log-analyzer

---

name: log-analyzer description: Expert guide for analyzing application logs including log searching, pattern detection, error tracking, and debugging. Use when investigating issues, tracking errors, or understanding application behavior. slug: log-analyzer category: operations complexity: complex version: "1.0.0" author: "id8Labs" triggers:

• "log-analyzer"
• "log analyzer" tags:
• development
• tool-factory-retrofitted---

Log Analyzer Skill

Core Workflows

Workflow 1: Primary Action

1. Analyze the input and context
2. Validate prerequisites are met
3. Execute the core operation
4. Verify the output meets expectations
5. Report results

Overview

This skill helps you effectively analyze application logs to diagnose issues, track errors, and understand system behavior. Covers log searching, pattern detection, structured logging, and integration with monitoring tools.

Log Analysis Philosophy

Principles

1. Structure over text: Structured logs are easier to analyze
2. Context matters: Include relevant metadata
3. Levels have meaning: Use appropriate severity levels
4. Correlation is key: Link related events across services

Log Levels

Level	When to Use	Example
ERROR	Something failed, needs attention	Database connection failed
WARN	Unexpected but handled	Retry succeeded after failure
INFO	Normal operation milestones	User signed up
DEBUG	Detailed diagnostic info	Query executed in 50ms
TRACE	Very detailed, usually disabled	Function entered/exited

Structured Logging

Winston Configuration (Node.js)

// src/lib/logger.ts
import winston from 'winston';

const logger = winston.createLogger({
  level: process.env.LOG_LEVEL || 'info',
  format: winston.format.combine(
    winston.format.timestamp(),
    winston.format.errors({ stack: true }),
    winston.format.json()
  ),
  defaultMeta: {
    service: 'my-app',
    environment: process.env.NODE_ENV,
  },
  transports: [
    // Console for development
    new winston.transports.Console({
      format: process.env.NODE_ENV === 'development'
        ? winston.format.combine(
            winston.format.colorize(),
            winston.format.simple()
          )
        : winston.format.json(),
    }),
  ],
});

// Add request context
export function createRequestLogger(requestId: string, userId?: string) {
  return logger.child({
    requestId,
    userId,
  });
}

export { logger };

Pino Logger (High Performance)

// src/lib/logger.ts
import pino from 'pino';

export const logger = pino({
  level: process.env.LOG_LEVEL || 'info',
  transport: process.env.NODE_ENV === 'development'
    ? {
        target: 'pino-pretty',
        options: {
          colorize: true,
          translateTime: 'SYS:standard',
        },
      }
    : undefined,
  base: {
    service: 'my-app',
    env: process.env.NODE_ENV,
  },
  redact: {
    paths: ['password', 'token', 'apiKey', '*.password', '*.token'],
    censor: '[REDACTED]',
  },
});

// Request-scoped logger
export function requestLogger(requestId: string, userId?: string) {
  return logger.child({ requestId, userId });
}

Logging Best Practices

// DO: Include context
logger.info('User signed up', {
  userId: user.id,
  email: user.email,
  plan: 'free',
  source: 'web',
});

// DO: Log errors with stack traces
logger.error('Payment failed', {
  error: err.message,
  stack: err.stack,
  userId: user.id,
  amount: 99.99,
  provider: 'stripe',
});

// DO: Use appropriate levels
logger.debug('Database query', {
  query: 'SELECT * FROM users WHERE id = ?',
  duration: 45,
  rows: 1,
});

// DON'T: Log sensitive data
// BAD: logger.info('Login', { password: user.password })

// DON'T: Use string concatenation
// BAD: logger.info('User ' + user.id + ' signed up')

Log Searching & Filtering

Command Line (grep, jq)

# Search JSON logs with jq
cat logs.json | jq 'select(.level == "error")'

# Search for specific user
cat logs.json | jq 'select(.userId == "usr_123")'

# Search by time range
cat logs.json | jq 'select(.timestamp >= "2024-01-15T10:00:00")'

# Count errors by type
cat logs.json | jq 'select(.level == "error") | .error.code' | sort | uniq -c

# Extract specific fields
cat logs.json | jq '{timestamp, level, message, userId}'

# Search text logs
grep -i "error" logs.txt
grep -E "error|warn" logs.txt
grep -A5 "ERROR" logs.txt  # 5 lines after match
grep -B3 "ERROR" logs.txt  # 3 lines before match

Vercel Log Analysis

# View live logs
vercel logs --follow

# Filter by level
vercel logs --level error

# Search specific timeframe
vercel logs --since 2h

# Filter by deployment
vercel logs --deployment-url https://myapp-abc123.vercel.app

# Output JSON for processing
vercel logs --output json | jq 'select(.level == "error")'

Supabase Log Analysis

-- Query Postgres logs
SELECT *
FROM postgres_logs
WHERE timestamp > now() - interval '1 hour'
  AND error_severity = 'ERROR'
ORDER BY timestamp DESC
LIMIT 100;

-- Query auth logs
SELECT *
FROM auth.audit_log_entries
WHERE timestamp > now() - interval '24 hours'
  AND payload->>'action' = 'login'
ORDER BY timestamp DESC;

-- Find slow queries
SELECT
  query,
  calls,
  mean_exec_time,
  total_exec_time
FROM pg_stat_statements
ORDER BY mean_exec_time DESC
LIMIT 20;

Pattern Detection

Common Error Patterns

// Error pattern analyzer
interface ErrorPattern {
  pattern: RegExp;
  category: string;
  severity: 'critical' | 'high' | 'medium' | 'low';
  suggestion: string;
}

const errorPatterns: ErrorPattern[] = [
  {
    pattern: /ECONNREFUSED|connection refused/i,
    category: 'connectivity',
    severity: 'critical',
    suggestion: 'Check if the target service is running and accessible',
  },
  {
    pattern: /timeout|ETIMEDOUT/i,
    category: 'timeout',
    severity: 'high',
    suggestion: 'Increase timeout or check for slow operations',
  },
  {
    pattern: /out of memory|heap|OOM/i,
    category: 'memory',
    severity: 'critical',
    suggestion: 'Increase memory allocation or fix memory leak',
  },
  {
    pattern: /rate.?limit|429|too many requests/i,
    category: 'rate-limiting',
    severity: 'medium',
    suggestion: 'Implement backoff strategy or increase rate limits',
  },
  {
    pattern: /unauthorized|401|invalid.?token/i,
    category: 'auth',
    severity: 'medium',
    suggestion: 'Check authentication credentials or token expiry',
  },
  {
    pattern: /not.?found|404/i,
    category: 'not-found',
    severity: 'low',
    suggestion: 'Verify resource exists or check URL',
  },
];

function categorizeError(message: string): ErrorPattern | null {
  for (const pattern of errorPatterns) {
    if (pattern.pattern.test(message)) {
      return pattern;
    }
  }
  return null;
}

Log Aggregation Script

// scripts/analyze-logs.ts
import * as readline from 'readline';
import * as fs from 'fs';

interface LogEntry {
  timestamp: string;
  level: string;
  message: string;
  error?: {
    message: string;
    code?: string;
  };
  [key: string]: any;
}

interface LogSummary {
  totalEntries: number;
  byLevel: Record<string, number>;
  errorMessages: Record<string, number>;
  timeRange: {
    start: string;
    end: string;
  };
}

async function analyzeLogs(filePath: string): Promise<LogSummary> {
  const summary: LogSummary = {
    totalEntries: 0,
    byLevel: {},
    errorMessages: {},
    timeRange: { start: '', end: '' },
  };

  const fileStream = fs.createReadStream(filePath);
  const rl = readline.createInterface({
    input: fileStream,
    crlfDelay: Infinity,
  });

  for await (const line of rl) {
    try {
      const entry: LogEntry = JSON.parse(line);
      summary.totalEntries++;

      // Count by level
      summary.byLevel[entry.level] = (summary.byLevel[entry.level] || 0) + 1;

      // Track error messages
      if (entry.level === 'error' && entry.error?.message) {
        const msg = entry.error.message.substring(0, 100);
        summary.errorMessages[msg] = (summary.errorMessages[msg] || 0) + 1;
      }

      // Track time range
      if (!summary.timeRange.start || entry.timestamp < summary.timeRange.start) {
        summary.timeRange.start = entry.timestamp;
      }
      if (!summary.timeRange.end || entry.timestamp > summary.timeRange.end) {
        summary.timeRange.end = entry.timestamp;
      }
    } catch {
      // Skip non-JSON lines
    }
  }

  return summary;
}

// Usage
analyzeLogs('logs.json').then(console.log);

Request Tracing

Request ID Middleware

// src/middleware.ts
import { NextResponse } from 'next/server';
import type { NextRequest } from 'next/server';
import { v4 as uuidv4 } from 'uuid';

export function middleware(request: NextRequest) {
  const requestId = request.headers.get('x-request-id') || uuidv4();

  const response = NextResponse.next();
  response.headers.set('x-request-id', requestId);

  return response;
}

Tracing Context

// src/lib/tracing.ts
import { AsyncLocalStorage } from 'async_hooks';

interface TraceContext {
  requestId: string;
  userId?: string;
  startTime: number;
}

const asyncLocalStorage = new AsyncLocalStorage<TraceContext>();

export function withTrace<T>(
  context: TraceContext,
  fn: () => T | Promise<T>
): T | Promise<T> {
  return asyncLocalStorage.run(context, fn);
}

export function getTrace(): TraceContext | undefined {
  return asyncLocalStorage.getStore();
}

// Usage in logger
export function log(level: string, message: string, data?: object) {
  const trace = getTrace();
  console.log(JSON.stringify({
    timestamp: new Date().toISOString(),
    level,
    message,
    requestId: trace?.requestId,
    userId: trace?.userId,
    duration: trace ? Date.now() - trace.startTime : undefined,
    ...data,
  }));
}

Error Tracking Integration

Sentry Integration

// src/lib/sentry.ts
import * as Sentry from '@sentry/nextjs';

Sentry.init({
  dsn: process.env.SENTRY_DSN,
  environment: process.env.NODE_ENV,
  tracesSampleRate: 0.1, // 10% of transactions
  beforeSend(event, hint) {
    // Filter out specific errors
    const error = hint.originalException;
    if (error instanceof Error && error.message.includes('Network')) {
      return null;
    }
    return event;
  },
});

// Capture with context
export function captureError(error: Error, context?: Record<string, any>) {
  Sentry.withScope((scope) => {
    if (context) {
      scope.setExtras(context);
    }
    Sentry.captureException(error);
  });
}

// Usage
try {
  await riskyOperation();
} catch (error) {
  captureError(error, {
    userId: user.id,
    action: 'payment',
    amount: 99.99,
  });
  throw error;
}

Custom Error Boundary Logging

// src/components/error-boundary.tsx
'use client';

import { useEffect } from 'react';
import * as Sentry from '@sentry/nextjs';

export default function Error({
  error,
  reset,
}: {
  error: Error & { digest?: string };
  reset: () => void;
}) {
  useEffect(() => {
    // Log to error tracking
    Sentry.captureException(error);

    // Log to console with context
    console.error('Client Error:', {
      message: error.message,
      digest: error.digest,
      stack: error.stack,
      url: window.location.href,
      userAgent: navigator.userAgent,
    });
  }, [error]);

  return (
    <div>
      <h2>Something went wrong!</h2>
      <button onClick={reset}>Try again</button>
    </div>
  );
}

Log Analysis Queries

Common Investigation Queries

# Find all errors for a specific user
jq 'select(.userId == "usr_123" and .level == "error")' logs.json

# Find errors in the last hour
jq --arg time "$(date -d '1 hour ago' -Iseconds)" \
  'select(.timestamp > $time and .level == "error")' logs.json

# Group errors by message
jq -s 'group_by(.error.message) |
  map({message: .[0].error.message, count: length}) |
  sort_by(-.count)' logs.json

# Find slow requests (>1 second)
jq 'select(.duration > 1000)' logs.json

# Trace a specific request
jq 'select(.requestId == "req_abc123")' logs.json | sort_by(.timestamp)

# Find patterns in error messages
jq -r 'select(.level == "error") | .error.message' logs.json | \
  sort | uniq -c | sort -rn | head -20

SQL-Style Log Analysis

-- Using tools like clickhouse or DuckDB for log analysis

-- Error rate by hour
SELECT
  date_trunc('hour', timestamp) as hour,
  count(*) FILTER (WHERE level = 'error') as errors,
  count(*) as total,
  round(100.0 * count(*) FILTER (WHERE level = 'error') / count(*), 2) as error_rate
FROM logs
WHERE timestamp > now() - interval '24 hours'
GROUP BY 1
ORDER BY 1;

-- Top error messages
SELECT
  error_message,
  count(*) as occurrences,
  min(timestamp) as first_seen,
  max(timestamp) as last_seen
FROM logs
WHERE level = 'error'
  AND timestamp > now() - interval '24 hours'
GROUP BY error_message
ORDER BY occurrences DESC
LIMIT 10;

-- Slowest endpoints
SELECT
  path,
  count(*) as requests,
  avg(duration) as avg_duration,
  max(duration) as max_duration,
  percentile_cont(0.95) WITHIN GROUP (ORDER BY duration) as p95
FROM logs
WHERE timestamp > now() - interval '1 hour'
GROUP BY path
ORDER BY avg_duration DESC
LIMIT 10;

Log Retention & Cleanup

Log Rotation Configuration

# /etc/logrotate.d/myapp
/var/log/myapp/*.log {
    daily
    rotate 14
    compress
    delaycompress
    missingok
    notifempty
    create 0640 www-data www-data
    sharedscripts
    postrotate
        systemctl reload myapp
    endscript
}

Automated Log Cleanup

// scripts/cleanup-logs.ts
import * as fs from 'fs';
import * as path from 'path';

const LOG_DIR = '/var/log/myapp';
const RETENTION_DAYS = 30;

function cleanupOldLogs() {
  const now = Date.now();
  const maxAge = RETENTION_DAYS * 24 * 60 * 60 * 1000;

  const files = fs.readdirSync(LOG_DIR);

  for (const file of files) {
    const filePath = path.join(LOG_DIR, file);
    const stats = fs.statSync(filePath);

    if (now - stats.mtime.getTime() > maxAge) {
      fs.unlinkSync(filePath);
      console.log(`Deleted: ${file}`);
    }
  }
}

cleanupOldLogs();

Debugging Checklist

When Investigating Issues

• Identify the time window
• Find related request IDs
• Check for error spikes
• Look at affected users/endpoints
• Check for pattern changes
• Review recent deployments

Log Quality Check

• Structured JSON format
• Appropriate log levels
• Request IDs included
• Sensitive data redacted
• Stack traces for errors
• Relevant context included

When to Use This Skill

Invoke this skill when:

• Investigating production issues
• Setting up logging infrastructure
• Debugging application errors
• Analyzing performance issues
• Creating log analysis scripts
• Setting up error tracking
• Implementing request tracing
• Cleaning up log data