80% of System Design Interviews Test the Same 20% of the concepts. Here is what you need to master. 1. Scalable Data Storage • Relational vs. NoSQL: Know when to use SQL vs. NoSQL databases. • Partitioning: Vertical and horizontal partitioning (sharding). Understand trade-offs. • Indexing: Covering indexes, primary vs. secondary indexes. • Consistency Models: Strong, eventual, causal. 2. Caching • Client-side vs. Server-side Cache: Understand where caching should happen. • Caching Strategies: Write-through, write-back, write-around. • Distributed Cache: Redis, Memcached. • Cache Eviction Policies: LRU, LFU, etc. 3. Load Balancing • Horizontal Scaling: Why and how to horizontally scale services. • Load Balancing Techniques: Round-robin, consistent hashing. • Reverse Proxy: Understand how to use Nginx, HAProxy. 4. Asynchronous Processing • Message Brokers: Kafka, RabbitMQ. When to use queues vs. streams. • Event-Driven Architecture: Benefits of decoupling and event sourcing. • Task Queues: For delayed jobs or retries. 5. Database Read and Write Scaling • Read Scaling: Master replication, read replicas. • Write Scaling: Challenges with partitioning for writes, leader-election. • CAP Theorem: Consistency, Availability, or Partition tolerance may be compromised. 6. Distributed Systems Concepts • Consensus Algorithms: Paxos, Raft. • Conflict Resolution: Last Write Wins, CRDTs, vector clocks for data reconciliation. 𝟳. Reliability and Failover • Redundancy: Active-passive vs. active-active configurations. • Health Checks. • Retries and Circuit Breakers: How to protect systems from cascading failures. 𝟴. CDNs (Content Delivery Networks) • Static Content Delivery: Why use a CDN, how does it work? • Caching at the Edge: How CDNs improve latency for end users. 9. API Design and Rate Management • REST vs. GraphQL: Difference and practical use-cases for each. • Pagination and Filtering: Strategies for efficiently fetching data. • API Versioning: Best practices for evolving APIs. • Throttle Requests: Why rate limiting is essential, algorithms like token bucket, leaky bucket. 10. Search Systems • Indexing: Building and maintaining indexes for fast search. • Full-Text Search Engines: ElasticSearch, Azure AI Search. • Ranking and Relevance: Basic understanding of how scoring works. 11. Monitoring, Observability and Security • Metrics Collection: Prometheus, Grafana. • Distributed Tracing: OpenTelemetry, Sentry. • Centralized Logging. • Authentication and Authorization: OAuth, JWT. • Encryption: Data in transit vs. data at rest. If you master these 11 areas, you'll be ready for most system design interviews thrown at you. Add more if you can 👇