The Datadog Grok Parser is a powerful tool for transforming unstructured log data into a structured format that can be easily analyzed and queried. Grok patterns allow you to define rules for extracting specific fields from log lines, making them searchable and actionable within your logging system. This online parser helps you test and refine your Grok patterns against sample log messages.

Grok Pattern Matching Examples

Example 1: Endpoint Availability Warning

This example demonstrates parsing a warning message indicating unavailable endpoints.

Message:

Endpoints not available for default/team-app-service-foobar

Pattern:

warning_endpoint_rule %{regex("[endpoints not available for a-zA-Z]*"):message_line}/%{regex("[a-zA-Z0-9-]*"):service}

Result:

{
  "message_line": "Endpoints not available for default",
  "service": "team-app-service-foobar"
}

Example 2: Task Execution Log

This example parses a detailed log message about a task's success, including timestamp, severity, and process information.

Message:

[2019-12-10 00:00:07,890: INFO/ForkPoolWorker-10] Task api.tasks.handle_job[000000a0-1a2a-12a3-4a56-d12dd3456789] succeeded in 0.02847545174881816s: None

Pattern:

my_rule \[%{date("yyyy-MM-dd HH:mm:ss,SSS"):timestamp}: %{word:severity}/%{regex("[a-zA-Z0-9-]*"):process}\] %{data:details}

Result:

{
  "timestamp": 1575982567890,
  "severity": "INFO",
  "process": "ForkPoolWorker-10",
  "details": "Task api.tasks.handle_job[000000a0-1a2a-12a3-4a56-d12dd3456789] succeeded in 0.02847545174881816s: None"
}

Example 3: Simplified Log Entry

This example shows a simpler pattern to extract the date and status from a log line.

Message:

2019-12-05 11:00:08,921 INFO module=trace, process_id=13, Task apps_dir.health.queue.tasks.add[000000a0-1a2a-12a3-4a56-d12dd3456789] succeeded in 0.0001603253185749054s: 8

Pattern:

my_rule .*%{date("yyyy-MM-dd HH:mm:ss,SSS"):date} %{word:status} .*

Result:

{
  "date": 1575982567890,
  "status": "INFO"
}

Understanding Grok Patterns

Grok uses a combination of predefined patterns and custom regular expressions to parse log data. Common predefined patterns include:

• %{WORD}: Matches a single word.
• %{NUMBER}: Matches an integer or floating-point number.
• %{TIMESTAMP_ISO8601}: Matches ISO8601 formatted timestamps.
• %{IP}: Matches an IP address.
• %{GREEDYDATA}: Matches everything until the end of the line.

You can also define custom patterns using regular expressions and assign names to the extracted fields, like %{regex("your_regex"):field_name}. This allows for highly flexible log parsing tailored to your specific needs.

Benefits of Using a Grok Parser

Leveraging a Grok parser offers significant advantages for log management and analysis:

• Structured Data: Converts messy, unstructured logs into organized key-value pairs.
• Enhanced Searchability: Enables precise searching and filtering of log data based on extracted fields.
• Improved Monitoring: Facilitates the creation of dashboards and alerts based on specific log events.
• Efficient Analysis: Simplifies the process of identifying trends, errors, and anomalies in your system logs.

For more information on Grok patterns and their usage, refer to the official Logstash Grok documentation.