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🏗️ High-Level Design (HLD) Pillars

At this level, you don't just "add a database"—you justify which database and how it scales.

1. Networking & Load Balancing

  • Load Balancers: L4 (Transport) vs L7 (Application) load balancing. Consistent Hashing.
  • API Gateways: Rate limiting, Auth, Request aggregation, Circuit Breaking.
  • Protocols: HTTP/2 vs gRPC vs WebSockets vs QUIC.
  • DNS: How global traffic is routed (Anycast, Geolocation).

2. Data Persistence & Scaling

  • SQL vs NoSQL: RDBMS (ACID) vs NoSQL (BASE). Key-Value, Document, Columnar, Graph.
  • Sharding & Partitioning: Horizontal vs Vertical scaling. Sharding keys and hotspots.
  • Scaling: Horizontal Sharding, Vertical Scaling, Sharding keys and hotspots.
  • Replication: Multi-leader, Single-leader, Leaderless (Quorums).
  • Consistency Models: Eventual, Strong, Causal, and Read-your-writes.

3. Caching Strategies

  • Eviction Policies: LRU, LFU, FIFO.
  • Cache Invalidation: Write-through, Write-around, Write-back.
  • Content Delivery: CDNs, Push vs Pull CDN models.
  • Distributed Cache: Redis Cluster vs Memcached.

4. Asynchronous & Messaging

  • Message Queues: Kafka vs RabbitMQ (Pull vs Push, ordered vs unordered).
  • Architecture: Event Sourcing, Change Data Capture (CDC), Pub-Sub.

5. Security & Identity

  • Authentication: OAuth2, OIDC, JWT vs Session-based.
  • Authorization: RBAC (Role-Based) vs ABAC (Attribute-Based).
  • Encryption: At rest (AES-256) and In-transit (TLS 1.3).

6. Observability & Reliability

  • The Three Pillars: Metrics, Logging, and Distributed Tracing.
  • CAP Theorem: Navigating the trade-offs in distributed data.
  • Coordination: Zookeeper (Leader Election, Distributed Locks).
  • Resilience: Circuit Breakers, Bulkheads, Retries with Exponential Backoff.
  • Consensus: Raft vs Paxos (Leader Election).