Performance problems are sneaky. They don't announce themselves like crashes or errors. They creep in gradually - a query gets a little slower, a bundle gets a little larger, memory usage grows a little higher. Each individual change seems harmless. Then one day your application is noticeably slow and nobody knows exactly when it happened or which change caused it.
By the time users complain about performance, you're playing detective across months of commits. The fix might be easy once found, but finding it is the hard part.
The solution is catching performance regressions before they ship. When every PR is checked against performance baselines, slowdowns are identified while the change is still fresh in the developer's mind.
Why Performance Degrades Gradually
Understanding how regression happens helps prevent it.
Death by a Thousand Cuts
No single PR makes performance terrible. Instead, each PR adds a small overhead:
- A new dependency adds 10KB
- A database query adds 50ms
- A component adds 5ms render time
Individually, each is acceptable. Collectively, they compound into slowness.
Shifting Baselines
When performance degrades slowly, the new performance becomes normal. Developers don't notice because they experienced the gradual change. New team members don't notice because they never knew it was faster.
Premature Optimization Avoidance
Teams rightfully avoid premature optimization. But the pendulum swings too far when teams also avoid all performance consideration. Not optimizing prematurely is different from not measuring at all.
Testing Environment Differences
Local development environments are fast. CI environments are fast. Production, with real data volumes and concurrent users, is slower. Performance problems hide until production.
Feature Pressure
When features and performance compete for attention, features win. Performance work is always deferrable - until it isn't.
Types of Performance to Monitor
Response Time
How long operations take:
@devonair monitor API endpoint response times and detect slowdowns
@devonair alert if average response time increases by more than 10%
Memory Usage
How much memory code consumes:
@devonair track memory usage patterns and detect leaks
@devonair alert if memory usage increases significantly after changes
Bundle Size
How large client-side bundles are:
@devonair track bundle size and alert on increases above threshold
@devonair identify which changes increased bundle size
Database Query Performance
How efficiently queries execute:
@devonair monitor query execution time and detect slow queries
@devonair alert on N+1 queries introduced by changes
Render Performance
How quickly UI renders:
@devonair monitor component render times and detect regressions
@devonair alert if render time exceeds target for critical components
Throughput
How many operations per second:
@devonair track operations per second and detect throughput degradation
Establishing Baselines
You need to know what's normal before detecting abnormal.
Current State Baseline
@devonair establish performance baseline from current main branch
Document what performance looks like today.
Historical Trending
@devonair track performance metrics over time to establish trends
Understand how performance has changed.
Component Baselines
@devonair establish render time baselines for critical components
Each component has its own acceptable performance.
Endpoint Baselines
@devonair establish response time baselines for each API endpoint
Different endpoints have different acceptable latencies.
CI Integration
Catch regressions in the pipeline.
PR-Level Checks
@devonair on PR: run performance benchmarks and compare to baseline
@devonair on PR: fail if response time increases more than 20%
Build Size Checks
@devonair on PR: report bundle size changes
@devonair on PR: block merge if bundle increases more than 50KB
Memory Checks
@devonair on PR: run memory profiling and detect increases
Query Checks
@devonair on PR: analyze query patterns for performance issues
Benchmark Implementation
API Benchmarks
@devonair create performance benchmarks for all API endpoints
@devonair run benchmarks against a consistent dataset
Component Benchmarks
@devonair create render benchmarks for React components
@devonair benchmark component with various prop combinations
Algorithm Benchmarks
@devonair create benchmarks for computationally intensive functions
@devonair compare algorithm performance with different input sizes
Load Testing
@devonair run load tests and compare to previous results
@devonair detect performance under concurrent load
Detecting Specific Regressions
Bundle Size Regression
@devonair analyze bundle changes and identify what added size
Know exactly which code increased your bundle.
Memory Leak Detection
@devonair run memory leak detection on long-running tests
@devonair identify objects that grow without bound
Query Regression
@devonair compare query execution plans before and after change
@devonair detect new queries that lack indexes
Render Regression
@devonair profile component renders and compare to baseline
@devonair detect unnecessary re-renders introduced by changes
Network Regression
@devonair track network request count and payload size
@devonair alert on increased API calls from components
Alerting and Reporting
Threshold-Based Alerts
@devonair alert when performance degrades beyond threshold
@devonair configure different thresholds for different metrics
Trend-Based Alerts
@devonair alert when metrics show consistent degradation trend
Catch slow drifts that stay under absolute thresholds.
Comparative Reports
@devonair generate performance comparison report between releases
@devonair report on performance changes in this sprint
Regression Attribution
@devonair identify which commit introduced performance regression
Know exactly what caused the problem.
Fixing Detected Regressions
Root Cause Analysis
@devonair analyze performance regression and suggest fixes
@devonair profile the slow code path and identify bottlenecks
Automated Fixes
@devonair optimize detected N+1 queries
@devonair add missing database indexes for slow queries
Reversion Recommendation
@devonair when regression detected: recommend reverting if no quick fix
Sometimes the right fix is undo.
Frontend Performance
Core Web Vitals
@devonair monitor Largest Contentful Paint (LCP) regression
@devonair detect Cumulative Layout Shift (CLS) increases
@devonair alert on First Input Delay (FID) degradation
JavaScript Performance
@devonair detect main thread blocking introduced by changes
@devonair identify long tasks added by new code
Image Optimization
@devonair detect unoptimized images added to the project
@devonair alert on images without responsive variants
CSS Performance
@devonair detect CSS selector complexity increases
@devonair identify render-blocking CSS
Backend Performance
Database Performance
@devonair monitor query execution times after deployment
@devonair detect queries that scan more rows than necessary
Caching Effectiveness
@devonair monitor cache hit rates and detect degradation
@devonair alert if cache misses increase significantly
Resource Usage
@devonair monitor CPU and memory usage after deployment
@devonair detect resource usage increases that correlate with code changes
External Service Calls
@devonair monitor external API call latency
@devonair detect increased external call frequency
Performance Budgets
Set limits and enforce them.
Bundle Budgets
@devonair enforce bundle size budget of 250KB for main bundle
@devonair set per-route bundle budgets
Response Time Budgets
@devonair enforce 200ms response time budget for list APIs
@devonair set different budgets for different endpoint types
Memory Budgets
@devonair enforce memory budget for long-running processes
Render Budgets
@devonair enforce 16ms render budget for interactive components
Preventing Regressions
Beyond detection, prevent them.
Code Review Focus
@devonair flag performance-sensitive changes for extra review
@devonair annotate PRs with potential performance impact
Performance Guidelines
@devonair verify new code follows performance best practices
@devonair flag common performance anti-patterns
Dependency Analysis
@devonair analyze performance impact of new dependencies
@devonair warn when adding large dependencies
Getting Started
Establish baselines first:
@devonair create performance baseline for current application state
Add CI checks:
@devonair on PR: compare performance against baseline and report changes
Set budgets:
@devonair configure performance budgets for bundle size and response time
Enforce over time:
@devonair schedule weekly: report on performance trends
Performance regression detection turns a vague "the app feels slow" into specific, actionable "PR #1234 increased endpoint latency by 40%." When you know exactly what caused slowness, you can fix it immediately - or revert and rethink.
FAQ
What performance thresholds should I use?
Start with relative thresholds: alert if metrics change by more than 10-20%. Then add absolute thresholds based on your requirements: API latency under 200ms, bundle under 300KB, etc.
Won't performance tests add too much CI time?
Run fast checks on every PR (bundle size, basic benchmarks). Run extensive performance tests on merge to main or nightly. Balance coverage with speed.
What about performance that varies by environment?
Performance baselines should come from consistent environments. Track production performance separately from CI performance - they're both important but not directly comparable.
How do I handle legitimate performance tradeoffs?
Sometimes features require performance tradeoffs. When this happens, document it, update baselines explicitly, and ensure the tradeoff is intentional rather than accidental.