Key Facts
- Category
- Data Processing
- Input Types
- textarea, select, text
- Output Type
- json
- Sample Coverage
- 4
- API Ready
- Yes
Overview
This tool parses Apache and Nginx access logs using regex patterns to extract structured data. It supports common log formats and custom patterns, outputting results in JSON, table, or CSV for easy analysis.
When to Use
- •When you need to analyze web server access logs for traffic patterns or errors.
- •When extracting specific fields like IP addresses, request methods, or status codes from unstructured logs.
- •When converting log data into a structured format like JSON or CSV for reporting or further processing.
How It Works
- •Paste your Apache or Nginx access log content into the input field.
- •Select the log format from predefined options or provide a custom regex pattern.
- •Choose the output format (JSON, table, or CSV) to view the parsed data.
- •The tool applies the regex pattern to extract fields and displays the structured results.
Use Cases
Web server log analysis for monitoring website traffic and identifying popular pages.
Security auditing by extracting and analyzing access patterns to detect suspicious activity.
Performance optimization by parsing request times and HTTP status codes to troubleshoot issues.
Examples
1. Parse Apache Combined Logs to JSON
- Background
- A web administrator has an Apache access log file in combined format with entries for IP, timestamp, request, status, and user agent.
- Problem
- Need to extract key fields like client IP, request URL, and HTTP status code for traffic analysis.
- How to Use
- Paste the log content, select 'Combined Log Format', and choose JSON as the output format.
- Outcome
- Structured JSON data with fields such as client IP, request method, URL, status code, and user agent for easy querying.
2. Custom Regex for Nginx Logs
DevOps Engineer- Background
- An engineer uses a custom Nginx log format that includes additional fields not covered by standard patterns.
- Problem
- Standard log formats don't match; need to parse logs with a specific regex to extract custom data.
- How to Use
- Select 'Custom Regex Pattern', enter a regex like '^([\d.]+) - - \[([^\]]+)\] "([A-Z]+) ([^"]+) HTTP/[^"]+" (\d+) (\d+)', and parse the logs.
- Example Config
-
^([\d.]+) - - \[([^\]]+)\] "([A-Z]+) ([^"]+) HTTP/[^"]+" (\d+) (\d+) - Outcome
- Extracted data in table format showing IP, timestamp, method, path, status, and size for detailed log analysis.
Try with Samples
regexNginx Log Parser Samples
Comprehensive Nginx log samples for testing log-parser tool, including access logs and error logs with various formats and scenarios
title token log,parser,nginx
Regex Named Capture Groups
Collection of regex patterns using named capture groups for extracting structured data from text. Named groups make patterns more readable and maintainable by assigning meaningful names to captured portions.
preferred input family regex
Web String Processing Python Samples
Web Python string processing examples including string split/join, regular expressions, and string replacement
preferred input family regex
Web String Processing Go Samples
Web Go string processing examples including string splitting/joining, regular expressions, and string replacement
preferred input family regex
FAQ
What log formats are supported?
Common Log Format (CLF), Combined Log Format, Nginx Default Format, and custom regex patterns.
Can I use my own regex pattern?
Yes, select 'Custom Regex Pattern' and enter a regex with capture groups to define the fields.
What output formats are available?
JSON, table, and CSV formats for flexible data handling.
Is there a limit on log size?
The tool processes pasted content directly; for very large logs, consider splitting the input.
How do I ensure accurate parsing?
Use the correct log format selection or define a precise custom regex that matches your log structure.