Jun 18, 2025
rust
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.
ReadMay 12, 2025
go
Client defaults Set Timeout on http.Client; set Transport with DialContext timeout (e.g., 3s), TLSHandshakeTimeout (3s), ResponseHeaderTimeout (5s), IdleConnTimeout (90s), MaxIdleConns/MaxIdleConnsPerHost. Retry only idempotent methods with backoff + jitter; cap attempts. Use context.WithTimeout per request; cancel on exit. Server defaults ReadHeaderTimeout (e.g., 5s) to mitigate slowloris. ReadTimeout/WriteTimeout to bound handler time (align with business SLAs). IdleTimeout to recycle idle connections; prefer HTTP/2 when available. Patterns Wrap handlers with middleware for deadline + logging when timeouts hit. For upstreams, expose metrics: connect latency, TLS handshake, TTFB, retries. Prefer connection re-use; avoid per-request clients. Checklist Timeouts set on both client and server. Retries limited to idempotent verbs with jitter. Connection pooling tuned; idle conns reused. Metrics for latency stages and timeouts.
ReadApr 2, 2025
go
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.
ReadMar 18, 2025
node
When to use CPU-bound tasks (crypto, compression, image/video processing) that would block event loop. Avoid for short, tiny tasks—overhead may outweigh benefit. Patterns Use a pool (e.g., piscina/workerpool) with bounded size; queue tasks. Pass data via Transferable when large buffers; avoid heavy serialization. Propagate cancellation/timeouts; surface errors to main thread. Observability Track queue length, task duration, worker utilization, crashes. Monitor event loop lag to confirm offload benefits. Safety Validate inputs in main thread; avoid untrusted code in workers. Cleanly shut down pool on SIGTERM; drain and close workers. Checklist CPU tasks isolated to workers; pool sized to cores. Transferables used for big buffers; timeouts set. Metrics for queue/utilization/errors in place.
ReadMar 5, 2025
kafka
Producers acks=all, min.insync.replicas>=2, idempotent producer on; enable transactions for exactly-once pipelines. Tune batch.size/linger.ms for throughput; cap max.in.flight.requests.per.connection for ordering. Handle retries with backoff; surface delivery errors. Consumers enable.auto.commit=false; commit after processing; DLT for poison messages. Size max.poll.interval.ms to work time; bound max.poll.records. Isolate heavy work in worker pool; keep poll loop fast. Topics & brokers RF ≥ 3; clean-up policy fit (delete vs compact); segment/retention sized to storage. Monitor ISR, under-replicated partitions, controller changes, request latency, disk usage. Throttle large produce/fetch; use quotas per client if needed. Checklist Producers idempotent, acks=all, MISR set. Consumers manual commit + DLT. RF/retention sized; ISR/URP monitored. Alerts on broker latency/disk/replication health.
ReadMar 2, 2025
node
Performance notes Fastify: schema-driven, AJV validation, low-overhead routing; typically better RPS and lower p99s. Express: mature ecosystem; middleware can add overhead; great for quick prototypes. Bench considerations Compare with same validation/parsing; disable unnecessary middleware in Express. Test under keep-alive and HTTP/1.1 & HTTP/2 where applicable. Measure event loop lag and heap; not just RPS. Migration tips Start new services with Fastify; for existing Express apps, migrate edge routes first. Replace middleware with hooks/plugins; map Express middlewares to Fastify equivalents. Validate payloads with JSON schema for speed and safety. Operational tips Use pino (Fastify default) for structured logs; avoid console log overhead. Keep plugin count minimal; watch async hooks cost. Load test with realistic payload sizes; profile hotspots before/after migration. Takeaway: Fastify wins on raw throughput and structured DX; Express still fine for smaller apps, but performance-critical services benefit from Fastify’s lower overhead.
ReadFeb 15, 2025
java
Approaches Token bucket for burst+steady control; sliding window for fairness. Enforce at edge (gateway/ingress) plus app-level for per-tenant safety. Spring implementation Use filters/interceptors with Redis/Lua for atomic buckets. Key by tenant/user/IP; return 429 with Retry-After. Expose metrics per key and rule; alert on near-capacity. Considerations Separate auth failures from rate limits; avoid blocking login endpoints too aggressively. Keep rule configs dynamic; hot-reload from config store. Combine with circuit breakers/timeouts for upstream dependencies. Checklist Edge and app-level limits defined. Redis-based atomic counters/buckets with TTL. Metrics + logs for limit decisions; alerts in place.
ReadFeb 10, 2025
go
Principles Keep goroutine count bounded; size pools to CPU/core and downstream QPS. Apply backpressure: bounded channels + select with default to shed load early. Context everywhere: cancel on timeouts/parent cancellation; close resources. Prefer immutability; minimize shared state. Use channels for coordination. Patterns Worker pool with buffered jobs; fan-out/fan-in via contexts. Rate limit with time.Ticker or golang.org/x/time/rate. Semaphore via buffered channel for limited resources (DB, disk, external API). Sync cheatsheet sync.Mutex for critical sections; avoid long hold times. sync.WaitGroup for bounded concurrent tasks; errgroup for cancellation on first error. sync.Map only for high-concurrency, write-light cases; prefer map+mutex otherwise. Instrument & guard pprof: net/http/pprof + CPU/mem profiles in staging under load. Trace blocking: GODEBUG=schedtrace=1000,scavtrace=1 when diagnosing. Metrics: goroutines, GC pause, allocations, queue depth, worker utilization. Checklist Context per request; timeouts set at ingress. Bounded goroutines; pools sized and observable. Backpressure on queues; drop/timeout strategy defined. pprof/metrics enabled in non-prod and behind auth in prod. Load tests for saturation behavior and graceful degradation.
ReadFeb 8, 2025
cicd
Metrics Lead time, MTTR, change failure rate, deploy frequency. Stage timing (queue, build, test, deploy); flake rate; retry counts. Tracing & logs Trace pipeline executions with build SHA, branch, trigger source; annotate stage spans. Structured logs with status, duration, infra node; keep artifacts linked. Guardrails Quality gates (tests, lint, security scans) per PR; fail fast on criticals. Retry budget per job to avoid infinite flake loops. Rollback hooks + auto-stop on repeated failures. Ops Parallelize where safe; cache dependencies; pin tool versions. Alert on SLA breaches (queue time, total duration) and rising flake rates. Keep dashboards per repo/team; trend regressions release to release.
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