Synchronous programming executes tasks sequentially, blocking subsequent operations until the current one completes, which can lead to inefficiencies in I/O-bound processes. Asynchronous programming allows tasks to run concurrently, freeing up resources while waiting for operations like database queries or API calls to finish. This non-blocking approach improves scalability and performance, especially in high-traffic applications. Back-end systems often use async patterns (e.g., callbacks, promises, or async/await) to handle multiple requests efficiently. For example, Node.js leverages an event loop for async execution, while Java employs threads or reactive frameworks like Spring WebFlux. Choosing between sync and async depends on use-case requirements like latency tolerance and resource constraints.

To design a scalable RESTful API, I’d follow REST principles: use resource-based URLs (e.g., `/products/{id}`), standard HTTP methods (GET, POST), and stateless interactions. I’d implement pagination (`/products?limit=20&offset=40`) and caching (via ETags or Redis) to reduce server load. Authentication would use JWT or OAuth2, and responses would be in JSON with HATEOAS links for discoverability. Microservices architecture would decouple functionalities (e.g., orders, inventory) for independent scaling. Rate limiting and API gateways (like Kong) would manage traffic. Database choices would include PostgreSQL for ACID transactions and read replicas for performance. Monitoring (Prometheus) and CI/CD pipelines would ensure reliability and rapid iterations.

Database optimization starts with indexing frequently queried columns (e.g., user IDs) to speed up searches. I’d normalize schemas to reduce redundancy but denormalize selectively for read-heavy workloads. Query optimization involves analyzing slow logs and avoiding N+1 queries via joins or batch fetching. Caching (Redis/Memcached) mitigates repeated read loads, while connection pooling manages concurrent requests. For writes, I’d consider sharding or partitioning by logical boundaries (e.g., geographic regions). Replication (master-slave) distributes read traffic, and NoSQL (like MongoDB) may suit unstructured data. Regular maintenance (vacuuming, reindexing) and monitoring (pg_stat_statements in PostgreSQL) ensure sustained performance under load.

To prevent SQL injection, I’d use parameterized queries or ORMs (e.g., Hibernate, Sequelize) that sanitize inputs automatically. Input validation (whitelisting) and least-privilege database roles further reduce risks. For XSS, output encoding (e.g., HTML escaping via libraries like OWASP ESAPI) neutralizes malicious scripts. CSRF tokens protect form submissions, while CORS policies restrict unauthorized domains. HTTPS encrypts data in transit, and security headers (Content-Security-Policy) harden responses. Regular dependency updates (e.g., npm audit) patch vulnerabilities. Logging and intrusion detection systems (IDS) help identify breaches. Security audits and penetration testing validate defenses, ensuring compliance with standards like OWASP Top 10.

To debug a Node.js memory leak, I’d first monitor heap usage via Chrome DevTools’ Memory tab or `process.memoryUsage()`. I’d identify growing memory patterns under load. Heap snapshots (using `heapdump`) help compare object allocations between intervals, pinpointing retained objects. Common culprits include global variables, unclosed event listeners, or circular references. I’d use `--inspect` flag for real-time profiling and `clinic.js` for automated diagnostics. Leaky middleware or dangling promises would be audited with async_hooks or APM tools (Datadog). Fixes involve proper cleanup (e.g., `removeListener`), streaming large data to avoid buffer overload, and GC tuning (`--max-old-space-size`). Load testing post-fix ensures resolution.