Topic
Distributed Systems
Move through replication, consensus, quorum, leader election, transactions, and failure recovery as one connected system.
10 Concepts97 Articles33h 52m
Overview
Move through replication, consensus, quorum, leader election, transactions, and failure recovery as one connected system.
How this topic helps
System Design
Architecture
Interview Prep
Databases
Learning Path in this Topic
Series that contain articles from Distributed Systems. Select a path to filter the article list.
Articles
97 matched articles
Article 1Split Brain Explained: When Two Nodes Both Think They Are LeaderTLDR: Split brain happens when a network partition causes two nodes to simultaneously believe they are the leader — each accepting writes the other never sees. Prevent it with quorum consensus (at lea22 min
Article 2Data Anomalies in Distributed Systems: Split Brain, Clock Skew, Stale Reads, and MoreTLDR: Distributed systems produce anomalies not because the code is buggy — but because physics makes perfect consistency impossible across network boundaries. Split brain, stale reads, clock skew, ca13 min
Article 3A Guide to Raft, Paxos, and Consensus AlgorithmsTLDR
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. Raft13 min
Article 4Stale Reads and Cascading Failures in Distributed SystemsTLDR: Stale reads return superseded data from replicas that haven't yet applied the latest write. Cascading failures turn one overloaded node into a cluster-wide collapse through retry storms and redi25 minArticle 5The Dual Write Problem: Why Two Writes Always Fail Eventually — and How to Fix ItTLDR: Any service that writes to a database and publishes a message in the same logical operation has a dual write problem. try/catch retries don't fix it — they turn failures into duplicates. The Tra23 minArticle 6How Kafka Works: The Log That Never ForgetsTLDR: 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 th13 minPage 1 of 17