Performance

Our Node.js API was restarting every 6 hours due to memory leaks. Took me two weeks to find the bug. It was a single missing removeListener() call. Here’s how I found it, and what I learned about debugging Node memory leaks that actually works. The symptom Memory usage graph looked like this: Memory │ ╱╱╱╱ │ ╱╱ │ ╱╱ └────────────> Time Classic memory leak pattern. Process starts at 200MB, grows to 2GB over 6 hours, then OOM kills it. Kubernetes restarts it. Repeat. ...

We were spending $12,000/month on Redis before I realized we were doing it completely wrong. Not “slightly inefficient” wrong. Full-on “why is our cache bigger than our database” wrong. Here’s what I learned after three months of firefighting and optimization. The problem nobody warned us about Our Redis instance hit 32GB of memory. Our actual PostgreSQL database? 8GB. Something was very, very wrong. Started digging through our caching logic. Found this gem: ...

So here’s the thing nobody tells you about JSONB in Postgres: it’s fast until it’s not. We migrated our user preferences table to use JSONB columns three months ago. The pitch was simple - no more ALTER TABLE every time marketing wants to track a new preference. Just stuff it in JSON and call it a day. Seemed smart at the time. The honeymoon phase First two weeks were great. Engineers loved it. Product loved it. We shipped features faster because we didn’t need schema migrations for every little thing. Our user_settings column grew from tracking 5 fields to 23. No problem, right? ...

Our CI pipeline was embarrassing. Every PR took 8+ minutes to build a Docker image for a Next.js app. Developers complained. I ignored them for two months because “it’s just CI, ship faster code.” Then we hit 50+ PRs per day and our CI bill jumped $400/month. Time to actually fix it. The original Dockerfile (the bad one) FROM node:18 WORKDIR /app COPY . . RUN npm install RUN npm run build CMD ["npm", "start"] Looks innocent. Builds every time though. Every. Single. Time. ...

Indexes are supposed to make queries fast. Sometimes they make them slower. Here’s what I wish someone told me before I tanked production performance trying to “optimize” our database. The query that started it all Support said the admin dashboard was timing out. Found this in the slow query log: SELECT * FROM orders WHERE customer_id = 12345 AND status IN ('pending', 'processing') AND created_at > '2025-12-01' ORDER BY created_at DESC LIMIT 20; Took 8 seconds on a table with 4 million rows. Obviously needs an index, right? ...

GraphQL makes it stupidly easy to write queries that murder your database. Our homepage was hitting the database 2,031 times per page load. Yeah. Here’s how we got it down to 3 queries without changing the API. The query that looked innocent query Homepage { posts { id title author { id name avatar } comments { id text author { name } } } } Looks fine. Perfectly reasonable GraphQL query. Returns 10 posts with their authors and comments. ...

This write-up condenses the published DEV tutorial “Browser-based performance testing with K6” into an actionable walkthrough for frontend teams. Why k6 browser Simulates real browser interactions (Chromium) vs. protocol-only tests. Captures Web Vitals (LCP/INP/CLS) and UX signals (spinners, interactivity). Reuses familiar k6 scripting with async/await + locators. Setup Install k6 (latest) and ensure Node/npm are available. Project skeleton: k6-tests/ libs/ # flows (login, add to cart, checkout) pages/ # Page Object Model classes tests/ # test entry scripts utils/ # shared options (browser config, VUs, iterations) Configure browser scenario (utils/k6-options.js): export const browserOptions = { scenarios: { ui: { executor: "shared-iterations", vus: 1, iterations: 15, options: { browser: { type: "chromium" } }, }, }, }; Example flow (simplified) import { browser } from "k6/browser"; import { check } from "k6"; export const options = browserOptions; export default async function () { const page = await browser.newPage(); await page.goto("https://www.saucedemo.com/"); await page.locator("#user-name").type("standard_user"); await page.locator("#password").type("secret_sauce"); await page.locator("#login-button").click(); check(page, { "login ok": () => page.locator(".title").textContent() === "Products" }); await page.close(); } Running & reporting CLI run: k6 run ./tests/buyItemsUserA.js Live dashboard: K6_WEB_DASHBOARD=true K6_WEB_DASHBOARD_OPEN=true k6 run ... Export HTML report: K6_WEB_DASHBOARD=true K6_WEB_DASHBOARD_EXPORT=results/report.html Metrics to watch (aligns to Core Web Vitals) browser_web_vital_lcp — largest content paint; target < 2.5s p75. browser_web_vital_inp — interaction to next paint; target < 200ms p75. browser_web_vital_cls — layout shift; target < 0.1. Add custom checks for “spinner disappears”, “cart count”, “success message”. Tips Keep tests short and focused; one scenario per file. Reuse Page Objects for maintainability. Run with realistic VUs/iterations that mirror expected traffic. Use screenshots on failure for debuggability (page.screenshot()). Takeaway: k6 browser mode gives frontend teams reproducible, scriptable UX performance checks—ideal for catching regressions before they reach real users.

This post distills the key ideas from the published DEV article “Frontend Performance Optimization: A Comprehensive Guide 🚀” into a concise, production-focused playbook. What matters Fast first paint and interaction: prioritize above-the-fold content, cut JS weight, avoid layout shifts. Ship less: smaller bundles, fewer blocking requests, cache aggressively. Ship smarter: load what’s needed when it’s needed (on demand and in priority order). Core techniques 1) Selective rendering Render only what is visible (e.g., IntersectionObserver + skeletons). Defer heavy components until scrolled into view. 2) Code splitting & dynamic imports Split by route/feature; lazy-load non-critical views. Example (React): const Page = lazy(() => import("./Page")); <Suspense fallback={<div>Loading…</div>}> <Page /> </Suspense>; 3) Prefetching & caching Prefetch likely-next routes/assets (<link rel="prefetch"> or router prefetch). Pre-warm API data with React Query/Next.js loader functions. 4) Priority-based loading Preload critical CSS/hero imagery: <link rel="preload" as="style" href="styles.css">. Use defer/async for non-critical scripts. 5) Compression & transfer Enable Brotli/Gzip at the edge; pre-compress static assets in CI/CD. Serve modern formats (AVIF/WebP) and keep caching headers long-lived. 6) Loading sequence hygiene Order: HTML → critical CSS → critical JS → images/fonts → analytics. Avoid long main-thread tasks; prefer requestIdleCallback for non-urgent work. 7) Tree shaking & dead-code elimination Use ESM named imports; avoid import *. Keep build in production mode with minification + module side-effects flags. Measurement & guardrails Track Core Web Vitals (LCP, INP, CLS) plus TTFB and bundle size per release. Run Lighthouse/WebPageTest in CI; fail builds on regressions above agreed budgets. Monitor real-user metrics (RUM) to validate improvements after deploys. Quick starter checklist Lazy-load routes and heavy widgets. Preload hero font/hero image; inline critical CSS if needed. Turn on Brotli at CDN; cache static assets with versioned filenames. Set performance budgets (JS < 200KB gz, LCP < 2.5s p75, INP < 200ms). Automate audits in CI and watch RUM dashboards after each release. Bottom line: Combine “ship less” (smaller, shaken, compressed bundles) with “ship smarter” (prioritized, on-demand loading) and enforce budgets with automated checks to keep your app fast over time.

Based on the DEV article “Building Performant UI with Rust, WebAssembly, and Tailwind CSS,” this summary focuses on the architecture and steps to integrate the stack. Why Rust + WASM Offload CPU-heavy tasks (parsing, transforms, image ops) from the JS main thread. Near-native speed with memory safety. Keeps UI responsive while heavy logic runs in WASM. Why Tailwind here Utility-first styling keeps CSS minimal and predictable. Co-locate styles with components; avoid global collisions. Fast to iterate on responsive layouts for WASM-powered widgets. Integration workflow Compile Rust to WASM with wasm-pack/wasm-bindgen. Import the .wasm module via your bundler (Vite/Webpack/esbuild) and expose JS bindings. Call Rust functions from JS; render results in Tailwind-styled components. Use containers like overflow-x-auto, max-w-full, sm:rounded-lg to keep WASM widgets responsive. Example flow (pseudo) // rust-lib/src/lib.rs (simplified) #[wasm_bindgen] pub fn summarize(data: String) -> String { // heavy work here... format!("size: {}", data.len()) } // web/src/useSummarize.ts import init, { summarize } from "rust-wasm-lib"; export async function useSummarize(input: string) { await init(); return summarize(input); } Performance notes Keep WASM modules small; lazy-load them when the feature is needed. Avoid blocking the main thread—invoke WASM in response to user actions, not eagerly. Profile with browser DevTools + wasm-bindgen debug symbols when needed. Design principles Establish Tailwind design tokens/utilities for spacing/typography early. Encapsulate WASM widgets as reusable components with clear props. Reserve space to prevent layout shifts when results arrive. Good fits Data/analytics dashboards, heavy transforms, visualization prep. Fintech/scientific tools where CPU work dominates. Developer tools needing deterministic, fast processing in-browser. Takeaway: Let Rust/WASM handle the heavy lifting while Tailwind keeps the UI lean and consistent—yielding responsive, performant web experiences.

Test design Define goals: latency budgets (p95/p99), error ceilings, throughput targets. Scenarios: ramping arrival rate, soak tests, spike tests; match production payloads. Include auth headers and realistic think time. k6 script skeleton import http from "k6/http"; import { check, sleep } from "k6"; export const options = { thresholds: { http_req_duration: ["p(95)<300"] }, scenarios: { api: { executor: "ramping-arrival-rate", startRate: 10, timeUnit: "1s", preAllocatedVUs: 50, maxVUs: 200, stages: [ { target: 100, duration: "3m" }, { target: 100, duration: "10m" }, { target: 0, duration: "2m" }, ]}, }, }; export default function () { const res = http.get("https://api.example.com/resource"); check(res, { "status 200": (r) => r.status === 200 }); sleep(1); } Run & observe Capture k6 summary + JSON output; feed to Grafana for trends. Correlate with Go metrics (pprof, Prometheus) to find CPU/alloc hot paths. Record build SHA; compare runs release over release. Checklist Scenarios match prod traffic shape. Thresholds tied to SLOs; tests fail on regressions. Service metrics/pprof captured during runs.