System Design

Understand the difference between horizontal (scale-out) and vertical (scale-up) scaling, their trade-offs, and when to use each approach.

Interview-focused load balancing: L4 vs L7, health checks, algorithms, and scaling/reliability trade-offs.

Caching patterns and trade-offs for interviews: invalidation, consistency, multi-layer caches, and failure modes.

Use Redis for caching: keys/TTLs, invalidation strategies, replication, clustering, hot keys, and cache stampede prevention.

Master database sharding strategies: horizontal partitioning, shard key selection, cross-shard queries, rebalancing, and handling failures in sharded systems.

Learn consistent hashing for distributed systems. Understand how it enables efficient data distribution, handles node failures, and minimizes data movement duri

Master replication models (primary-replica, multi-primary), sync vs async trade-offs, replication lag, read consistency, and failover.

Learn database indexing strategies: B-tree, hash indexes, composite indexes, covering indexes, and how to design indexes for optimal query performance.

Master the CAP theorem: understand why you can only guarantee two of Consistency, Availability, and Partition tolerance. Learn real-world trade-offs and example

Compare monolithic and microservices architectures. Understand when to choose each, migration strategies, and the trade-offs involved in architectural decisions

Master microservices architecture: service decomposition, communication patterns, data management, deployment strategies, and challenges like distributed transa

Understand API gateways as the single entry point for microservices. Learn routing, authentication, rate limiting, request/response transformation, and circuit

Compare message queue systems: Kafka for event streaming vs RabbitMQ for traditional messaging. Learn use cases, patterns, and when to choose each.

Master event-driven architecture: event sourcing, CQRS, event streaming, event choreography vs orchestration, and building scalable event-driven systems.

Learn the circuit breaker pattern for fault tolerance. Understand states (closed, open, half-open), implementation, and preventing cascading failures.

Design safe retries: exponential backoff + jitter, timeouts, idempotency, circuit breaking, and avoiding retry storms.

Learn rate limiting algorithms (token/leaky bucket, sliding window), distributed enforcement, Redis patterns, and common failure modes.

Understand Content Delivery Networks (CDNs), how they reduce latency, edge caching strategies, cache invalidation, and designing a CDN architecture.

Understand reverse proxies (Nginx/Envoy): TLS termination, routing, load balancing, caching, auth, and common deployment patterns.

Design for high availability: redundancy, failover strategies, health checks, circuit breakers, and achieving 99.9%+ uptime in distributed systems.

Compare REST and GraphQL for real APIs: performance, caching, versioning, schema evolution, and operational complexity.

Master CQRS (Command Query Responsibility Segregation): separate read and write models, when to use it, and implementation patterns.

Learn event sourcing: storing state changes as events, rebuilding state, event replay, and when event sourcing is the right choice.

Learn sagas for distributed transactions: choreography vs orchestration, compensations, idempotency, and failure handling.