/optimizing-performance

name: optimizing-performance
description: Analyzes and optimizes application performance across frontend, backend, and database layers. Use when diagnosing slowness, improving load times, optimizing queries, reducing bundle size, or when asked about performance issues.

Optimizing Performance

When to Load

• **Trigger**: Diagnosing slowness, profiling, caching strategies, reducing load times, bundle size optimization
• **Skip**: Correctness-focused work where performance is not a concern

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"
  width="600"
  height="400"
  alt="Description"
  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.id, u.name, o.id AS order_id, o.total
FROM users u
LEFT JOIN orders o ON u.id = o.user_id;

-- ✅ Or use pagination
SELECT id, name FROM users WHERE id > :last_id ORDER BY id LIMIT 100;

**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:${id}`);
  if (user) {
    user = JSON.parse(user);
    memoryCache.set(`user:${id}`, user, 60);
    return 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.status(202).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 | | INP | < 200ms | 200-500ms | > 500ms | | 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.