1. Load Balancing: Distribute workloads evenly across servers to avoid bottlenecks and improve system reliability. 2. Effective Caching: Use caching at strategic layers (e.g., in-memory or edge caching) to reduce data retrieval overhead. 3. Optimize Algorithms: Refactor algorithms to minimize complexity and resource consumption. 4. Horizontal Scaling: Add more nodes to handle increased loads instead of overburdening existing ones. 5. Rate Limiting: Control resource usage by limiting requests from individual clients. 6. Asynchronous Processing: Offload non-critical tasks to background processes to keep the system responsive.

The magic answer, it depends. Sometimes caching will not do much impact, changing algorithms is so intrusive and can cause errors, load balancing is useful only for scaling and can cause a slowness if the instances uses the same resources like db... Performance is a mindset to adopt during dev, respected during code reviews and verified with load tests. Performance is often related to architecture decisions, choosing the wrong tool/design can be unfixable, it's always tightly coupled withe the app/service context, like the number of users/requests, the SLA, the response time...

When resources are tight in a distributed system, every decision matters. I focus on three things: Reduce Unnecessary Work – Cache intelligently, minimize redundant computations, and compress data before transfer. Balance Load Effectively – Distribute tasks efficiently across nodes, use asynchronous processing, and avoid bottlenecks. Profile & Optimize Critical Paths – Identify slow operations, fine-tune database queries, and prioritize optimizations where they matter most. Small, focused improvements make a big difference.

In a distributed system with resource constraints, optimization is about balancing efficiency and scalability. One key approach is load balancing—distributing traffic intelligently across nodes to prevent bottlenecks. Caching frequently accessed data (e.g., using Redis) reduces repeated expensive queries. Vertical scaling (adding more power to existing nodes) works, but horizontal scaling (adding more nodes) is often more resilient. Rate limiting protects critical resources from overload. For example, in a large e-commerce platform, caching product details at the edge reduces database hits, ensuring smooth performance during peak sales events. Fine-tuning these strategies helps maximize efficiency under constraints.

Prioritize smart load balancing using real-time metrics (CPU, latency, errors) to avoid bottlenecks. Push non-critical tasks (jobs, webhooks, notifications) to async queues. Continuously profile and optimize code and DB queries to catch performance leaks early. Use auto-scaling and self-healing infra (Kubernetes, serverless) to handle dynamic demand. Make decisions based on real metrics — Prometheus, Grafana, and flame graphs reveal real bottlenecks.