Replication
Keeping copies of the same data on multiple servers. Improves read performance and provides fault tolerance if one server goes down.
What is Replication?
Keeping copies of the same data on multiple servers. Improves read performance and provides fault tolerance if one server goes down.
Replication is a foundational concept that sits in the Database Fundamentals 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 "Replication" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
Learn Replication in depth
Full interactive lesson with diagrams, code examples, real-world references, and a quiz.
Open the Replication lessonRelated lessons
Lessons that touch on Replication as part of a larger topic.
Master-Slave Replication
The most common replication topology, one writer, many readers, and the trade-offs that come with it
foundation · database fundamentals
Asynchronous Replication
The default replication mode, fast writes at the cost of potential data loss
intermediate · data replication distribution
Binlog Replication
MySQL's binary log, the engine behind MySQL replication and CDC
intermediate · data replication distribution
Write-Ahead Log Replication
Postgres WAL, the foundation of crash recovery, replication, and point-in-time restore
intermediate · data replication distribution
Replication Topology
A decision framework for choosing the right replication topology for your system
intermediate · data replication distribution
See also
Related glossary terms you might want to look up next.
Sharding
Splitting a database into smaller pieces (shards) distributed across multiple servers. Each shard holds a subset of the data.
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.
Consensus
The process of getting multiple nodes in a distributed system to agree on a single value. The foundation of distributed databases and coordination services.