Debugging Production Issues: A Real-World Case Study

3:47 AM. The on-call phone rings. Your heart sinks. A critical production issue is affecting customers, and you're the one who needs to fix it.

This is the story of how we used LogzAI to diagnose and resolve a complex production issue in under 30 minutes—what traditionally would have taken hours.

The Incident

Time: 3:47 AM PST Impact: 15% of API requests returning 500 errors Severity: Critical Customer Impact: High

Initial Symptoms

Our monitoring showed:

• Sudden spike in 500 errors
• Response times increased from 150ms to 8+ seconds
• Database connections maxed out
• Memory usage climbing steadily

Traditional Debugging Approach

Normally, this investigation would involve:

1. SSH into multiple servers
2. Grep through gigabytes of logs
3. Correlate timestamps across services
4. Query databases manually
5. Check system metrics individually

Estimated time: 2-4 hours

The LogzAI Approach

Instead, we opened LogzAI and asked:

"What changed in the last hour that could cause 500 errors?"

AI Analysis (30 seconds)

LogzAI immediately identified:

Critical Insight:
Database connection pool exhaustion detected at 03:42 AM

Related Events:
- Deployment: auth-service v2.1.3 at 03:40 AM
- New query pattern: SELECT * FROM users WHERE...
- Connection leak: 15 unclosed connections per minute

Root Cause Probability: 94%

The Investigation

Step 1: Verify the Hypothesis

We asked LogzAI to show us the connection lifecycle:

1logzai query "Show connection open/close events since 03:40"

Result: Clear pattern of connections opening but not closing after the deployment.

Step 2: Identify the Code Change

LogzAI correlated the deployment with recent code changes:

1// Problematic code introduced in v2.1.3
2async function getUserPermissions(userId) {
3  const connection = await db.getConnection()
4  const permissions = await connection.query(
5    'SELECT * FROM permissions WHERE user_id = ?',
6    [userId]
7  )
8  return permissions // ❌ Connection never released!
9}

Step 3: Immediate Mitigation

While preparing a fix, we asked LogzAI:

"How can I quickly reduce the impact without a full rollback?"

AI Suggestion:

1# Increase connection pool temporarily
2kubectl set env deployment/auth-service \
3  DB_POOL_SIZE=100 \
4  DB_POOL_TIMEOUT=30000

Impact: Error rate dropped from 15% to 2% within 2 minutes.

Step 4: The Fix

We quickly patched the code:

1// Fixed code
2async function getUserPermissions(userId) {
3  const connection = await db.getConnection()
4  try {
5    const permissions = await connection.query(
6      'SELECT * FROM permissions WHERE user_id = ?',
7      [userId]
8    )
9    return permissions
10  } finally {
11    connection.release() // ✅ Always release the connection
12  }
13}

Step 5: Deploy and Verify

1# Deploy the fix
2git commit -m "fix: release database connections in getUserPermissions"
3git push origin hotfix/connection-leak
4
5# Verify with LogzAI
6logzai query "Monitor connection pool usage for the next 10 minutes"

Results

Total Resolution Time: 28 minutes

Timeline breakdown:

• 3:47 AM: Alert received
• 3:48 AM: Logged into LogzAI
• 3:49 AM: Root cause identified
• 3:52 AM: Mitigation deployed
• 4:02 AM: Fix deployed
• 4:15 AM: Incident resolved

Impact Comparison

| Metric | Traditional Approach | LogzAI Approach | |--------|---------------------|----------------| | Time to Root Cause | 60-120 min | 2 min | | Time to Mitigation | 90-150 min | 5 min | | Total Resolution | 120-240 min | 28 min | | Customer Impact | High | Minimal |

Key Takeaways

1. AI-Powered Correlation

LogzAI connected:

• Deployment events
• Code changes
• Database metrics
• Error patterns

This correlation would have taken hours manually.

2. Natural Language Queries

No need to remember complex query syntax or know which logs to check. Just ask questions in plain English.

3. Proactive Suggestions

LogzAI didn't just identify the problem—it suggested both immediate mitigation and long-term fixes.

4. Context Preservation

All analysis and queries are saved, making post-incident reviews effortless.

Lessons Learned

For Your Team

1. Deploy LogzAI before you need it: Don't wait for an incident to set up proper logging
2. Practice during calm times: Familiarize yourself with AI queries when there's no pressure
3. Create runbooks: Document common issues and how LogzAI helped resolve them
4. Automate mitigation: Use LogzAI's insights to create automated responses

Code Changes Made

Beyond the fix, we implemented:

1// Added connection monitoring
2class DatabaseConnection {
3  constructor(connection) {
4    this.connection = connection
5    this.acquired = Date.now()
6    this.released = null
7    this.stackTrace = new Error().stack
8  }
9
10  release() {
11    this.released = Date.now()
12    logger.info({
13      event: 'connection_released',
14      duration: this.released - this.acquired,
15      stackTrace: this.stackTrace
16    })
17    this.connection.release()
18  }
19}

Conclusion

Modern production environments are too complex for traditional debugging approaches. AI-powered log analysis isn't just a nice-to-have—it's becoming essential for maintaining reliable systems.

The difference between a 28-minute incident and a 4-hour outage can be transformative for both your business and your on-call engineer's sleep schedule.

Ready to transform your incident response?

Start your free trial of LogzAI today and experience the difference AI-powered log analysis can make when it matters most.