Oct 20, 2024
database
Database performance is critical for application scalability. Here are proven optimization techniques.
1. Indexing Strategy When to Index -- Index frequently queried columns CREATE INDEX idx_user_email ON users(email); -- Index foreign keys CREATE INDEX idx_post_user_id ON posts(user_id); -- Composite indexes for multi-column queries CREATE INDEX idx_user_status_role ON users(status, role); When NOT to Index Columns with low cardinality (few unique values) Frequently updated columns Small tables (< 1000 rows) 2. Query Optimization Avoid SELECT * -- Bad SELECT * FROM users WHERE id = 123; -- Good SELECT id, name, email FROM users WHERE id = 123; Use LIMIT -- Always limit large result sets SELECT * FROM posts ORDER BY created_at DESC LIMIT 20; Avoid N+1 Queries // Bad: N+1 queries users.forEach(user => { const posts = db.query('SELECT * FROM posts WHERE user_id = ?', [user.id]); }); // Good: Single query with JOIN const usersWithPosts = db.query(` SELECT u.*, p.* FROM users u LEFT JOIN posts p ON u.id = p.user_id `); 3. Connection Pooling // Configure connection pool const pool = mysql.createPool({ connectionLimit: 10, host: 'localhost', user: 'user', password: 'password', database: 'mydb', waitForConnections: true, queueLimit: 0 }); 4. Caching Application-Level Caching // Cache frequently accessed data const cache = new Map(); async function getUser(id) { if (cache.has(id)) { return cache.get(id); } const user = await db.query('SELECT * FROM users WHERE id = ?', [id]); cache.set(id, user); return user; } Query Result Caching -- Use query cache (MySQL) SET GLOBAL query_cache_size = 67108864; SET GLOBAL query_cache_type = 1; 5. Database Schema Optimization Normalize Properly -- Avoid over-normalization -- Balance between normalization and performance Use Appropriate Data Types -- Use smallest appropriate type TINYINT instead of INT for small numbers VARCHAR(255) instead of TEXT when possible DATE instead of DATETIME when time not needed 6. Partitioning -- Partition large tables by date CREATE TABLE logs ( id INT, created_at DATE, data TEXT ) PARTITION BY RANGE (YEAR(created_at)) ( PARTITION p2023 VALUES LESS THAN (2024), PARTITION p2024 VALUES LESS THAN (2025), PARTITION p2025 VALUES LESS THAN (2026) ); 7. Query Analysis EXPLAIN Plan EXPLAIN SELECT * FROM users WHERE email = '[email protected]'; Slow Query Log -- Enable slow query log SET GLOBAL slow_query_log = 'ON'; SET GLOBAL long_query_time = 1; 8. Batch Operations // Bad: Multiple individual inserts users.forEach(user => { db.query('INSERT INTO users (name, email) VALUES (?, ?)', [user.name, user.email]); }); // Good: Batch insert const values = users.map(u => [u.name, u.email]); db.query('INSERT INTO users (name, email) VALUES ?', [values]); 9. Database Maintenance Regular Vacuuming (PostgreSQL) VACUUM ANALYZE; Optimize Tables (MySQL) OPTIMIZE TABLE users; 10. Monitoring Monitor query performance Track slow queries Monitor connection pool usage Watch for table locks Monitor disk I/O Best Practices Index strategically Optimize queries Use connection pooling Implement caching Normalize appropriately Use appropriate data types Partition large tables Analyze query performance Batch operations Regular maintenance Conclusion Database optimization requires:
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ReadOct 18, 2024
rabbitmq
Queue types Prefer quorum queues for HA; classic for transient/high-throughput if loss acceptable. Set durability/persistence appropriately; avoid auto-delete for critical flows. Flow control Enable publisher confirms; set mandatory flag to catch unroutable messages. Use basic.qos to bound unacked messages; prefetch tuned per consumer. Watch memory/flow events; avoid oversized messages—use blob storage for big payloads. Topology & ops Mirror/quorum across AZs; avoid single-node SPOF. Use consistent hash/partitioning for hot-key spreading. Metrics: publish/consume rates, unacked count, queue depth, confirm latency, blocked connections. Checklist Queue type chosen (quorum vs classic) per workload. Publisher confirms + unroutable handling. Prefetch/qos tuned; consumers idempotent. Monitoring/alerts on depth, unacked, flow control.
ReadOct 5, 2024
java
Choosing G1: balanced latency/throughput for heaps 4–64GB; predictable pauses. ZGC: sub-10ms pauses on large heaps; great for latency-sensitive APIs; slightly higher CPU. Baseline flags G1: -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:+ParallelRefProcEnabled -XX:+AlwaysPreTouch ZGC: -XX:+UseZGC -XX:+ZGenerational -XX:+AlwaysPreTouch Set -Xms = -Xmx for stable footprint; size heap from prod RSS data. Metrics to watch Pause p95/p99, GC CPU %, allocation rate, remembered set size (G1), heap occupancy. STW reasons: promotion failure, humongous allocations (G1), metaspace growth. Common fixes Reduce humongous allocations: avoid giant byte[]; use chunked buffers. Lower pause targets only after measuring; avoid over-constraining MaxGCPauseMillis. Cap thread counts: -XX:ParallelGCThreads, -XX:ConcGCThreads if CPU saturated. For ZGC, ensure kernel pages hugepage-friendly; watch NUMA pinning. Checklist Heap sized from live data; -Xms = -Xmx. GC logs on (JDK 17+): -Xlog:gc*:tags,level,time,uptime:file=gc.log:utctime,filesize=20M,files=10 Dashboards for pause/CPU/allocation. Load test changes before prod; compare pause histograms release to release.
ReadSep 30, 2024
go
Sizing Pool size ≈ CPU cores * 2–4 per service instance; avoid per-request opens. For PgBouncer tx-mode: disable session features; avoid session-prepared statements. Timeouts & limits Set ConnMaxLifetime, ConnMaxIdleTime, MaxOpenConns, MaxIdleConns. Add statement timeouts; enforce context deadlines on queries. Instrumentation Track pool acquire latency, in-use/idle, wait count, timeouts. Log slow queries; sample EXPLAIN ANALYZE in staging for heavy ones. Hygiene Use prepared statements judiciously; reuse sqlx.Named/pgx prepared for hot paths. Prefer keyset pagination; cap result sizes; parameterize everything. Checklist Pool sized and monitored. Query timeouts set; slow logs reviewed. No per-request connections; connections closed via context cancellation.
ReadSep 18, 2024
node
Phases refresher timers → pending → idle/prepare → poll → check → close callbacks. Microtasks (Promises/queueMicrotask) run after each phase; process.nextTick runs before microtasks. Pitfalls Long JS on main thread blocks poll → delays I/O; move CPU work to worker threads. nextTick storms can starve I/O; prefer setImmediate when deferring. Unhandled rejections crash (from Node 15+ default); handle globally in prod. Debugging NODE_DEBUG=async_hooks or --trace-events-enabled --trace-event-categories node.async_hooks. Measure event loop lag: perf_hooks.monitorEventLoopDelay(). Profile CPU with node --inspect + Chrome DevTools; use flamegraphs for hotspots. Practices Limit synchronous JSON/crypto/zlib; offload to worker threads or native modules. Keep microtask chains short; avoid deep promise recursion. Use AbortController for cancellable I/O; always clear timers. Checklist Monitor event loop lag & heap usage. Worker pool sized for CPU tasks; main loop kept light. Errors and rejections centrally handled; graceful shutdown in place.
ReadSep 10, 2024
architecture
Choosing between microservices and monolithic architecture is a critical decision. Here’s how to make the right choice.
Monolithic Architecture Characteristics Single codebase Single deployment unit Shared database Tightly coupled components Advantages // Simple development // Easy to test // Straightforward deployment // Shared code and data Disadvantages Hard to scale individual components Technology lock-in Deployment risk (all or nothing) Difficult to maintain as it grows Microservices Architecture Characteristics Multiple independent services Separate deployments Service-specific databases Loosely coupled via APIs Advantages Independent scaling Technology diversity Fault isolation Team autonomy Disadvantages Increased complexity Network latency Data consistency challenges Operational overhead When to Choose Monolith Start with Monolith If: Small team (< 10 developers) Simple application Rapid prototyping Limited resources Unclear requirements // Good for startups // MVP development // Small to medium applications When to Choose Microservices Consider Microservices If: Large team (> 50 developers) Complex domain Different scaling needs Multiple teams Clear service boundaries // Good for large organizations // Complex systems // Different technology needs Migration Strategy Strangler Fig Pattern // Gradually replace monolith // Extract services incrementally // Keep monolith running during migration Steps Identify service boundaries Extract one service at a time Maintain backward compatibility Monitor and iterate Best Practices For Monoliths Keep modules loosely coupled Use clear boundaries Plan for future extraction Maintain clean architecture For Microservices Define clear service boundaries Implement proper API contracts Use service mesh for communication Implement distributed tracing Handle failures gracefully Common Pitfalls Microservices Anti-patterns Too many small services Distributed monolith Shared databases Synchronous communication everywhere Monolith Anti-patterns God classes Tight coupling No module boundaries Big ball of mud Conclusion Start with monolith when:
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ReadSep 10, 2024
elasticsearch
Elasticsearch is a powerful search and analytics engine, but optimizing it for production requires understanding indexing strategies, query patterns, and cluster configuration. This guide covers essential optimization techniques.
Cluster architecture Node roles Configure nodes with specific roles:
# Master node node.roles: [master] # Data node node.roles: [data] # Ingest node node.roles: [ingest] # Coordinating node (default) node.roles: [] # No specific role Shard strategy Primary shards: Set at index creation (cannot be changed)
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ReadSep 5, 2024
security
Static (SAST) Run linters/semgrep/bandit/gosec/spotbugs per language; fail on high severity. Baseline to reduce noise; allow sarif upload to code host for inline review. Secret scanning in every push; block commits with keys. Dynamic (DAST) Stage environment with prod-like config; run zap/owasp/amass scopes carefully. Define allowlist targets; time-bounded scans; throttle to avoid DoS. Pipeline hygiene Shift-left: PR checks for fast SAST; nightly deeper scans. Artifacts stored with build SHA; track findings trend. Break-glass only with approvals; keep rules versioned. Checklist SAST/secret scans on push + PR. DAST on staging with scoped targets. SARIF/uploaded results visible to devs. Findings triaged with SLA.
ReadAug 21, 2024
cache
Choosing engine Redis: rich data types, persistence, clustering, scripts; good for queues/rate limits. Memcached: simple KV, pure in-memory, fast; great for stateless caches. Design Namespaced keys, versioned; TTL with jitter; avoid giant values. Pick eviction (LRU/LFU); monitor hit rate and evictions. Stampede control: locks/SETNX, stale-while-revalidate, per-key backoff. Ops Pooling + timeouts; alert on latency, evictions, memory fragmentation. For Redis, persistence config (AOF/RDB) matched to durability needs; replicas for HA. For Memcached, spread slabs; watch for evictions of hot slabs. Checklist Engine chosen per use case; durability/HA decided. TTLs + jitter; stampede protection in place. Metrics for hit/miss/evictions/latency.
ReadAug 15, 2024
security
Security is crucial in web development. Here are essential security practices to protect your applications.
1. Authentication & Authorization Strong Password Policies // Enforce strong passwords const passwordRegex = /^(?=.*[a-z])(?=.*[A-Z])(?=.*\d)(?=.*[@$!%*?&])[A-Za-z\d@$!%*?&]{8,}$/; // Hash passwords (never store plaintext) const hashedPassword = await bcrypt.hash(password, 10); JWT Best Practices // Use short expiration times const token = jwt.sign( { userId: user.id }, process.env.JWT_SECRET, { expiresIn: '15m' } ); // Implement refresh tokens // Store tokens securely (httpOnly cookies) 2. Input Validation Server-Side Validation // Always validate on server const schema = z.object({ email: z.string().email(), password: z.string().min(8), age: z.number().min(18).max(120) }); const validated = schema.parse(req.body); Sanitize Inputs // Prevent XSS const sanitized = DOMPurify.sanitize(userInput); // Prevent SQL Injection (use parameterized queries) db.query('SELECT * FROM users WHERE id = ?', [userId]); 3. HTTPS & SSL/TLS // Always use HTTPS in production // Redirect HTTP to HTTPS // Use HSTS headers app.use((req, res, next) => { if (!req.secure && process.env.NODE_ENV === 'production') { return res.redirect(`https://${req.headers.host}${req.url}`); } next(); }); 4. CORS Configuration // Configure CORS properly app.use(cors({ origin: process.env.ALLOWED_ORIGINS.split(','), credentials: true, methods: ['GET', 'POST', 'PUT', 'DELETE'], allowedHeaders: ['Content-Type', 'Authorization'] })); 5. Rate Limiting // Prevent brute force attacks const rateLimit = require('express-rate-limit'); const limiter = rateLimit({ windowMs: 15 * 60 * 1000, // 15 minutes max: 5 // limit each IP to 5 requests per windowMs }); app.use('/api/login', limiter); 6. SQL Injection Prevention // Always use parameterized queries // Bad db.query(`SELECT * FROM users WHERE email = '${email}'`); // Good db.query('SELECT * FROM users WHERE email = ?', [email]); 7. XSS Prevention // Escape user input const escapeHtml = (text) => { const map = { '&': '&', '<': '<', '>': '>', '"': '"', "'": ''' }; return text.replace(/[&<>"']/g, m => map[m]); }; // Use Content Security Policy app.use((req, res, next) => { res.setHeader('Content-Security-Policy', "default-src 'self'; script-src 'self'"); next(); }); 8. CSRF Protection // Use CSRF tokens const csrf = require('csurf'); const csrfProtection = csrf({ cookie: true }); app.use(csrfProtection); app.get('/form', (req, res) => { res.render('form', { csrfToken: req.csrfToken() }); }); 9. Security Headers // Set security headers app.use((req, res, next) => { res.setHeader('X-Content-Type-Options', 'nosniff'); res.setHeader('X-Frame-Options', 'DENY'); res.setHeader('X-XSS-Protection', '1; mode=block'); res.setHeader('Strict-Transport-Security', 'max-age=31536000'); next(); }); 10. Dependency Security # Regularly update dependencies npm audit npm audit fix # Use tools like Snyk, Dependabot 11. Error Handling // Don't expose sensitive information app.use((err, req, res, next) => { if (process.env.NODE_ENV === 'production') { res.status(500).json({ error: 'Internal server error' }); } else { res.status(500).json({ error: err.message }); } }); 12. Logging & Monitoring // Log security events logger.warn('Failed login attempt', { ip: req.ip, email: req.body.email, timestamp: new Date() }); // Monitor for suspicious activity Best Practices Summary Strong authentication Input validation & sanitization Use HTTPS Configure CORS properly Implement rate limiting Prevent SQL injection Prevent XSS CSRF protection Security headers Keep dependencies updated Proper error handling Monitor security events Conclusion Security requires:
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