Writing system design by data flow to understand to design your system design better.

1. Scalability

1. Vertical vs. Horizontal Scaling

2. Partitioning — Divide and Conquer

3. Replication — Redundancy for Speed and Safety

   • Synchronous Replication

   • Asynchronous Replication

   • Leader-Follower Model

4. Caching — The Shortcut to Scale

   • In-Memory Caches

   • Edge Caches

   • Application Caches

   • TTL (Time to Live)

   • Write-through

   • Cache-aside

5. Elasticity — Scaling as a Living System

   • Auto-scaling groups

   • Serverless platforms: event-driven.

   • Container orchestration (Kubernetes)

   • Observability and automation.

6. Load Distribution and Partitioning

   • Traffic Distribution

   • Data Distribution

   • Task Distribution

7. Sharding — Divide and Conquer

   • Throughput

   • Storage

   • Isolation

8. Consistent Hashing

9. Data Locality

10. Balancing the System — Avoiding Hotspots

2. Bottlenecks & Backpressures

1. Buffering — The Elastic Pause

2. Batching — Efficiency Through Grouping

3. Flow Control Strategies

   • Credit-Based Flow Control: maximum number of consumer can handle.

   • Rate Limiting

   • Reactive Streams: producer request N messages -> consumer consume N messages.

4. Consumer Lag

   • Use bounded queues

   • Monitor queue depth, latency, and throughput

3. Consistency

1. CAP Theorem

2. Eventual Consistency

3. The Raft Algorithm — Consensus + The Paxos

   • Leader election

   • Log replication

   • Safety guarantees

4. Coordination Beyond Consensus

   • Distributed Locks

   • Barriers

   • Leases

5. Quorums — The Math Behind Consensus

4. Reliability and Fault Tolerance