Performance Metrics
Learn to measure what matters. Master the Four Golden Signals of monitoring and understand how top tech companies track system performance and user experience.
The Four Golden Signals (Google SRE)
Google's Site Reliability Engineering team identified four key metrics that provide comprehensive insight into system health. These signals form the foundation of effective monitoring.
Why These Four?
They cover the complete user experience: how fast (latency), how much load (traffic), how often it breaks (errors), and when it might break (saturation). Together, they predict both current user experience and future system stability.
Latency
Time taken to serve a request
Traffic
Amount of demand on your system
Errors
Rate of failed requests
Saturation
How "full" your service is
Real-World Performance Benchmarks
Performance standards from companies serving billions of users. These benchmarks represent world-class user experiences and are targets worth aspiring to.
Key Insight
Notice how these companies set aggressive performance targets. They understand that every millisecond matters for user experience and business outcomes. Speed is a competitive advantage.
Understanding Percentiles
Averages can be misleading. A few slow requests can hide widespread performance issues. Percentiles tell the complete story of user experience.
Why Percentiles Matter
Common Percentiles
P50 (Median)
Represents the typical user experience. Good for general performance tracking.
P95
Primary SLA metric. Balances user experience with engineering practicality.
P99
Catches edge cases and system stress. Critical for high-scale applications.
Performance Metrics Quick Reference
Essential Metrics
- ☐ P95 response time < 200ms
- ☐ Error rate < 0.1%
- ☐ Availability > 99.9%
- ☐ Apdex score > 0.85
- ☐ CPU utilization < 70%
Monitoring Tools
- • Prometheus + Grafana
- • New Relic, Datadog (SaaS)
- • CloudWatch (AWS)
- • Application Performance Monitoring (APM)
- • Synthetic monitoring / uptime checks
🧮 SLA Performance Calculator
Calculate the impact of different performance targets on user experience and costs
Inputs
Result
99.9% availability means 8.76 hours downtime per year
🎯 Performance Metrics in Action
Real-world examples of how performance metrics drive business decisions
Metrics
Outcome
Performance optimization became top priority. CDN implementation and database optimization reduced load times to under 1 second, recovering conversion rates.
Lessons Learned
- Every 100ms delay costs 1% in conversions for e-commerce
- Peak traffic periods expose hidden performance bottlenecks
- Error rate above 1% indicates system stress requiring immediate attention
- Performance monitoring should trigger automatic alerts before user impact
ScenariosClick to explore
Context
Major retailer sees 40% drop in conversions during peak shopping season
Metrics
Outcome
Performance optimization became top priority. CDN implementation and database optimization reduced load times to under 1 second, recovering conversion rates.
Key Lessons
- •Every 100ms delay costs 1% in conversions for e-commerce
- •Peak traffic periods expose hidden performance bottlenecks
- •Error rate above 1% indicates system stress requiring immediate attention
- •Performance monitoring should trigger automatic alerts before user impact
1. E-commerce Site Performance Crisis
Context
Major retailer sees 40% drop in conversions during peak shopping season
Metrics
Outcome
Performance optimization became top priority. CDN implementation and database optimization reduced load times to under 1 second, recovering conversion rates.
Key Lessons
- •Every 100ms delay costs 1% in conversions for e-commerce
- •Peak traffic periods expose hidden performance bottlenecks
- •Error rate above 1% indicates system stress requiring immediate attention
- •Performance monitoring should trigger automatic alerts before user impact
2. Streaming Service Buffer Rate Analysis
Context
Video platform reduces churn by optimizing startup time and buffering
Metrics
Outcome
Aggressive content pre-positioning and adaptive bitrate algorithms kept users watching. Startup time under 2 seconds became competitive advantage.
Key Lessons
- •Video startup time over 2 seconds causes 6% of viewers to abandon
- •Buffer events have 3x higher impact on user satisfaction than startup delay
- •Geographic content distribution reduces latency for global audiences
- •Real-time quality adaptation prevents buffer events during network congestion
3. API Gateway Saturation Event
Context
Microservices platform experiences cascading failures due to ignored saturation metrics
Metrics
Outcome
Resource saturation at API gateway caused widespread timeouts. Horizontal scaling and circuit breakers prevented future incidents.
Key Lessons
- •Saturation metrics predict failures before they impact users
- •CPU utilization over 80% requires immediate scaling action
- •Circuit breakers prevent cascade failures in microservice architectures
- •Load testing should validate performance under sustained high utilization
4. Mobile App Performance Optimization
Context
Social media app improves user engagement through latency optimization
Metrics
Outcome
Feed pre-loading and image optimization delivered sub-second load times. User engagement metrics improved across all demographics.
Key Lessons
- •Mobile networks amplify latency issues compared to desktop
- •Image optimization has outsized impact on mobile performance
- •Feed pre-loading during app startup improves perceived performance
- •Performance improvements directly correlate with user engagement
5. Database Performance Degradation
Context
SaaS platform detects and resolves gradual performance decline
Metrics
Outcome
Proactive monitoring caught slowly degrading query performance. Index optimization and query refactoring restored normal operation.
Key Lessons
- •Performance degradation often happens gradually and goes unnoticed
- •Database index maintenance is critical for sustained performance
- •Query performance monitoring should track trends, not just absolute values
- •Automated performance regression detection prevents user impact
6. Real-time Chat System Scaling
Context
Messaging platform maintains low latency while scaling to millions of users
Metrics
Outcome
Regional message routing and connection pooling enabled massive scale while maintaining chat-quality latency requirements.
Key Lessons
- •Real-time applications require consistent latency, not just low average latency
- •WebSocket connection management critical for chat application performance
- •Message routing optimization reduces cross-region latency
- •Connection pooling and load balancing essential for million-user scale