Log Level Token Normalizer

Description

Normalize a log-level token through explicit caller aliases with cycle/path evidence; no universal severity vocabulary.

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.

Normalize a log-level token through explicit caller aliases with cycle/path evidence; no universal severity vocabulary.

When to use this tool

Use this tool to normalize a log-level token through explicit caller aliases with cycle/path evidence; no universal severity vocabulary.

How to use the tool and how it works

Enter level, aliasesJson using the field definitions below. For JSON text, serialize the explicit records in the shown shape.
Run the operation. Correct input errors before interpreting an output.
Read result.normalized, result.cycle, result.path. Use the ordering, boundary rules and absence conditions described below.

Follow exact alias name-to-target records from level, returning the visited path and terminal label, or null terminal with cycle true.^[1]

Input field	Meaning and units	Accepted input
level	Exact level label to resolve through the supplied aliases.	Text, at most 8,192 UTF-16 code units; the operation’s grammar also applies.
aliasesJson	JSON array of {name, target} string records. Alias names must be distinct; matching is exact and case-sensitive. Cycles are reported rather than resolved to an invented terminal label.	Text, at most 8,192 UTF-16 code units; the operation’s grammar also applies.

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

json

{
  "aliasesJson": [
    {
      "name": "warn",
      "target": "warning"
    }
  ]
}

Output field	Interpretation
result	Object containing the named fields below.
result.normalized	Terminal alias label, or null when a cycle is encountered.
result.cycle	Whether alias traversal encountered a repeated label.
result.path	Visited alias labels in traversal order.

json

{
  "input": {
    "level": "warn",
    "aliasesJson": "[{\"name\":\"warn\",\"target\":\"warning\"}]"
  },
  "output": {
    "result": {
      "normalized": "warning",
      "cycle": false,
      "path": [
        "warn",
        "warning"
      ]
    }
  }
}

Limitations and assumptions

There is no built-in level vocabulary, case normalization or automatic cycle repair.
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

Tool behavior and example: Log Level Token Normalizer — tool.io

Similar or alternative tools

Log Level Order Selector
Select supplied severity tokens at or above an explicit least-to-most ordered level bank; retain unknown tokens instead of assuming rank.
Log Prefix Token Extractor
Extract every caller-defined literal log message prefix and remainder with original prefix indexes; no inferred log format or priority interpretation.