Split Brain
A failure scenario where a network partition causes two halves of a cluster to operate independently, each believing it's the leader. Can cause data corruption if not handled.
What is Split Brain?
A failure scenario where a network partition causes two halves of a cluster to operate independently, each believing it's the leader. Can cause data corruption if not handled.
Split Brain is a advanced concept that sits in the Distributed Systems Core area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space — not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition — with diagrams, code, and a quiz to lock it in — work through the "Split Brain" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
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Full interactive lesson with diagrams, code examples, real-world references, and a quiz.
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Related glossary terms you might want to look up next.
CAP Theorem
In a distributed system, you can only guarantee two of three: Consistency, Availability, and Partition tolerance. You must choose your trade-off.
Leader Election
The process of choosing one node in a cluster to coordinate actions. If the leader fails, a new one is elected. Used by Kafka, ZooKeeper, and etcd.
Quorum
The minimum number of nodes that must agree for a read or write to succeed. With N replicas, W+R > N ensures overlap between write and read sets for consistency.