optimizing-performance

Optimizing Performance

Strategies for identifying, analyzing, and resolving performance bottlenecks.

When to Use This Skill

• Application is running slowly
• High resource consumption (CPU, memory)
• Database queries are slow
• API response times are high
• Need to scale for more users
• Preparing for load testing

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Performance Optimization Philosophy

The Golden Rules

1. Measure first - Never optimize without data
2. Optimize the right thing - Find the actual bottleneck
3. Keep it simple - Complexity often hurts performance
4. Test after - Verify the optimization worked
5. Document trade-offs - Performance often costs readability

The 80/20 Rule

80% of performance problems come from 20% of the code.

Focus on:
├── Hot paths (frequently executed code)
├── I/O operations (database, network, disk)
├── Memory allocation patterns
└── Algorithm complexity

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Profiling Techniques

Types of Profiling

Type	What It Measures	Tools
CPU Profiling	Time spent in functions	pprof, py-spy, Chrome DevTools
Memory Profiling	Allocation patterns, leaks	Valgrind, memory_profiler, Chrome
I/O Profiling	Disk/network operations	strace, perf, Wireshark
Database Profiling	Query performance	EXPLAIN, slow query log, APM

Profiling Workflow

1. Establish baseline
   └─ Measure current performance with realistic load

2. Identify hotspots
   └─ Profile to find where time/resources are spent

3. Form hypothesis
   └─ Why is this slow? What would make it faster?

4. Implement fix
   └─ Make ONE change at a time

5. Measure again
   └─ Did it help? By how much?

6. Repeat
   └─ Until performance goals are met

Common Profiling Commands

# Node.js
node --prof app.js
node --prof-process isolate-*.log > profile.txt

# Python
python -m cProfile -s cumtime app.py
py-spy record -o profile.svg -- python app.py

# Go
go test -cpuprofile cpu.prof -memprofile mem.prof -bench .
go tool pprof cpu.prof

# Database (PostgreSQL)
EXPLAIN ANALYZE SELECT * FROM users WHERE email = '[email protected]';