Performance

Deadlines & retries Require client deadlines; enforce server-side context with grpc.DeadlineExceeded handling. Configure retry/backoff on idempotent calls; avoid retry storms with jitter + max attempts. Interceptors Unary/stream interceptors for auth, metrics (Prometheus), logging, and panic recovery. Use per-RPC circuit breakers and rate limits for critical dependencies. TLS & auth Enable TLS everywhere; prefer mTLS for internal services. Rotate certs automatically; watch expiry metrics. Add authz checks in interceptors; propagate identity via metadata. Resource protection Limit concurrent streams and max message sizes. Bounded worker pools for handlers performing heavy work. Tune keepalive to detect dead peers without flapping. Observability Metrics: latency, error codes, message sizes, active streams, retries. Traces: annotate methods, peer info, attempt counts; sample smartly. Logs: structured fields for method, code, duration, peer. Checklist Deadlines required; retries only for idempotent calls with backoff. Interceptors for auth/metrics/logging/recovery. TLS/mTLS enabled; cert rotation automated. Concurrency and message limits set; keepalive tuned.

Process manager modes pm=dynamic for most apps; pm=static only when workload is predictable and memory bounded. Key knobs: pm.max_children, pm.start_servers, pm.min_spare_servers, pm.max_spare_servers. Size max_children = (available RAM - OS/webserver/DB) / avg worker RSS. Opcache Enable: opcache.enable=1, opcache.enable_cli=0, opcache.memory_consumption sized for codebase, opcache.interned_strings_buffer, opcache.max_accelerated_files. Avoid opcache.revalidate_freq=0 in prod unless you control deploy restarts; prefer deploy-triggered reloads. Timeouts & queues Keep request_terminate_timeout sane (e.g., 30s-60s); long requests move to queues. Use pm.max_requests to recycle leaky workers (e.g., 500-2000). Watch slowlog to catch blocking I/O or heavy CPU. Observability Export status_path and scrape: active/idle/slow requests, max children reached. Correlate with Nginx/Apache logs for upstream latency and 502/504s. Alert on max children reached, slowlog entries, and rising worker RSS. Checklist Pool sizing validated under load test. Opcache enabled and sized; reload on deploy. Timeouts/queues tuned; slowlog on. Status endpoint protected and scraped.

The N+1 problem is a common performance issue in JPA. Here’s how to solve it in Spring Data JPA. Understanding N+1 Problem // N+1 queries: 1 for users + N for each user's posts List<User> users = userRepository.findAll(); for (User user : users) { List<Post> posts = postRepository.findByUserId(user.getId()); // N queries } Solutions 1. Eager Fetching @Entity public class User { @OneToMany(fetch = FetchType.EAGER) private List<Post> posts; } 2. Join Fetch (JPQL) @Query("SELECT u FROM User u JOIN FETCH u.posts") List<User> findAllWithPosts(); 3. Entity Graphs @EntityGraph(attributePaths = {"posts"}) @Query("SELECT u FROM User u") List<User> findAllWithPosts(); 4. Batch Fetching @Entity public class User { @BatchSize(size = 10) @OneToMany(fetch = FetchType.LAZY) private List<Post> posts; } Best Practices Use lazy loading by default Fetch associations when needed Use entity graphs Monitor query performance Use DTO projections Conclusion Solve N+1 problems with: ...

This article summarizes the DEV post “Key Performance Metrics for Web Apps and How to Measure Them,” focusing on the most important signals and how to capture them. Core metrics LCP (Largest Contentful Paint): loading speed; target < 2.5s p75. INP/TTI (Interaction to Next Paint / Time to Interactive): interactivity; target INP < 200ms p75. FCP (First Contentful Paint): first visual response. TTFB (Time to First Byte): server responsiveness. Bundle size & request count: total transferred bytes and requests. Measurement toolbox Lighthouse: automated audits; budgets and suggestions. WebPageTest: multi-location runs, filmstrips, waterfalls. RUM (GA or custom): real-user timing for live traffic. React profiler & perf tools: find slow renders/update frequency. Webpack/Vite bundle analyzer: visualize bundle composition and dead weight. Optimization reminders Ship less JS/CSS; enable tree shaking/code splitting. Compress and cache static assets; serve modern image formats. Trim request count; inline critical CSS for hero; defer non-critical JS. Watch layout stability; reserve space to avoid CLS hits. Set and enforce budgets (JS gz < 200KB, LCP < 2.5s p75, INP < 200ms). Takeaway: Track a small, high-signal set of metrics with both lab (Lighthouse/WPT) and field (RUM) data, then enforce budgets so regressions fail fast.

Optimizing React applications is crucial for better user experience. Here are proven techniques. 1. Memoization React.memo const ExpensiveComponent = React.memo(({ data }) => { return <div>{processData(data)}</div>; }, (prevProps, nextProps) => { return prevProps.data.id === nextProps.data.id; }); useMemo const expensiveValue = useMemo(() => { return computeExpensiveValue(a, b); }, [a, b]); useCallback const handleClick = useCallback(() => { doSomething(id); }, [id]); 2. Code Splitting React.lazy const LazyComponent = React.lazy(() => import('./LazyComponent')); function App() { return ( <Suspense fallback={<div>Loading...</div>}> <LazyComponent /> </Suspense> ); } 3. Virtualization import { FixedSizeList } from 'react-window'; function VirtualizedList({ items }) { return ( <FixedSizeList height={600} itemCount={items.length} itemSize={50} > {({ index, style }) => ( <div style={style}>{items[index]}</div> )} </FixedSizeList> ); } 4. Avoid Unnecessary Renders // Bad: Creates new object on every render <ChildComponent config={{ theme: 'dark' }} /> // Good: Use useMemo or constant const config = useMemo(() => ({ theme: 'dark' }), []); <ChildComponent config={config} /> Best Practices Memoize expensive computations Split code by routes Virtualize long lists Avoid inline functions/objects Use production builds Conclusion Optimize React apps for better performance! ⚡

Redis is powerful for caching. Here are effective caching strategies. Cache-Aside Pattern async function getUser(id) { // Check cache let user = await redis.get(`user:${id}`); if (user) { return JSON.parse(user); } // Cache miss - fetch from DB user = await db.query('SELECT * FROM users WHERE id = ?', [id]); // Store in cache await redis.setex(`user:${id}`, 3600, JSON.stringify(user)); return user; } Write-Through Pattern async function updateUser(id, data) { // Update database const user = await db.update('users', id, data); // Update cache await redis.setex(`user:${id}`, 3600, JSON.stringify(user)); return user; } Cache Invalidation async function deleteUser(id) { // Delete from database await db.delete('users', id); // Invalidate cache await redis.del(`user:${id}`); } Best Practices Set appropriate TTL Handle cache misses Invalidate properly Monitor cache hit rate Use consistent key patterns Conclusion Implement effective Redis caching strategies! 🔴

Optimizing MongoDB queries is essential for performance. Here’s how. Indexing Create Indexes // Single field index db.users.createIndex({ email: 1 }); // Compound index db.users.createIndex({ status: 1, createdAt: -1 }); // Text index db.posts.createIndex({ title: "text", content: "text" }); Explain Queries db.users.find({ email: "[email protected]" }).explain("executionStats"); Query Optimization Use Projection // Bad: Fetches all fields db.users.find({ status: "active" }); // Good: Only needed fields db.users.find( { status: "active" }, { name: 1, email: 1, _id: 0 } ); Limit Results db.users.find({ status: "active" }) .limit(10) .sort({ createdAt: -1 }); Best Practices Create appropriate indexes Use projection Limit result sets Avoid $regex without index Monitor slow queries Conclusion Optimize MongoDB for better performance! 🍃

PostgreSQL performance tuning requires understanding configuration and queries. Here’s a guide. Configuration Tuning postgresql.conf # Memory settings shared_buffers = 256MB effective_cache_size = 1GB work_mem = 16MB # Connection settings max_connections = 100 # Query planner random_page_cost = 1.1 Query Optimization Use EXPLAIN ANALYZE EXPLAIN ANALYZE SELECT * FROM users WHERE email = '[email protected]'; Indexing -- B-tree index CREATE INDEX idx_user_email ON users(email); -- Partial index CREATE INDEX idx_active_users ON users(email) WHERE status = 'active'; -- Composite index CREATE INDEX idx_user_status_created ON users(status, created_at); Best Practices Analyze query plans Create appropriate indexes Use connection pooling Monitor slow queries Vacuum regularly Conclusion Tune PostgreSQL for optimal performance! 🐘

Rust WebAssembly can significantly improve React application performance. Here’s how to integrate it. Setup cargo install wasm-pack wasm-pack build --target web Rust Code use wasm_bindgen::prelude::*; #[wasm_bindgen] pub fn add(a: i32, b: i32) -> i32 { a + b } React Integration import init, { add } from './pkg/rust_wasm.js'; async function loadWasm() { await init(); console.log(add(2, 3)); // 5 } Performance Benefits Faster computation for heavy operations Memory efficient Type safe Near-native performance Best Practices Use for CPU-intensive tasks Minimize data transfer Profile performance Handle errors properly Bundle efficiently Conclusion Boost React performance with Rust WebAssembly! ⚡