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Node.js Bunyan Structured JSON Logger Parser

Parse Node.js Bunyan JSON logs with numeric severity levels, PIDs, hostnames, and event records. Test pattern matching, inspect named capture groups, and export production-ready parser definitions across Fluent Bit, Vector VRL, Datadog Pipelines, Logstash, and OpenTelemetry.

Live Interactive Debugger & Generator

Matches execute locally in-browser via Web Worker
Interactive Test & Config Generator Sandbox
Extraction Pattern (Grok / PCRE Expression)
Pattern Valid (6 fields)
Detected Fields:namehostnamepid:integerlevel:integermsgtime
Raw Log Stream Sandbox(0/3 matched)
No log lines provided. Paste lines or select a preset above.
No matching lines available to generate JSON output.
Parsed 0/3 lines0 ms (0 μs)
ReDoS Risk: SAFE
AdvertisementActive Viewability 30s

Log Architecture & Structural Overview

The Node.js Bunyan Structured JSON Logger Parser is an essential telemetry stream within the Application Frameworks & Runtimes ecosystem. Parse Node.js Bunyan JSON logs with numeric severity levels, PIDs, hostnames, and event records.

This schema defines a structure of 6 extracted attributes, including 2 numeric metrics and 4 string dimensions. In production observability architectures, these tokens provide high-cardinality indexing keys for telemetry pipelines before shipping to storage backends such as ClickHouse, Elasticsearch, Amazon S3, or Datadog.

Raw Telemetry Ingestion Profile

A typical raw event line for node-bunyan-json averages 144 bytes across 6 tokens. Modern collectors such as Fluent Bit and Vector require zero-backtracking regular expressions to avoid CPU spikes during traffic surges.

Extracted Field Schema & Data Types

The transpiled Grok pattern extracts the following schema fields from each raw event line. Data collectors cast these values according to the typed mappings below.

Field NameInferred TypeDescription & Collector Semantics
namestringApplication name.
hostnamestringHost system name.
pidintegerNode.js worker process ID.
levelintegerBunyan numeric level (10=trace, 20=debug, 30=info, 40=warn, 50=error, 60=fatal).
msgstringMain log message.
timestringISO-8601 UTC timestamp.

Common Regex Traps & Production Edge Cases

Engineers frequently encounter ingestion failures or pipeline drops due to subtle variations in real-world event logs. Watch out for these verified pitfalls:

1Bunyan records level as an integer (e.g. 30 for INFO). Map numeric levels to standard strings in downstream collectors.
2The 'err' object is serialized as a nested JSON structure when exceptions occur.

Production Collector Setup & Configurations

Pre-configured parser definitions ready to be dropped into your infrastructure repository.

Fluent Bit (parsers.conf)

Format: regex
# ==============================================================================
# Fluent Bit Parser Configuration (parsers.conf)
# ==============================================================================
[PARSER]
    Name        logpipeline_parser
    Format      regex
    Regex       ^\{"name":"(?<name>\S+)","hostname":"(?<hostname>\b(?:[0-9A-Za-z][0-9A-Za-z-]{0,62})(?:\.(?:[0-9A-Za-z][0-9A-Za-z-]{0,62}))*(?:\.?|\b))","pid":(?<pid>(?:[+-]?(?:[0-9]+))),"level":(?<level>(?:[+-]?(?:[0-9]+))),"msg":"(?<msg>.*?)","time":"(?<time>(?:\b[0-9]{4}\b)-(?:(?:0?[1-9]|1[0-2]))-(?:(?:(?:0[1-9])|(?:[12][0-9])|(?:3[01])|[1-9]))[T ](?:(?:2[0123]|[01]?[0-9])):?(?:(?:[0-5][0-9]))(?::?(?:(?:(?:[0-5]?[0-9]|60)(?:[:.,][0-9]+)?)))?(?:(?:Z|[+-](?:(?:2[0123]|[01]?[0-9]))(?::?(?:(?:[0-5][0-9])))))?)"\}$
    Time_Key    time
    Time_Format %Y-%m-%dT%H:%M:%S%z
    Types       pid:integer level:integer

# ==============================================================================
# Fluent Bit Pipeline Filter (fluent-bit.conf)
# ==============================================================================
[FILTER]
    Name         parser
    Match        *
    Key_Name     log
    Parser       logpipeline_parser
    Reserve_Data On

Vector.dev (Remap VRL)

parse_regex!
# ==============================================================================
# Vector.dev Remap Language (VRL) Transform
# Use inside a 'remap' transform in vector.yaml
# ==============================================================================
.parsed, err = parse_regex(.message, r'^\{"name":"(?<name>\S+)","hostname":"(?<hostname>\b(?:[0-9A-Za-z][0-9A-Za-z-]{0,62})(?:\.(?:[0-9A-Za-z][0-9A-Za-z-]{0,62}))*(?:\.?|\b))","pid":(?<pid>(?:[+-]?(?:[0-9]+))),"level":(?<level>(?:[+-]?(?:[0-9]+))),"msg":"(?<msg>.*?)","time":"(?<time>(?:\b[0-9]{4}\b)-(?:(?:0?[1-9]|1[0-2]))-(?:(?:(?:0[1-9])|(?:[12][0-9])|(?:3[01])|[1-9]))[T ](?:(?:2[0123]|[01]?[0-9])):?(?:(?:[0-5][0-9]))(?::?(?:(?:(?:[0-5]?[0-9]|60)(?:[:.,][0-9]+)?)))?(?:(?:Z|[+-](?:(?:2[0123]|[01]?[0-9]))(?::?(?:(?:[0-5][0-9])))))?)"\}$')

if err == null {
    . = merge(., .parsed)
    del(.parsed)

    # Type coercions
    .pid = to_int!(.pid)
    .level = to_int!(.level)

} else {
    log("LogPipeline parsing warning: " + err, level: "warn")
}

# ==============================================================================
# vector.yaml Pipeline Component
# ==============================================================================
transforms:
  parse_logs:
    type: remap
    inputs: ["source_logs"]
    source: |
      .parsed, err = parse_regex(.message, r'^\{"name":"(?<name>\S+)","hostname":"(?<hostname>\b(?:[0-9A-Za-z][0-9A-Za-z-]{0,62})(?:\.(?:[0-9A-Za-z][0-9A-Za-z-]{0,62}))*(?:\.?|\b))","pid":(?<pid>(?:[+-]?(?:[0-9]+))),"level":(?<level>(?:[+-]?(?:[0-9]+))),"msg":"(?<msg>.*?)","time":"(?<time>(?:\b[0-9]{4}\b)-(?:(?:0?[1-9]|1[0-2]))-(?:(?:(?:0[1-9])|(?:[12][0-9])|(?:3[01])|[1-9]))[T ](?:(?:2[0123]|[01]?[0-9])):?(?:(?:[0-5][0-9]))(?::?(?:(?:(?:[0-5]?[0-9]|60)(?:[:.,][0-9]+)?)))?(?:(?:Z|[+-](?:(?:2[0123]|[01]?[0-9]))(?::?(?:(?:[0-5][0-9])))))?)"\}$')
      if err == null {
        . = merge(., .parsed)
        del(.parsed)
      }

Datadog Log Pipeline Grok Parser

match_rules
# ==============================================================================
# Datadog Log Processing Pipeline Grok Parser
# Navigate to: Logs -> Configuration -> Pipelines -> Add Processor -> Grok Parser
# ==============================================================================

# Match Rule:
rule \{"name":"%{NOTSPACE:name}","hostname":"%{HOSTNAME:hostname}","pid":%{INT:pid},"level":%{INT:level},"msg":"%{DATA:msg}","time":"%{TIMESTAMP_ISO8601:time}"\}

# Complete Datadog Pipeline Processor JSON:
{
  "type": "grok-parser",
  "name": "LogPipeline Grok Parser",
  "is_enabled": true,
  "source": "message",
  "samples": [],
  "grok": {
    "match_rules": "rule \\{\"name\":\"%{NOTSPACE:name}\",\"hostname\":\"%{HOSTNAME:hostname}\",\"pid\":%{INT:pid},\"level\":%{INT:level},\"msg\":\"%{DATA:msg}\",\"time\":\"%{TIMESTAMP_ISO8601:time}\"\\}",
    "support_rules": ""
  }
}

# Target Fields Created:
# name (string), hostname (string), pid (integer), level (integer), msg (string), time (string)

OpenTelemetry Collector (transform processor)

regex_parser
# ==============================================================================
# OpenTelemetry Collector Configuration (otel-collector-config.yaml)
# Option 1: Filelog Receiver with regex_parser Operator
# ==============================================================================
receivers:
  filelog:
    include: [ /var/log/**/*.log ]
    start_at: beginning
    operators:
      - type: regex_parser
        id: logpipeline_regex_parser
        regex: '^\{"name":"(?<name>\S+)","hostname":"(?<hostname>\b(?:[0-9A-Za-z][0-9A-Za-z-]{0,62})(?:\.(?:[0-9A-Za-z][0-9A-Za-z-]{0,62}))*(?:\.?|\b))","pid":(?<pid>(?:[+-]?(?:[0-9]+))),"level":(?<level>(?:[+-]?(?:[0-9]+))),"msg":"(?<msg>.*?)","time":"(?<time>(?:\b[0-9]{4}\b)-(?:(?:0?[1-9]|1[0-2]))-(?:(?:(?:0[1-9])|(?:[12][0-9])|(?:3[01])|[1-9]))[T ](?:(?:2[0123]|[01]?[0-9])):?(?:(?:[0-5][0-9]))(?::?(?:(?:(?:[0-5]?[0-9]|60)(?:[:.,][0-9]+)?)))?(?:(?:Z|[+-](?:(?:2[0123]|[01]?[0-9]))(?::?(?:(?:[0-5][0-9])))))?)"\}$'
        timestamp:
          parse_from: attributes.time
          layout: '%Y-%m-%dT%H:%M:%S%z'

# ==============================================================================
# Option 2: Transform Processor (OTel Transformation Language - OTTL)
# ==============================================================================
processors:
  transform:
    error_mode: ignore
    log_statements:
      - context: log
        statements:
          - merge_maps(attributes, extract_patterns(body, "^\\{\"name\":\"(?<name>\\S+)\",\"hostname\":\"(?<hostname>\\b(?:[0-9A-Za-z][0-9A-Za-z-]{0,62})(?:\\.(?:[0-9A-Za-z][0-9A-Za-z-]{0,62}))*(?:\\.?|\\b))\",\"pid\":(?<pid>(?:[+-]?(?:[0-9]+))),\"level\":(?<level>(?:[+-]?(?:[0-9]+))),\"msg\":\"(?<msg>.*?)\",\"time\":\"(?<time>(?:\\b[0-9]{4}\\b)-(?:(?:0?[1-9]|1[0-2]))-(?:(?:(?:0[1-9])|(?:[12][0-9])|(?:3[01])|[1-9]))[T ](?:(?:2[0123]|[01]?[0-9])):?(?:(?:[0-5][0-9]))(?::?(?:(?:(?:[0-5]?[0-9]|60)(?:[:.,][0-9]+)?)))?(?:(?:Z|[+-](?:(?:2[0123]|[01]?[0-9]))(?::?(?:(?:[0-5][0-9])))))?)\"\\}$"), "insert")

service:
  pipelines:
    logs:
      receivers: [filelog]
      processors: [transform]
      exporters: [otlp]

Logstash Filter Configuration

filter.grok
# ==============================================================================
# Logstash Pipeline Configuration (/etc/logstash/conf.d/logpipeline.conf)
# ==============================================================================
filter {
  grok {
    match => { "message" => "\{\"name\":\"%{NOTSPACE:name}\",\"hostname\":\"%{HOSTNAME:hostname}\",\"pid\":%{INT:pid:integer},\"level\":%{INT:level:integer},\"msg\":\"%{DATA:msg}\",\"time\":\"%{TIMESTAMP_ISO8601:time}\"\}" }
    tag_on_failure => [ "_grokparsefailure" ]
  }

  date {
    match => [ "time", "ISO8601", "dd/MMM/yyyy:HH:mm:ss Z" ]
    target => "@timestamp"
    remove_field => [ "time" ]
  }
}