Never debug in production." It's one of the first rules every developer learns, right up there with "always backup your code" and "test everything twice." But after 15 years of building software, I've learned that sometimes—just sometimes—debugging in production isn't just acceptable, it's necessary.
Last month, I found myself SSH'd into a production server at 2 AM, frantically adding console.log statements to figure out why our payment processing was failing for exactly 23% of transactions. By all conventional wisdom, I was committing a cardinal sin. But that "sin" saved our company $50,000 in lost revenue and taught me that the real world of software development is messier and more nuanced than our textbooks suggest.
The Night Everything Went Wrong
The Setup: A Perfect Storm
It was a Tuesday evening when our monitoring alerts started screaming. Our ecommerce platform was processing payments normally for most customers, but roughly one in four transactions was failing with a cryptic error: "Payment processor timeout code 4021."
The Initial Response:
Checked all monitoring dashboards: ✅ All systems green
Reviewed recent deployments: ✅ Nothing deployed in 48 hours
Examined error logs: ❌ Minimal useful information
Contacted payment processor: 🤷♂️ "Everything looks normal on our end"
The Growing Crisis:
Revenue dropping by $2,000 per hour
Customer support tickets flooding in
Social media complaints starting to trend
CEO asking for hourly updates
The Dilemma:
Our staging environment couldn't replicate the issue. The error only occurred with real customer data, real payment methods, and real transaction volumes. We had two choices: spend hours trying to recreate the production environment locally, or debug directly in production.
The Decision to Break the Rules
At 1:47 AM, with revenue losses mounting and no clear path forward, I made the call that would have horrified my computer science professors: I was going into production to debug live.
The Justification:
The issue was already affecting customers—we weren't making it worse
Every hour of delay cost more than the risk of careful debugging
We had safeguards in place (feature flags, rollback capabilities, database backups)
The alternative was potentially days of investigation while customers suffered
The Safety Measures:
Before touching production, we established guardrails:
All changes would be logged and reversible
Only nondestructive debugging code (logging, monitoring)
Realtime monitoring of system performance
Immediate rollback plan if anything went wrong
Second engineer reviewing every change
The Hunt Begins: Debugging in the Wild
Adding Eyes to the Black Box
The first step was adding visibility to what was actually happening in production:
javascript
// Added strategic logging to the payment processing pipeline
app.post('/processpayment', async (req, res) => {
const startTime = Date.now();
console.log([DEBUG] Payment processing started for user ${req.user.id} at ${new Date().toISOString()});
try {
// Existing payment logic
const paymentResult = await processPayment(req.body);
console.log([DEBUG] Payment processing completed in ${Date.now() startTime}ms);
console.log([DEBUG] Payment result:, JSON.stringify(paymentResult, null, 2));
res.json(paymentResult);
} catch (error) {
console.log([DEBUG] Payment processing failed after ${Date.now() startTime}ms);
console.log([DEBUG] Error details:, error.message, error.stack);
console.log([DEBUG] Request payload:, JSON.stringify(req.body, null, 2));
res.status(500).json({ error: 'Payment processing failed' });
}
});
What We Discovered:
Within 10 minutes of adding logging, patterns emerged:
Failures were happening exactly 30 seconds after payment initiation
All failing transactions had customer IDs ending in specific digits
The error occurred during a database query, not the payment API call
Following the Breadcrumbs
The logs revealed that our payment processor wasn't the problem—our database was. But only for certain customers.
javascript
// Added database query logging
async function getUserPaymentMethods(userId) {
console.log([DEBUG] Fetching payment methods for user ${userId});
const startTime = Date.now();
try {
const query =
SELECT pm., cc.last_four, cc.expiry_date
FROM payment_methods pm
LEFT JOIN credit_cards cc ON pm.id = cc.payment_method_id
WHERE pm.user_id = ? AND pm.active = 1
;
const result = await db.query(query, [userId]);
console.log([DEBUG] Query completed in ${Date.now() startTime}ms, returned ${result.length} records);
return result;
} catch (error) {
console.log([DEBUG] Database query failed after ${Date.now() startTime}ms:, error.message);
throw error;
}
}
The Smoking Gun:
The logs showed that for certain users, the database query was taking exactly 30 seconds—hitting our timeout limit. But why only some users?
The Root Cause Revelation
More targeted logging revealed the issue:
javascript
// Added query plan analysis
async function getUserPaymentMethods(userId) {
console.log([DEBUG] Analyzing query for user ${userId});
// Check if this user ID triggers the slow path
const userIdStr = userId.toString();
const lastDigit = userIdStr[userIdStr.length 1];
console.log([DEBUG] User ID last digit: ${lastDigit});
// Add query execution plan logging
const explainQuery = EXPLAIN SELECT pm., cc.last_four, cc.expiry_date
FROM payment_methods pm
LEFT JOIN credit_cards cc ON pm.id = cc.payment_method_id
WHERE pm.user_id = ? AND pm.active = 1;
const queryPlan = await db.query(explainQuery, [userId]);
console.log([DEBUG] Query execution plan:, JSON.stringify(queryPlan, null, 2));
// Rest of the function...
}
The Discovery:
Users with IDs ending in certain digits were triggering a database bug where the query optimizer chose an inefficient execution plan. A recent database update had changed the optimizer's behavior, but only for specific data patterns.
The 23% Mystery Solved:
Our user ID generation algorithm created a nonuniform distribution—23% of user IDs ended in the digits that triggered the slow query path.
The Fix: Production Debugging Pays Off
The Immediate Solution
With the root cause identified, we could implement a targeted fix:
javascript
// Temporary workaround deployed to production
async function getUserPaymentMethods(userId) {
// Force the database to use the correct index
const query =
SELECT pm., cc.last_four, cc.expiry_date
FROM payment_methods pm USE INDEX (idx_user_id_active)
LEFT JOIN credit_cards cc ON pm.id = cc.payment_method_id
WHERE pm.user_id = ? AND pm.active = 1
;
return await db.query(query, [userId]);
}
The Results:
Payment failures dropped to 0% within 5 minutes
Average payment processing time improved by 40%
Customer complaints stopped immediately
Revenue recovery began instantly
The Proper LongTerm Fix
The next day, we implemented a comprehensive solution:
Updated database statistics to fix the query optimizer
Added proper database monitoring for slow queries
Implemented query performance alerts
Created automated tests for database performance regression
Total Time to Resolution:
Production debugging approach: 3 hours from problem to fix
Estimated traditional approach: 23 days to recreate the issue locally
Financial Impact:
Revenue saved: $50,000+ in prevented losses
Customer trust preserved: Zero churn from the incident
Engineering time saved: 40+ hours of investigation avoided
When Production Debugging Makes Sense
The Criteria for Breaking the Rules
Based on this experience and others, I've developed criteria for when production debugging is not just acceptable, but necessary:
The Issue Only Exists in Production
Real data volumes or patterns trigger the bug
Production environment complexity can't be replicated
Timing or concurrency issues that don't appear in stagingThe Cost of Delay Exceeds the Risk
Revenue impact is significant and growing
Customer experience is severely degraded
Security vulnerabilities are actively being exploited
Competitive advantage is at stakeYou Have Adequate Safeguards
Nondestructive debugging methods available
Rollback capabilities are tested and ready
Monitoring can detect if debugging causes issues
Team expertise to debug safelyTraditional Methods Have Failed
Staging environments can't reproduce the issue
Log analysis provides insufficient information
The problem is timesensitive and urgent
The Safe Production Debugging Playbook
Phase 1: Preparation
Ensure all changes are reversible
Set up enhanced monitoring
Prepare rollback procedures
Get stakeholder approval for the approach
Document everything in realtime
Phase 2: Minimal Invasive Debugging
Start with readonly operations (logging, monitoring)
Add observability without changing business logic
Use feature flags to control debugging code
Monitor system performance continuously
Phase 3: Hypothesis Testing
Form specific hypotheses based on initial data
Test hypotheses with minimal changes
Validate findings before implementing fixes
Document learnings for future reference
Phase 4: Careful Implementation
Implement fixes incrementally
Monitor impact at each step
Be ready to rollback immediately
Validate fix effectiveness with real data
The Lessons Learned
Production Debugging Best Practices
Observability is Your Best Friend
The ability to see what's happening in production is crucial. Invest in:
Comprehensive logging frameworks
Realtime monitoring and alerting
Distributed tracing for complex systems
Performance profiling toolsFeature Flags Enable Safe Experimentation
Feature flags allow you to:
Turn debugging code on and off instantly
Test fixes with a subset of traffic
Rollback changes without deployments
Experiment safely in productionDatabase Debugging Requires Extra Care
When debugging database issues in production:
Use readonly queries when possible
Monitor query performance impact
Have database rollback procedures ready
Consider read replicas for investigationCommunication is Critical
During production debugging:
Keep stakeholders informed of progress
Document decisions and rationale
Share findings with the team immediately
Conduct thorough postmortems
The Mindset Shift
From "Never Debug in Production" to "Debug Safely in Production When Necessary"
The absolute rule against production debugging assumes that:
You can always recreate production issues locally
The cost of delay is always less than the risk
Production environments are too dangerous to touch
There are no safe ways to debug live systems
The Reality:
Modern systems are too complex to fully replicate
Business impact often outweighs technical risk
Safe debugging practices can minimize danger
Sometimes production is the only place to find answers
The Tools That Make It Possible
Modern Debugging Infrastructure
Observability Platforms:
DataDog, New Relic, Honeycomb: Realtime system monitoring
Sentry, Rollbar: Error tracking and alerting
Jaeger, Zipkin: Distributed tracing for microservices
Grafana, Kibana: Log analysis and visualization
Safe Deployment Tools:
LaunchDarkly, Split.io: Feature flag management
Kubernetes, Docker: Containerized rollback capabilities
Bluegreen deployments: Zerodowntime deployment strategies
Canary releases: Gradual rollout with monitoring
Database Safety Tools:
Read replicas: Safe environments for query analysis
Query performance monitoring: Realtime slow query detection
Database migration tools: Safe schema changes
Pointintime recovery: Database rollback capabilities
The CounterArguments and Responses
"But What About Security?"
The Concern: Production debugging could expose sensitive data or create security vulnerabilities.
The Response:
Use proper access controls and audit logging
Sanitize sensitive data in debug output
Limit debugging access to essential personnel
Monitor all debugging activities
"What About Compliance?"
The Concern: Regulatory requirements might prohibit production changes.
The Response:
Many compliance frameworks allow emergency procedures
Document debugging activities for audit trails
Get preapproval for emergency debugging procedures
Ensure debugging doesn't violate data protection rules
"Isn't This Just Technical Debt?"
The Concern: Production debugging creates shortcuts that become permanent.
The Response:
Always follow up with proper fixes
Use debugging as investigation, not permanent solution
Document technical debt created and prioritize resolution
Learn from debugging to improve development processes
Building a Culture That Supports Safe Production Debugging
Organizational Changes
Update Your Incident Response Procedures
Include production debugging as an approved escalation path
Define criteria for when it's appropriate
Establish approval processes for emergency debugging
Create templates for documenting debugging activitiesInvest in Debugging Infrastructure
Build observability into all systems from the start
Implement feature flag systems across your platform
Create safe debugging environments and tools
Train teams on safe production debugging practicesChange the Conversation
Move from "never debug in production" to "debug safely when necessary"
Celebrate successful production debugging that saves the business
Learn from both successful and failed debugging attempts
Share knowledge and best practices across teams
Team Preparation
Skills Development:
Train engineers in safe production debugging techniques
Practice debugging scenarios in controlled environments
Develop expertise in observability and monitoring tools
Build confidence in rollback and recovery procedures
Process Development:
Create checklists for production debugging decisions
Establish communication protocols during incidents
Define roles and responsibilities for debugging teams
Develop postincident review processes
Conclusion: Embracing Pragmatic Engineering
The night I debugged our payment processing issue in production, I learned that software engineering isn't about following rules blindly—it's about making informed decisions that balance risk and reward. Sometimes the safest choice is the one that seems most dangerous.
The Key Insights:
Production debugging can be safe when done with proper precautions
The cost of delay often exceeds the risk of careful investigation
Modern tools make production debugging safer than ever before
Realworld problems sometimes require realworld solutions
The New Rule:
Instead of "never debug in production," perhaps we should teach "debug in production safely when the situation demands it."
The Bigger Picture:
This experience taught me that the best engineers aren't those who never break rules—they're those who know when and how to break them safely. In a world where software systems are increasingly complex and business requirements are increasingly urgent, the ability to debug safely in production isn't just a useful skill—it's a competitive advantage.
The next time you're facing a production issue that can't be reproduced elsewhere, don't automatically dismiss the idea of debugging in production. Instead, ask yourself: Do I have the tools, knowledge, and safeguards to do this safely? Can I afford not to?
Sometimes the most professional thing you can do is break the rules professionally.
Remember: The goal isn't to avoid all risk—it's to manage risk intelligently while delivering value to users and the business. Sometimes that means getting your hands dirty in production, and that's okay.
Top comments (1)
Is this really a lesson in debugging in production? Rather than just not having enough observability of your applications? If anything, I would argue that the main lesson to learn here would be to add more logs and traceability to your system for the long term...