10 articles
Notes from the workbench
Guides, patterns and lessons learned building full-stack products and AI features.
· 17 min read
Caching, Consistency, and Trade-offs: Designing Scalable Distributed Systems
Every optimisation introduces a trade-off, and nowhere is this more visible than in caching and consistency. This post walks through caching strategies, consistency models, and how production systems at scale actually navigate these trade-offs.
- distributed-systems
- eventual-consistency
- cap-theorem
· 10 min read
Idempotency: Making Retries Safe in Distributed Systems
Retries are unavoidable in distributed systems. Make side-effecting endpoints safe with idempotency keys, Redis and DB constraints.
- distributed-systems
- backend
- system-design
· 3 min read
Fanout at Scale: Push vs. Pull Strategies in Distributed Systems
The fanout problem asks one question: when a single event occurs, how do we efficiently deliver it to millions of consumers? There are two dominant strategies, Fanout-on-Write (Push) and Fanout-on-Read (Pull), and both come with serious trade-offs.
- programming
- system-design
- javascript
· 3 min read
Clustering in Node.js: Scaling Your Applications Like Never Before!
The Node.js cluster module implements a sophisticated multi-process architecture that enables applications to scale across multiple CPU cores. This is an in-depth exploration of the fundamental concepts and mechanisms that power Node.js clustering.