The Saga Pattern Explained
How the saga pattern coordinates long running business transactions across services using local commits and compensating actions instead of distributed two phase commit.
·5 min read · #backend#microservices#saga
#distributed-systems
8 posts · page 1 of 1
CAP Theorem in Practice: What It Actually Means for Your System
A pragmatic look at the CAP theorem: what consistency and availability mean for real workloads, and how PACELC describes the trade-offs better.
·5 min read · #system-design#cap-theorem#distributed-systems
Consistent Hashing Explained for Engineers Who Operate Real Systems
How consistent hashing actually works in production: virtual nodes, rebalancing, hot keys, and why naive modulo hashing fails at scale.
·5 min read · #system-design#consistent-hashing#distributed-systems
Designing Rate Limiters: A System Design Deep Dive
A senior-engineer guide to designing rate limiters: algorithms, distributed coordination, trade-offs, and production patterns that actually scale.
·5 min read · #system-design#rate-limiting#scalability
Distributed Locks with Redis: What Works, What Breaks
A practical look at distributed locking with Redis: SET NX EX, Redlock, fencing tokens, and the failure modes that cause data corruption.
·5 min read · #system-design#redis#distributed-systems
ACID vs BASE: Transaction Models Explained
What ACID and BASE actually mean, where each shines, and how to reason about consistency, availability, and durability when you design data systems.
·4 min read · #sql#acid#base
System Design: Distributed Cache from Scratch
Design a distributed cache using consistent hashing, replication, and eviction policies to speed up reads at internet scale.
·3 min read · #system-design#cache#redis
System Design: Token Bucket Rate Limiter
Design a distributed rate limiter using the token bucket algorithm with Redis, handling bursty traffic while protecting backend services.
·3 min read · #system-design#rate-limiting#redis