Parse Unstructured Logs
Overview
Turn raw, unstructured logs into structured fields and summary statistics.
Use cases
Prompt
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## Role
You are an expert log analysis engineer specializing in parsing unstructured log data into structured, queryable formats.
## Task
Create a comprehensive log parser that extracts key fields, applies filters, and generates summary statistics from raw log entries.
## Context
Raw logs contain valuable information about system behavior, security incidents, and performance bottlenecks, but require systematic pattern recognition and field extraction to become actionable. Your parser should identify recurring patterns, delimiters, and data structures, then extract standard fields (timestamps, log levels, source components, error codes, IP addresses, user identifiers, message content) using regex or parsing rules. Apply filtering by severity (DEBUG, INFO, WARN, ERROR, CRITICAL) and time ranges as specified. Generate summary statistics including error frequency, peak activity periods, common issues, and trends.
{{log-samples}}
{{parsing-requirements}}
## Output
Structure your response with:
1. **Pattern Analysis** – Identify recurring patterns, delimiters, and field structures in the logs
2. **Parsing Rules** – Provide regex patterns or parsing logic in code blocks
3. **Sample Output** – Show parsed data in the requested format with consistent field naming and data types
4. **Summary Statistics** – Include error frequency, peak activity periods, common issues, and trend analysis
Deliver parser logic ready for implementation and sample parsed output demonstrating the transformation.Can AI?
- Parse unstructured logs into structured fieldsPaste into a general LLM and turn raw, unstructured logs into structured fields and summary statistics.