Common Use Cases of Consistent Hashing
Consistent hashing is the backbone of many distributed systems. From caches and databases to load balancers and CDNs, it enables deterministic routing with minimal reshuffling when nodes change. Let’s explore the most common real-world use cases.
CRDTs: Mergeable Data Structures for Distributed Systems
Start With This Excellent Explanation
Designing a Load Balancer
Load balancing is about deciding where to send the next request. From weighted round robin to response-time aware routing to consistent hashing at L4 or L7, each strategy has trade-offs in complexity, fairness, and statefulness. This guide walks through the common approaches and key design considerations.
Distributed Systems Interview Gotchas Cheat Sheet
Common pitfalls and misconceptions about distributed systems topics like CAP, ACID, quorum math, and leaderless replication—designed for quick interview prep.
Hash + TTL: The 80/20 Dedup Pattern
A simple, scalable way to suppress duplicate events and requests without heavy comparisons or global coordination.
Leaderless Replication: Simplicity Through Redundancy
⚙️ What Is Leaderless Replication?
Operational Transforms Explained
A deep dive into Operational Transformation (OT): the algorithm behind real-time collaborative editing systems like Google Docs.
Semantic vs. Non-Semantic Sharding and Partitioning
Explaining the often-missed distinction between semantic and non-semantic partitioning, and how it applies across databases, caches, and even load balancing.
The Inbox/Outbox Pattern Explained
A clear explanation of the Inbox/Outbox pattern, why it exists, and how it powers social feeds, messaging, and event-driven systems.
Wait… Why Are We Modding by the Number of Nodes?
A developer’s journey from naive hashing to consistent hashing with virtual nodes.
Why Celebrities Are Necessary
Without celebrities, we’d never discover concepts like hot shards, read contention, or write amplification. Celebrities are the reason these patterns exist.