Overview

Application logs contain a wealth of runtime information, but manual analysis is time-consuming and labor-intensive. Claude can help you quickly parse logs, extract key metrics, identify error patterns, and generate visual reports.

Use Cases

Troubleshoot production environment errors
Analyze API request performance
Monitor system resource usage
Track user behavior paths
Generate operations reports

Steps

Step 1: Identify Log Format

First understand the structure and format of the logs.

Please analyze the ~/logs/app.log file:
- Identify log format (JSON, plain text, or other)
- Extract first 20 lines as samples
- Identify fields: timestamp, log level, message, source, etc.
- File size and line count
- Time span covered

Step 2: Error Statistics

Extract and count all error messages.

Please analyze errors in the logs:
- Count ERROR and FATAL level log entries
- Group by error type
- List the top 10 most frequent errors
- Show first and last occurrence time for each error
- Extract complete error stack traces

Step 3: Performance Analysis

Analyze API or feature performance metrics.

Extract performance data from logs:
- Identify log lines containing response times
- Calculate average, maximum, and minimum response times
- Group statistics by API endpoint or feature
- Identify slow requests with response time over 1 second
- Draw time series chart (if possible)
- Save performance report to ~/logs/performance_report.txt

Step 4: Time Pattern Analysis

Analyze time patterns of when issues occur.

Analyze time patterns in logs:
- Count log volume and error rate by hour
- Identify peak hours
- Check for periodic issues (e.g., errors at certain times each day)
- Compare weekday vs weekend differences
- Display results in table or chart format

Step 5: Generate Summary Report

Create a readable analysis report.

Based on the above analysis, generate a Markdown format log analysis report:
# Log Analysis Report - 2025-01-12

## Overview
- Analysis time range
- Total log entries
- Error rate

## Key Findings
- Top 3 critical issues
- Performance bottlenecks
- Anomaly patterns

## Detailed Statistics
- Error distribution table
- Performance metrics
- Time distribution chart

## Recommendations
- Issues requiring priority attention

Save as ~/logs/analysis_report.md

Warning: Large log files (several GB) may cause slow processing or memory issues. It's recommended to filter or process in batches, analyzing only critical time periods.

Tip: For production environments, you can create scheduled tasks to analyze the latest logs every hour, automatically generate reports and send alert emails for proactive monitoring.

FAQ

Q: Log file too large to load at once? A: Claude can use streaming processing or read only specific time ranges. You can also use grep to filter error logs first, then analyze in detail.

Q: How to handle multi-line error stack traces? A: Tell Claude the multi-line rules for logs (e.g., stack traces start with tab or specific markers), and it will merge related lines into complete error records.

Q: Can multiple log files be analyzed? A: Yes. Claude can merge and analyze multiple log files, or analyze each file separately and generate comparison reports.