Distributed Storage: A Visual Guide
How to Read This Post Each scenario shows a diagram first, then a short note on why the pattern matters. Complexity increases as you scroll. Strategy Category Strength Trade-off Leader-Follower Replication Simple, strong consistency for reads Write bottleneck at leader Multi-Leader Replication Multi-DC writes Conflict resolution needed Leaderless (Quorum) Replication No single point of failure Tunable consistency Range Partitioning Partitioning Range scans, sorted data Hotspot risk Hash Partitioning Partitioning Even distribution No range queries Consistent Hashing Partitioning Minimal redistribution Virtual nodes needed Level 1 — Foundations 1. Single Node Storage %%{init: {'theme':'dark', 'themeVariables': {'primaryTextColor':'#e5e7eb','secondaryTextColor':'#e5e7eb','tertiaryTextColor':'#e5e7eb','textColor':'#e5e7eb','nodeTextColor':'#e5e7eb','edgeLabelText':'#e5e7eb','clusterTextColor':'#e5e7eb','actorTextColor':'#e5e7eb'}}}%% flowchart LR C1["Client 1"] --> DB[(Single Database)] C2["Client 2"] --> DB C3["Client 3"] --> DB DB -->|"⚡ crash"| LOST["All data LOSTAll clients BLOCKED"] style C1 fill:#4a9eff,stroke:#2d7ed8,color:#fff style C2 fill:#4a9eff,stroke:#2d7ed8,color:#fff style C3 fill:#4a9eff,stroke:#2d7ed8,color:#fff style DB fill:#ff6b6b,stroke:#d44,color:#fff style LOST fill:#546e7a,stroke:#90a4ae,color:#fff Single point of failure. One database handles everything — simple but fatal. If it crashes, all data is lost and all clients are blocked. Every distributed storage pattern exists to solve this problem. ...