For the complete documentation index, see llms.txt. This page is also available as Markdown.

Examples

Examples of log patterns identified and optimized by Nimbus.

Logs with common message patterns

These are high volume log events that repeat most of their content. For most applications most of the time, this will be the primary driver of log volume. Examples include health checks and heart beat notifications.

[
  {
    "ddsource": "nodejs",
    "host": "itemrefresh-0",
    "message": "refresh item catalogue for itemId: ITEM470",
    "path": "/",
    "service": "itemrefresh",
    "status": "info",
    "timestamp": "2023-11-23T00:16:09.970Z"
  },
  {
    "ddsource": "nodejs",
    "host": "itemrefresh-0",
    "message": "refresh item catalogue for itemId: ITEM8185",
    "path": "/",
    "service": "itemrefresh",
    "status": "info",
    "timestamp": "2023-11-23T00:16:09.997Z"
  },
  {
    "ddsource": "nodejs",
    "host": "itemrefresh-0",
    "message": "refresh item catalogue for itemId: ITEM7594",
    "path": "/",
    "service": "itemrefresh",
    "status": "info",
    "timestamp": "2023-11-23T00:16:10.010Z"
  },
  // 37 more messages
  ...
]
  • 97.5% event volume reduction

  • 79% ingest volume reduction

Logs with common identifiers

These are logs that describe a sequence of related events. These sequences usually have some sort of common identifier like a transactionId or a jobId. Examples include a background job and business specific user flows.

  • 75% reduction in event volume

  • 4% reduction in ingest volume

Multi-Line Logs

Many times, an application will emit a single log across multiple lines such as the case with a JSON log. Unless you specifically account for this, most logging agents will consume each newline as a separate log event. Nimbus can identify when this happens and stitch these logs back together.

  • 90% in event volume reduction

  • 87% in ingest volume reduction

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