key Outcomes

    By refining the source type structure, you can enhance how HLA reads log messages, detect anomalies effectively, and ensure accurate data processing. This leads to improved log analysis and better insights into your system’s performance. For detailed adjustments, you may refer to the related knowledge base article.

    Health Log Analytics enables you to reclassify auto-classified properties and change auto-mapped labels. These adjustments help Health Log Analytics machine learning to better understand your priorities.

    What HLA does automatically

    HLA automatically separates the transport header from the inner log message and sends only the inner log message to the source type structure. The system extracts properties from incoming log messages and auto-maps labels to source type fields.
    • Classification determines how the HLA engine analyzes and processes each field. Available classifications are: METER, GAUGE, ARC_ONLY, HISTOGRAM, INVALID.
    • Labels tell HLA what role a field plays in the log structure. Available labels are: MESSAGE, HOST, TIMESTAMP, SEVERITY, EVENT-ID.

    Reviewing and, if necessary, modifying automatically mapped labels and auto-classified properties in the source type structure verifies the HLA engine interprets log fields correctly.

    Note:
    Because a single data input can contain multiple source types, HLA structures log data by source type rather than by data input.

    AI-powered classification and labeling suggestions

    Simplify classification and labeling of parsed log properties with AI-powered automatic classification and labeling suggestions. You can access the AI agent from the Source Type Structure page to receive AI-suggested classifications and labels based on dynamic analysis of your log samples. AI-suggested classifications include METER, GAUGE, ARC_ONLY, and INVALID. AI-suggested labels include MESSAGE, HOST, TIMESTAMP, and SEVERITY. After reviewing the suggestions, select your preferred classifications and labels.

    Example

    The following example shows how to modify values of extracted properties in the source type structure with JavaScript.

    Consider the following log:

    {
      "TenantId": "abc-01-02-03-04-05050708091011121314",
      "@timestamp": "2020-08-28T08:29:23.967Z",
      "Computer": "john Doe_computer",
      "EventType_s": "LogMessage",
      "Job_s": "johnDoe_cell",
      "IP_s": "1.00.00.00",
      "message": "This is the extracted message. This part of the message includes superfluous content and values",
      "MessageType_s": "OUT",
      "Timestamp_d": 1598603359017850000,
      "Type": "my_LogMessage_is",
      "_ResourceId": ""
    }

    The sample code contains "key":"value" pairs: The key is the property name. The value is the property value.

    The key "message" in the sample has the following value: "This is the extracted message. This part of the message includes superfluous content and values".

    If you wanted your logs to contain only the meaningful part of that message, you would add JavaScript code instructing the system to extract only that part:

    //Added JavaScript to extract only the first sentence in the message! if (output['message'] != null){ output['message'] = output['message'].slice(0, output['message'].indexOf("\.")); } (edited) 

    You could use the same logic to reclassify a value. For example, if the key "Computer" is insignificant, you could set its value to "Invalid."

    For more information about the source type structure, see the Source Type Structure – Labels & Classifications [KB0863562] article in the Now Support Knowledge Base.

    Note:
    You can reactivate the learning mode for a source type structure to use the HLA engine's learning functionality again after the initial learning period becomes outdated. On the Source Type Structure form, select Relearn to reset parsing, extraction, classification, and labeling for the source type structure and restart the learning process.