performance-optimization

--- name: performance-optimization description: Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks. --- # Performance Optimization ## Overview Measure before optimizing. Performance work without measurement is guessing — and guessing leads to premature optimization that adds complexity without improving what matters. Profile first, identify the actual bottleneck, fix it, measure again. Optimize only what measurements prove matters. ## When to Use - Performance requirements exist in the spec (load time budgets, response time SLAs) - Users or monitoring report slow behavior - Core Web Vitals scores are below thresholds - You suspect a change introduced a regression - Building features that handle large datasets or high traffic **When NOT to use:** Don't optimize before you have evidence of a problem. Premature optimization adds complexity that costs more than the performance it gains. ## Core Web Vitals Targets | Metric | Good | Needs Improvement | Poor | |--------|------|-------------------|------| | **LCP** (Largest Contentful Paint) | ≤ 2.5s | ≤ 4.0s | > 4.0s | | **INP** (Interaction to Next Paint) | ≤ 200ms | ≤ 500ms | > 500ms | | **CLS** (Cumulative Layout Shift) | ≤ 0.1 | ≤ 0.25 | > 0.25 | ## The Optimization Workflow ``` 1. MEASURE → Establish baseline with real data 2. IDENTIFY → Find the actual bottleneck (not assumed) 3. FIX → Address the specific bottleneck 4. VERIFY → Measure again; keep or revert 5. GUARD → Add monitoring or tests to prevent regression ``` ### Step 1: Measure Two complementary approaches — use both: - **Synthetic (Lighthouse, DevTools Performance tab):** Controlled conditions, reproducible. Best for CI regression detection and isolating specific issues. - **RUM (web-vitals library, CrUX):** Real user data in real conditions. Required to validate that a fix actually improved user experience. **Frontend:** ```bash # Synthetic: Lighthouse in Chrome DevTools (or CI) # Chrome DevTools → Performance tab → Record # Chrome DevTools MCP → Performance trace # RUM: Web Vitals library in code import { onLCP, onINP, onCLS } from 'web-vitals'; onLCP(console.log); onINP(console.log); onCLS(console.log); ``` **Backend:** ```bash # Response time logging # Application Performance Monitoring (APM) # Database query logging with timing # Simple timing console.time('db-query'); const result = await db.query(...); console.timeEnd('db-query'); ``` ### Where to Start Measuring Use the symptom to decide what to measure first: ``` What is slow? ├── First page load │ ├── Large bundle? --> Measure bundle size, check code splitting │ ├── Slow server response? --> Measure TTFB in DevTools Network waterfall │ │ ├── DNS long? --> Add dns-prefetch / preconnect for known origins │ │ ├── TCP/TLS long? --> Enable HTTP/2, check edge deployment, keep-alive │ │ └── Waiting (server) long? --> Profile backend, check queries and caching │ └── Render-blocking resources? --> Check network waterfall for CSS/JS blocking ├── Interaction feels sluggish │ ├── UI freezes on click? --> Profile main thread, look for long tasks (>50ms) │ ├── Form input lag? --> Check re-renders, controlled component overhead │ └── Animation jank? --> Check layout thrashing, forced reflows ├── Page after navigation │ ├── Data loading? --> Measure API response times, check for waterfalls │ └── Client rendering? --> Profile component render time, check for N+1 fetches └── Backend / API ├── Single endpoint slow? --> Profile database queries, check indexes ├── All endpoints slow? --> Check connection pool, memory, CPU └── Intermittent slowness? --> Check for lock contention, GC pauses, external deps ``` ### Step 2: Identify the Bottleneck Common bottlenecks by category: **Frontend:** | Symptom | Likely Cause | Investigation | |---------|-------------|---------------| | Slow LCP | Large images, render-blocking resources, slow server | Check network waterfall, image sizes | | High CLS | Images without dimensions, late-loading content, font shifts | Check layout shift attribution | | Poor INP | Heavy JavaScript on main thread, large DOM updates | Check long tasks in Performance trace | | Slow initial load | Large bundle, many network requests | Check bundle size, code splitting | **Backend:** | Symptom | Likely Cause | Investigation | |---------|-------------|---------------| | Slow API responses | N+1 queries, missing indexes, unoptimized queries | Check database query log | | Memory growth | Leaked references, unbounded caches, large payloads | Heap snapshot analysis | | CPU spikes | Synchronous heavy computation, regex backtracking | CPU profiling | | High latency | Missing caching, redundant computation, network hops | Trace requests through the stack | ### Step 3: Fix Common Anti-Patterns #### N+1 Queries (Backend) ```typescript // BAD: N+1 — one query per task for the owner const tasks = await db.tasks.findMany(); for (const task of tasks) { task.owner = await db.users.findUnique({ where: { id: task.ownerId } }); } // GOOD: Single query with join/include const tasks = await db.tasks.findMany({ include: { owner: true }, }); ``` #### Unbounded Data Fetching ```typescript // BAD: Fetching all records const allTasks = await db.tasks.findMany(); // GOOD: Paginated with limits const tasks = await db.tasks.findMany({ take: 20, skip: (page - 1) * 20, orderBy: { createdAt: 'desc' }, }); ``` #### Queries That Ignore Their Index "Add an index" is the guess. The query plan is the measurement: ```sql EXPLAIN ANALYZE SELECT id, title FROM tasks WHERE owner_id = 42 ORDER BY created_at DESC LIMIT 20; ``` Three things in the output decide the fix: | What you see | What it means | |---|---| | `Seq Scan` on a large table where you expected an index | No usable index for this predicate | | Estimated `rows=` off from actual by an order of magnitude | Stale statistics; the planner is choosing on bad information | | A `Sort` node above the scan | The index covers the filter but not the `ORDER BY` | Index for the **shape of the query**, not the column in isolation. In a composite index, equality columns come first, then the range or sort column: ```sql CREATE INDEX idx_tasks_owner_created ON tasks (owner_id, created_at DESC); ``` **When an index will not help:** | Situation | Why | |---|---| | Low selectivity, querying the dominant value (a `status` column that is 95% `active`, filtered on `active`) | A sequential scan is genuinely cheaper; the planner will ignore the index. Filtering on the rare value is the opposite case, and a partial index serves it well | | Leading wildcard (`LIKE '%term'`) | A B-tree cannot seek without a prefix; needs trigram or full-text | | Function on the column (`WHERE lower(email) = ?`) | The plain column index is unusable; index the expression instead | | Write-heavy table | Every index is a tax on every `INSERT`/`UPDATE`; measure the write cost, not just the read gain | Re-run `EXPLAIN ANALYZE` after. An index that did not change the plan is a revert (Step 4), and it is not free: it still costs on every write. #### Connection Pool Exhaustion The signature is distinctive: **every** endpoint slows at once, the slow time is spent waiting for a connection rather than executing, and the database reports mostly idle sessions. ```typescript // BAD: a pool per request or per module — under serverless this multiplies // by instance count and exhausts the database's connection limit // GOOD: one pool per process, sized against the database's ceiling const pool = new Pool({ max: 10, // instances × max must stay under max_connections idleTimeoutMillis: 30_000, connectionTimeoutMillis: 5_000, // fail fast instead of queueing forever }); ``` **Bigger is not faster.** A pool larger than what the database can execute concurrently just relocates the queue from your app to the database, where it is harder to see. When instance count is unbounded (serverless, autoscaling), a proxy that multiplexes connections (pgbouncer, RDS Proxy) is the fix, not a higher `max`. #### Missing Image Optimization (Frontend) ```html <!-- BAD: No dimensions, no format optimization --> <img src="/hero.jpg" /> <!-- GOOD: Hero / LCP image — art direction + resolution switching, high priority --> <!-- Two techniques combined: - Art direction (media): different crop/composition per breakpoint - Resolution switching (srcset + sizes): right file size per screen density --> <picture> <!-- Mobile: portrait crop (8:10) --> <source media="(max-width: 767px)" srcset="/hero-mobile-400.avif 400w, /hero-mobile-800.avif 800w" sizes="100vw" width="800" height="1000" type="image/avif" /> <source media="(max-width: 767px)" srcset="/hero-mobile-400.webp 400w, /hero-mobile-800.webp 800w" sizes="100vw" width="800" height="1000" type="image/webp" /> <!-- Desktop: landscape crop (2:1) --> <source srcset="/hero-800.avif 800w, /hero-1200.avif 1200w, /hero-1600.avif 1600w" sizes="(max-width: 1200px) 100vw, 1200px" width="1200" height="600" type="image/avif" /> <source srcset="/hero-800.webp 800w, /hero-1200.webp 1200w, /hero-1600.webp 1600w" sizes="(max-width: 1200px) 100vw, 1200px" width="1200" height="600" type="image/webp" /> <img src="/hero-desktop.jpg" width="1200" height="600" fetchpriority="high" alt="Hero image description" /> </picture> <!-- GOOD: Below-the-fold image — lazy loaded + async decoding --> <img src="/content.webp" width="800" height="400" loading="lazy" decoding="async" alt="Content image description" /> ``` #### Unnecessary Re-renders (React) ```tsx // BAD: Creates new object on every render, causing children to re-render function TaskList() { return <TaskFilters options={{ sortBy: 'date', order: 'desc' }} />; } // GOOD: Stable reference const DEFAULT_OPTIONS = { sortBy: 'date', order: 'desc' } as const; function TaskList() { return <TaskFilters options={DEFAULT_OPTIONS} />; } // Use React.memo for expensive components const TaskItem = React.memo(function TaskItem({ task }: Props) { return <div>{/* expensive render */}</div>; }); // Use useMemo for expensive computations function TaskStats({ tasks }: Props) { const stats = useMemo(() => calculateStats(tasks), [tasks]); return <div>{stats.completed} / {stats.total}</div>; } ``` #### Large Bundle Size ```typescript // Modern bundlers (Vite, webpack 5+) handle named imports with tree-shaking automatically, // provided the dependency ships ESM and is marked `sideEffects: false` in package.json. // Profile before changing import styles — the real gains come from splitting and lazy loading. // GOOD: Dynamic import for heavy, rarely-used features const ChartLibrary = lazy(() => import('./ChartLibrary')); // GOOD: Route-level code splitting wrapped in Suspense const SettingsPage = lazy(() => import('./pages/Settings')); function App() { return ( <Suspense fallback={<Spinner />}> <SettingsPage /> </Suspense> ); } ``` #### Missing Caching (Backend) Cache what is expensive to produce and read far more often than it changes. Caching a query that was already fast adds a network hop, a staleness bug, and an eviction policy to maintain, in exchange for nothing. **Pick the layer deliberately:** | Layer | Visible to | Use when | Cost | |---|---|---|---| | In-process (`Map`, LRU) | One instance | Small, hot, per-instance staleness is acceptable | Each instance drifts independently; invalidation reaches only one | | Shared (Redis, Memcached) | All instances | Instances must agree, or the value is expensive to recompute | A network hop, and another service to run and monitor | | CDN / edge | Everyone, per URL | Responses are public and identical for a given key | Invalidation is the hard part; assume you cannot recall a bad response quickly | ```typescript // Cache frequently-read, rarely-changed data const CACHE_TTL = 5 * 60 * 1000; // 5 minutes let cachedConfig: AppConfig | null = null; let cacheExpiry = 0; async function getAppConfig(): Promise<AppConfig> { if (cachedConfig && Date.now() < cacheExpiry) { return cachedConfig; } cachedConfig = await db.config.findFirst(); cacheExpiry = Date.now() + CACHE_TTL; return cachedConfig; } // HTTP caching headers for static assets app.use('/static', express.static('public', { maxAge: '1y', // Cache for 1 year immutable: true, // Never revalidate (use content hashing in filenames) })); // Cache-Control for API responses res.set('Cache-Control', 'public, max-age=300'); // 5 minutes ``` **Key design decides correctness.** Every input that changes the response belongs in the key: tenant, locale, permissions, feature flags. A key that omits the viewer is how one user's data gets served to another, and that ships as a performance win. **Choose one invalidation strategy, not three:** | Strategy | Trade-off | |---|---| | TTL | Simplest. You accept staleness up to the TTL, so state the acceptable window explicitly | | Event or tag based | Fresh on write, but writers now have to know the cache topology | | Versioned keys (`user:42:profile:v7`) | Never invalidate, just stop reading old keys. Costs memory until eviction | **Guard against the stampede.** A hot key expires, every concurrent request misses together, and the origin takes the full load at once, which is how a cache turns into an outage instead of preventing one. Serve stale while a single request recomputes (`stale-while-revalidate`), or coalesce concurrent misses behind one in-flight promise so N waiters cause one recompute. **Do not cache:** anything whose staleness is a correctness bug (balances, permissions, inventory at checkout), or per-user data under a key that does not identify the user. See `../../references/performance-checklist.md` for request coalescing, write strategies, negative caching, and the cache checklist. ### Step 4: Verify (Keep or Revert) A fix is a hypothesis until you re-measure. This step decides whether it survives. **Re-measure the way you measured the baseline:** same command, same conditions, same fixed budget (wall-clock, sample count, or request count). A baseline taken on a cold cache against a result taken on a warm one measures the cache, not your change. **Change one thing at a time.** Three optimizations landed together produce one number, and you cannot attribute it. If they must ship together, measure each in isolation first. **Beat the noise, not just the mean.** Repeat the measurement and compare the delta against run-to-run variance. A 3% gain inside ±5% variance is not a gain; it is a different sample. Then decide, strictly: | Result vs. baseline | Action | |---|---| | Past the threshold, tests green | **Keep.** Commit with the before/after numbers in the message. | | Within noise (no measurable change) | **Revert.** | | Worse | **Revert.** | | Improved, but a test went red | **Revert.** A regression wearing a win's clothing. | **"Neutral" is a revert, not a keep.** This is the step teams skip: the change is already written, throwing it away feels wasteful, so it lands unmeasured, and the codebase accretes complexity that never bought anything. Code you keep, you maintain forever. Make it pay for itself. **Correctness gates the metric.** The suite stays green *and* the number moves. An "optimization" that wins by dropping work the product needed (skipping a validation, caching something that must be fresh, removing an `await` that was load-bearing) is a regression, not a win. #### Log every attempt, including the reverted ones Reverted work leaves no trace in git history, which is exactly why the same dead idea gets tried again next quarter. Keep a short ledger so a discarded idea stays discarded: | Idea | Baseline → Result | Verdict | Why | |---|---|---|---| | Memoize the row component | INP 240ms → 235ms | reverted | Inside noise (±15ms). Rows weren't the bottleneck. | | Virtualize the list | INP 240ms → 90ms | kept | Long tasks gone from the trace. | | Preconnect to the API origin | LCP 2.8s → 2.8s | reverted | Already same-origin. | A section in the PR description or a `PERF.md` in the repo both work. What matters is that the next person (or the next agent) reads it before proposing an experiment, and doesn't re-run one that already failed. ### Step 5: Guard Against Regression Guard the metric the user actually feels, not every available number. Use the same LCP, INP, p95 latency, or other primary metric that justified the fix. Use two complementary layers when the surface is user-facing: - **Synthetic CI gate:** Catch reproducible regressions before merge with a performance budget. Repeat noisy measurements or compare a median/trend so normal run-to-run variance does not turn the gate into a flaky check. - **Field monitoring:** Alert on a meaningful p75 movement in RUM data. Use attributed `web-vitals` data to locate the cause; treat CrUX's rolling window as confirmation rather than an immediate alert. When either guard fires, return to Step 1 and establish a fresh baseline before proposing another fix. **Set budgets and enforce them:** ``` JavaScript bundle: < 200KB gzipped (initial load) CSS: < 50KB gzipped Images: < 200KB per image (above the fold) Fonts: < 100KB total API response time: < 200ms (p95) Time to Interactive: < 3.5s on 4G Lighthouse Performance score: ≥ 90 ``` **Enforce in CI:** ```bash # Bundle size check npx bundlesize --config bundlesize.config.json # Lighthouse CI npx lhci autorun ``` ## See Also For detailed performance checklists, optimization commands, and anti-pattern reference, see `../../references/performance-checklist.md`. ## Common Rationalizations | Rationalization | Reality | |---|---| | "We'll optimize later" | Performance debt compounds. Fix obvious anti-patterns now, defer micro-optimizations. | | "It's fast on my machine" | Your machine isn't the user's. Profile on representative hardware and networks. | | "This optimization is obvious" | If you didn't measure, you don't know. Profile first. | | "Users won't notice 100ms" | Research shows 100ms delays impact conversion rates. Users notice more than you think. | | "The framework handles performance" | Frameworks prevent some issues but can't fix N+1 queries or oversized bundles. | | "The query is slow, add an index" | Read the plan first. The index may already exist and be unusable, and every index taxes writes forever. | | "Just cache it" | Caching an already-cheap call buys nothing and adds a staleness bug. Cache what is expensive *and* re-read far more than written. | | "Raise the pool size, we're running out of connections" | A pool bigger than the database can serve moves the queue somewhere less visible. Find what holds connections. | | "It didn't help much, but it doesn't hurt" | Neutral changes are a revert. You pay maintenance on them forever and got nothing back. | | "We already wrote it, may as well keep it" | Sunk cost. The measurement doesn't care how long the change took to write. | | "The improvement is obvious, no need to re-measure" | Then re-measuring is cheap and proves it. Unmeasured wins are how neutral complexity lands. | ## Red Flags - Optimization without profiling data to justify it - N+1 query patterns in data fetching - An index added without a query plan before and after to justify it - A cache key that omits an input the response depends on (tenant, locale, viewer) - A cache with no stated staleness window and no invalidation strategy - Connection pool size raised in response to exhaustion, without finding what holds connections - List endpoints without pagination - Images without dimensions, lazy loading, or responsive sizes - Bundle size growing without review - No performance monitoring in production - `React.memo` and `useMemo` everywhere (overusing is as bad as underusing) - Optimizations kept without a re-measurement that justifies them - Several optimizations bundled into one measurement, so no single change can be attributed - A "win" that required a test to be changed, skipped, or deleted - The same failed optimization attempted more than once because nobody recorded the first attempt ## Verification After any performance-related change: - [ ] Before and after measurements exist (specific numbers) - [ ] The result was re-measured the same way as the baseline (same command, same conditions) - [ ] The improvement exceeds run-to-run variance, not just the mean - [ ] Changes that didn't beat the baseline were reverted, not kept as neutral - [ ] Attempts are logged, kept and reverted alike, so a dead idea isn't re-run - [ ] The specific bottleneck is identified and addressed - [ ] Core Web Vitals are within "Good" thresholds - [ ] Bundle size hasn't increased significantly - [ ] No N+1 queries in new data fetching code - [ ] Any new index is justified by a query plan before and after, and its write cost was considered - [ ] Any new cache states what it keys on and how it goes stale - [ ] The measured user-facing metric has a synthetic budget or field monitor that can detect regression - [ ] Existing tests still pass (optimization didn't break behavior)

1. **Profile First**: Use tools like [Lighthouse](https://developer.chrome.com/docs/lighthouse/overview/), [New Relic](https://newrelic.com/), or [Datadog APM](https://www.datadoghq.com/product/apm/) to identify bottlenecks. Export the data as a JSON/CSV report and paste it into the prompt. 2. **Specify the Scope**: Clearly define the [APPLICATION_NAME] and [SPECIFIC_AREA] (e.g., 'homepage load time' or 'checkout API latency'). Include metrics like current load times or error rates if available. 3. **Set Targets**: Define performance goals (e.g., 'reduce LCP from 3.5s to 2.5s' or 'cut API response time by 50%'). Use placeholders like [PLACEHOLDER: e.g., 20%] to guide the AI. 4. **Implement Incrementally**: Start with the highest-impact fixes (e.g., image optimization or N+1 queries) and validate improvements using real user monitoring (RUM) tools. 5. **Iterate**: Re-run the analysis after each optimization cycle to uncover new bottlenecks. Use the AI’s output to generate tickets in tools like Jira or Linear.