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Log Record Time Gap Buckets

Bucket signed adjacent log timestamp gaps by caller-supplied strictly increasing thresholds; negative clock jumps remain signed observations.

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Description

Bucket signed adjacent log timestamp gaps by caller-supplied strictly increasing thresholds; negative clock jumps remain signed observations.

Structured log records need an explicit ordering, time unit and label convention. A sequence gap can be measured in supplied record order without proving loss, and grouping adjacent messages differs from grouping all equal messages. The record tools here operate on caller-supplied structured data rather than collecting logs.

Bucket signed adjacent log timestamp gaps by caller-supplied strictly increasing thresholds; negative clock jumps remain signed observations.

When to use this tool

Use this tool to bucket signed adjacent log timestamp gaps by caller-supplied strictly increasing thresholds; negative clock jumps remain signed observations.

How to use the tool and how it works

  1. Enter recordsJson, edges using the field definitions below. For JSON text, serialize the explicit records in the shown shape.
  2. Run the operation. Correct input errors before interpreting an output.
  3. Read result.gaps. Use the ordering, boundary rules and absence conditions described below.

Subtract adjacent supplied record times and assign bucket equal to the number of strictly increasing edges not greater than the gap.1

Input fieldMeaning and unitsAccepted input
recordsJsonJSON array of {id, time, source, level, message, sequence} records. Time and gap units are caller-defined; sequence is a nonnegative integer.Text, at most 8,192 UTF-16 code units; the operation’s grammar also applies.
edgesStrictly increasing numeric gap edges in the same units as record time.Dense list of at most 128 finite numbers; integer/shape constraints above also apply.

These are the decoded structures used in the example’s JSON text fields. Supply the corresponding serialized JSON strings as input.

json
{
  "recordsJson": [
    {
      "id": "a",
      "time": 1,
      "source": "s",
      "level": "info",
      "message": "x",
      "sequence": 1
    },
    {
      "id": "b",
      "time": 3,
      "source": "s",
      "level": "info",
      "message": "x",
      "sequence": 3
    }
  ]
}
Output fieldInterpretation
resultObject containing the named fields below.
result.gapsAdjacent input time gaps with later record index and numeric bucket.
json
{
  "input": {
    "recordsJson": "[{\"id\": \"a\", \"time\": 1, \"source\": \"s\", \"level\": \"info\", \"message\": \"x\", \"sequence\": 1}, {\"id\": \"b\", \"time\": 3, \"source\": \"s\", \"level\": \"info\", \"message\": \"x\", \"sequence\": 3}]",
    "edges": [
      0,
      1,
      5
    ]
  },
  "output": {
    "result": {
      "gaps": [
        {
          "index": 1,
          "gap": 2,
          "bucket": 2
        }
      ]
    }
  }
}

Limitations and assumptions

  • Input order is preserved and negative gaps are possible; time units are caller-defined and must match edges.
  • Structured log records have id, time, source, level, message and sequence fields. time is a finite caller-defined numeric scale; sequence is an integer from 0 through 1,000,000,000,000. Use the same time units for windows and gap edges.
  • Every declared top-level input field is required and unexpected fields are rejected. Ordinary strings are limited to 8,192 UTF-16 code units and lists to 128 entries, with tighter operation-specific bounds where stated. JSON text has bounded nesting and node counts.
  • Numeric outputs must be finite and no larger in absolute magnitude than the safe-integer limit; operations returning exact large integers use decimal text. Positions are zero-based and spans end exclusively unless the operation explicitly states a different convention.

Alternative or Complementary methods

Keep original record order and provenance alongside summaries. Sort, partition by source or normalize aliases explicitly before applying a rule that depends on those choices.

References

  1. Tool behavior and example: Log Record Time Gap Buckets — tool.io

Similar or alternative tools

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  • Log Level Token Normalizer

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