Split-brain is a challenging problem that occurs in distributed systems when a network partition or communication failure causes a cluster of nodes to divide into two or more separate, isolated groups. Each group operates independently, leading to inconsistencies and conflicts in data or system state. This article will discuss the split-brain problem, provide a real-world example, and outline best practices for when to use and avoid specific techniques to handle split-brain scenarios.

The Split-Brain Problem

In distributed systems, maintaining a consistent view of data across all nodes is crucial for correct operation. When a split-brain scenario occurs, each partitioned group may receive different updates, causing data inconsistency and making it challenging to resolve conflicts when the partitions eventually reconnect. Split-brain is particularly problematic in distributed databases, file systems, and consensus-based systems.

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