Optimizing Performance

Performance Optimization Workflow

Copy this checklist and track progress:

Performance Optimization Progress:
- [ ] Step 1: Measure baseline performance
- [ ] Step 2: Identify bottlenecks
- [ ] Step 3: Apply targeted optimizations
- [ ] Step 4: Measure again and compare
- [ ] Step 5: Repeat if targets not met

Critical Rule: Never optimize without data. Always profile before and after changes.

Step 1: Measure Baseline

Profiling Commands

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

# Python profiling
python -m cProfile -o profile.stats app.py
python -m pstats profile.stats

# Web performance
lighthouse https://example.com --output=json

Step 2: Identify Bottlenecks

Common Bottleneck Categories

Category	Symptoms	Tools
CPU	High CPU usage, slow computation	Profiler, flame graphs
Memory	High RAM, GC pauses, OOM	Heap snapshots, memory profiler
I/O	Slow disk/network, waiting	strace, network inspector
Database	Slow queries, lock contention	Query analyzer, EXPLAIN

Step 3: Apply Optimizations

Frontend Optimizations

Bundle Size:

// ❌ Import entire library
import _ from 'lodash';

// ✅ Import only needed functions
import debounce from 'lodash/debounce';

// ✅ Use dynamic imports for code splitting
const HeavyComponent = lazy(() => import('./HeavyComponent'));

Rendering:

// ❌ Render on every parent update
function Child({ data }) {
  return <ExpensiveComponent data={data} />;
}

// ✅ Memoize when props don't change
const Child = memo(function Child({ data }) {
  return <ExpensiveComponent data={data} />;
});

// ✅ Use useMemo for expensive computations
const processed = useMemo(() => expensiveCalc(data), [data]);

Images:

<!-- ❌ Unoptimized -->
<img src="large-image.jpg" />

<!-- ✅ Optimized -->
<img
  src="image.webp"
  srcset="image-300.webp 300w, image-600.webp 600w"
  sizes="(max-width: 600px) 300px, 600px"
  loading="lazy"
  decoding="async"
/>

Backend Optimizations

Database Queries:

-- ❌ N+1 Query Problem
SELECT * FROM users;
-- Then for each user:
SELECT * FROM orders WHERE user_id = ?;

-- ✅ Single query with JOIN
SELECT u.*, o.*
FROM users u
LEFT JOIN orders o ON u.id = o.user_id;

-- ✅ Or use pagination
SELECT * FROM users LIMIT 100 OFFSET 0;

Caching Strategy:

// Multi-layer caching
const getUser = async (id) => {
  // L1: In-memory cache (fastest)
  let user = memoryCache.get(`user:${id}`);
  if (user) return user;

  // L2: Redis cache (fast)
  user = await redis.get(`user:${id}`);
  if (user) {
    memoryCache.set(`user:${id}`, user, 60);
    return JSON.parse(user);
  }

  // L3: Database (slow)
  user = await db.users.findById(id);
  await redis.setex(`user:${id}`, 3600, JSON.stringify(user));
  memoryCache.set(`user:${id}`, user, 60);

  return user;
};

Async Processing:

// ❌ Blocking operation
app.post('/upload', async (req, res) => {
  await processVideo(req.file);  // Takes 5 minutes
  res.send('Done');
});

// ✅ Queue for background processing
app.post('/upload', async (req, res) => {
  const jobId = await queue.add('processVideo', { file: req.file });
  res.send({ jobId, status: 'processing' });
});

Algorithm Optimizations

// ❌ O(n²) - nested loops
function findDuplicates(arr) {
  const duplicates = [];
  for (let i = 0; i < arr.length; i++) {
    for (let j = i + 1; j < arr.length; j++) {
      if (arr[i] === arr[j]) duplicates.push(arr[i]);
    }
  }
  return duplicates;
}

// ✅ O(n) - hash map
function findDuplicates(arr) {
  const seen = new Set();
  const duplicates = new Set();
  for (const item of arr) {
    if (seen.has(item)) duplicates.add(item);
    seen.add(item);
  }
  return [...duplicates];
}

Step 4: Measure Again

After applying optimizations, re-run profiling and compare:

Comparison Checklist:
- [ ] Run same profiling tools as baseline
- [ ] Compare metrics before vs after
- [ ] Verify no regressions in other areas
- [ ] Document improvement percentages

Performance Targets

Web Vitals

Metric	Good	Needs Work	Poor
LCP	< 2.5s	2.5-4s	> 4s
FID	< 100ms	100-300ms	> 300ms
CLS	< 0.1	0.1-0.25	> 0.25
TTFB	< 800ms	800ms-1.8s	> 1.8s

API Performance

Metric	Target
P50 Latency	< 100ms
P95 Latency	< 500ms
P99 Latency	< 1s
Error Rate	< 0.1%

Validation

After optimization, validate results:

Performance Validation:
- [ ] Metrics improved from baseline
- [ ] No functionality regressions
- [ ] No new errors introduced
- [ ] Changes are sustainable (not one-time fixes)
- [ ] Performance gains documented

If targets not met, return to Step 2 and identify remaining bottlenecks.