Performance Profiling

Performance Profiling Skill

Find the bottleneck before optimizing. Measure twice, optimize once.

The Golden Rule

"Premature optimization is the root of all evil" — Donald Knuth

But also:

"Measure, don't guess" — Everyone who's optimized the wrong thing

Performance Investigation Flow

┌──────────────────────────────────────────────────────────┐
│  1. OBSERVE                                              │
│     User reports slowness → Reproduce → Measure baseline │
├──────────────────────────────────────────────────────────┤
│  2. IDENTIFY                                             │
│     Profile → Find hotspots → Determine bottleneck type  │
├──────────────────────────────────────────────────────────┤
│  3. HYPOTHESIZE                                          │
│     Why is this slow? → Form theory → Plan fix           │
├──────────────────────────────────────────────────────────┤
│  4. OPTIMIZE                                             │
│     Implement fix → Measure improvement → Verify         │
├──────────────────────────────────────────────────────────┤
│  5. MONITOR                                              │
│     Add metrics → Set alerts → Prevent regression        │
└──────────────────────────────────────────────────────────┘

Bottleneck Types

CPU Profiling

Node.js

# Built-in profiler
node --prof app.js
node --prof-process isolate-*.log > processed.txt

# Chrome DevTools
node --inspect app.js
# Open chrome://inspect

# clinic.js (recommended)
npm install -g clinic
clinic doctor -- node app.js
clinic flame -- node app.js

Reading Flame Graphs

┌─────────────────────────────────────────────────────────┐
│                        main()                            │
│  ┌─────────────────────────┐  ┌────────────────────────┐│
│  │      processData()      │  │     renderUI()         ││
│  │  ┌──────────┐  ┌──────┐ │  │  ┌──────┐  ┌────────┐  ││
│  │  │ parseJSON││ │sort()│ │  │  │layout││ │ paint()│  ││
│  │  └──────────┘  └──────┘ │  │  └──────┘  └────────┘  ││
│  └─────────────────────────┘  └────────────────────────┘│
└─────────────────────────────────────────────────────────┘
             Width = Time spent

• Wide bars = slow functions (targets for optimization)
• Deep stacks = look for unnecessary recursion
• Flat tops = time spent in that function, not children

.NET

# dotnet-trace
dotnet trace collect --process-id <PID> --format speedscope

# dotnet-counters (quick overview)
dotnet counters monitor --process-id <PID>

# Visual Studio Profiler
# Debug → Performance Profiler → CPU Usage

Memory Profiling

Node.js Heap Analysis

// Take heap snapshot programmatically
const v8 = require('v8');
const fs = require('fs');

const snapshotPath = `heap-${Date.now()}.heapsnapshot`;
const snapshot = v8.writeHeapSnapshot(snapshotPath);
console.log(`Heap snapshot written to ${snapshot}`);

// Memory usage check
console.log(process.memoryUsage());
// { rss, heapTotal, heapUsed, external, arrayBuffers }

Common Memory Leaks

.NET Memory

# Heap dump
dotnet-dump collect -p <PID>
dotnet-dump analyze <dump-file>

# GC stats
dotnet-counters monitor --counters System.Runtime

Network Profiling

Browser DevTools

1. Network tab → Record
2. Look for:
   • Waterfall — Blocked/waiting time
   • Size — Large payloads
   • Time — Slow responses

API Latency Breakdown

Total Request Time: 500ms
├── DNS Lookup: 20ms
├── TCP Connection: 30ms
├── TLS Handshake: 50ms (HTTPS)
├── Time to First Byte: 350ms  ← Server processing
└── Content Download: 50ms

Common Network Issues

Database Profiling

Query Analysis

-- PostgreSQL: Enable slow query logging
ALTER SYSTEM SET log_min_duration_statement = 100;  -- Log queries > 100ms

-- MySQL: Enable slow query log
SET GLOBAL slow_query_log = 'ON';
SET GLOBAL long_query_time = 0.1;

-- SQL Server
-- Query Store or Extended Events

Query Plan Analysis

-- PostgreSQL
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT * FROM orders WHERE customer_id = 123;

-- Look for:
-- - Seq Scan on large tables (need index?)
-- - High "actual rows" vs "plan rows" (stats out of date?)
-- - Nested loops with high iterations (join order?)

Connection Pool Issues

VS Code Extension Profiling

For the AI assistant-like extensions:

Activation Performance

// Measure activation time
export async function activate(context: vscode.ExtensionContext) {
    const start = performance.now();
    
    // ... initialization ...
    
    console.log(`Extension activated in ${performance.now() - start}ms`);
    
    // Report to telemetry
    telemetry.logUsage('activation', { durationMs: performance.now() - start });
}

Command Performance

// Wrap commands with timing
function withTiming<T>(
    commandId: string,
    handler: () => Promise<T>
): () => Promise<T> {
    return async () => {
        const start = performance.now();
        try {
            return await handler();
        } finally {
            const duration = performance.now() - start;
            if (duration > 1000) {
                console.warn(`Slow command: ${commandId} took ${duration}ms`);
            }
        }
    };
}

Extension Host Profiling

1. Developer: Show Running Extensions
2. Note CPU/memory per extension
3. Use --prof flag for detailed profiling

Benchmarking Best Practices

Benchmark Setup

// Node.js with Benchmark.js
import Benchmark from 'benchmark';

const suite = new Benchmark.Suite();

suite
  .add('Method A', () => methodA(testData))
  .add('Method B', () => methodB(testData))
  .on('cycle', (event) => console.log(String(event.target)))
  .on('complete', function() {
    console.log('Fastest is ' + this.filter('fastest').map('name'));
  })
  .run({ async: true });

Benchmarking Rules

1. Warm up — Run once before measuring
2. Isolate — One variable at a time
3. Repeat — Statistical significance (n > 30)
4. Realistic data — Use production-like inputs
5. Disable optimizations — Ensure code runs (avoid dead code elimination)
6. Document baseline — Record environment, date, conditions

Performance Optimization Patterns

Caching

Cache strategy:

async function getCached<T>(key: string, fetchFn: () => Promise<T>, ttlMs: number): Promise<T> {
    const cached = cache.get(key);
    if (cached && cached.expires > Date.now()) {
        return cached.value;
    }
    const value = await fetchFn();
    cache.set(key, { value, expires: Date.now() + ttlMs });
    return value;
}

Lazy Evaluation

// ❌ Eager - computes immediately
const allUsers = users.filter(u => u.active).map(u => u.name);
const firstTen = allUsers.slice(0, 10);

// ✅ Lazy - computes only what's needed (with generator)
function* activeUserNames(users) {
    for (const user of users) {
        if (user.active) yield user.name;
    }
}
const iterator = activeUserNames(users);
const firstTen = Array.from({ length: 10 }, () => iterator.next().value);

Batching

// ❌ N requests
for (const id of ids) {
    await fetchUser(id);  // N round trips
}

// ✅ 1 request
const users = await fetchUsers(ids);  // Batch endpoint

Debouncing/Throttling

// Debounce - wait for pause in calls
function debounce(fn: Function, delay: number) {
    let timeout: NodeJS.Timeout;
    return (...args: any[]) => {
        clearTimeout(timeout);
        timeout = setTimeout(() => fn(...args), delay);
    };
}

// Throttle - max once per interval
function throttle(fn: Function, interval: number) {
    let lastCall = 0;
    return (...args: any[]) => {
        const now = Date.now();
        if (now - lastCall >= interval) {
            lastCall = now;
            fn(...args);
        }
    };
}

Performance Budget

Define acceptable limits:

Implementation Checklist

Investigation

• Reproduce the performance issue
• Establish baseline measurement
• Identify bottleneck type (CPU/memory/I/O/network)
• Profile with appropriate tool
• Find the hotspot

Optimization

• Form hypothesis for fix
• Implement fix (smallest change first)
• Measure improvement
• Verify no regression elsewhere
• Document the change

Prevention

• Add performance metric to monitoring
• Set alert threshold
• Add benchmark to CI
• Update performance budget

Related Skills

• observability-monitoring — Ongoing performance visibility
• database-design — Query optimization at design level
• debugging-patterns — Systematic investigation approaches
• code-review — Catch performance issues in review

The fastest code is code that doesn't run. The second fastest is code that runs once.