Category
distributed-systems
5 articles in this category
Understanding Consistency Patterns: An In-Depth Analysis
TLDR: Consistency is about whether all nodes in a distributed system show the same data at the same time. Strong consistency gives correctness but costs latency. Eventual consistency gives speed but requires tolerance for briefly stale reads. Choose ...
How Kafka Works: The Log That Never Forgets
TLDR: Kafka is a distributed event store. Unlike a traditional queue (RabbitMQ) where messages disappear after reading, Kafka stores them in a persistent Log. This allows multiple consumers to read the same data at their own pace, replay history, and...
The 8 Fallacies of Distributed Systems
TLDR: In 1994, L. Peter Deutsch at Sun Microsystems listed 8 assumptions that developers make about distributed systems — all of which are false. Believing them leads to hard-to-reproduce bugs, timeout cascades, and security holes. Knowing them is a ...
Consistent Hashing: Scaling Without Chaos
TLDR: Standard hashing (key % N) breaks when $N$ changes — adding or removing a server reshuffles almost all keys. Consistent Hashing maps both servers and keys onto a ring (0–360°). When a server is added, only its immediate neighbors' keys move, mi...
A Guide to Raft, Paxos, and Consensus Algorithms
TLDR: Consensus algorithms allow a cluster of computers to agree on a single value (e.g., "Who is the leader?"). Paxos is the academic standard — correct but notoriously hard to understand. Raft is the practical standard — designed for understandabil...