Data Replication Explained: Single-Leader, Multi-Leader, and Leaderless Strategies

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Data replication creates a live, continuously updated copy of a database, unlike backups which capture only a point-in-time snapshot. Replication serves key purposes including high availability, read scaling, and reducing latency through geographical distribution of data. The most common approach is Single-Leader Replication, where one node handles all writes and followers sync from it, balancing simplicity with read scalability. Multi-Leader Replication allows multiple nodes to accept writes, but introduces complex data conflicts that must be resolved through strategies like Last Write Wins or CRDTs. Leaderless Replication, popularized by Amazon's 2007 Dynamo paper and used in systems like Apache Cassandra, allows writes to any node but comes with its own consistency trade-offs.

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ProgrammingDEV Community ·

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