Performance

Incident Report: Node.js Memory Leak Analysis Date: 2026-01-20 Severity: P1 (Production Impact) MTTR: 14 days Root Cause: Event listener leak in WebSocket handler Timeline of Events Day 1 - Jan 6, 09:30 UTC Monitoring alerts: API instances restarting every 6 hours Memory usage shows sawtooth pattern (gradual climb, sudden drop) Initial hypothesis: Database connection leak Day 3 - Jan 8 Ruled out database connections (pool metrics normal) Added heap profiling to staging environment Identified EventEmitter instances growing unbounded Day 7 - Jan 13 ...

Common Redis Caching Mistakes (Q&A Format) Q: Our Redis instance is 32GB but our database is only 8GB. Is this normal? A: No, this is a red flag indicating poor cache hygiene. We encountered this exact scenario. Investigation revealed cache entries from: Users inactive for 18+ months Deleted/banned accounts Test accounts from 2023 Orphaned session data Problem root cause: Missing TTL (Time To Live) on cache entries. # ❌ Wrong - lives forever redis.set(cache_key, json.dumps(data)) # ✅ Correct - expires after 1 hour redis.setex(cache_key, 3600, json.dumps(data)) Impact: Set appropriate TTLs → 40% memory reduction immediately. ...

Executive Summary This document analyzes a three-month production deployment of PostgreSQL JSONB columns, documenting performance issues encountered, indexing strategies implemented, and architectural patterns that proved effective. The findings are relevant for teams considering JSONB for schema flexibility in high-traffic applications. Key Metrics: Initial table size: 2M records Average JSONB column size: 50KB Write throughput: 10K updates/minute Query degradation: 200ms → 14s (70x regression) Background and Context In Q4 2025, our engineering team migrated user preference storage from normalized columns to JSONB format. Primary motivation was reducing schema migration overhead as product requirements evolved. ...

Step-by-Step Guide: Optimizing Docker Build Performance Introduction Problem Statement: Docker builds taking 8+ minutes per PR, causing CI bottlenecks and increased costs. Solution Overview: Reduce build time to 40 seconds through multi-stage builds, BuildKit caching, and proper layer optimization. What You’ll Need: Docker 20.10+ (for BuildKit support) Basic understanding of Dockerfile syntax A Next.js or Node.js application (examples are adaptable to other frameworks) Expected Time Investment: 2-3 hours for implementation and testing ...

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.