Database clustering and database replication are both strategies to improve performance and reliability, but they operate differently. Clustering involves grouping multiple database instances to work as a single system, often sharing storage or coordinating tasks to handle requests collectively. Replication focuses on copying data from one database (the primary) to one or more secondary databases, keeping them in sync. While clustering aims to provide high availability and scalability through a unified system, replication prioritizes data redundancy and read scalability by maintaining copies.
In a clustered database, nodes (individual database instances) work together to distribute workloads or provide failover support. For example, a PostgreSQL cluster using streaming replication and a shared-nothing architecture might split data across nodes, with each node handling a subset of queries. If one node fails, others can take over its workload. Clustering often requires specialized configurations, like MySQL Cluster’s NDB storage engine, which synchronizes in-memory data across nodes for real-time consistency. Clusters typically manage tasks like load balancing, automatic failover, and transaction coordination internally, making them appear as a single logical database to applications. This setup is useful for high-throughput systems where downtime is unacceptable, such as financial transaction platforms.
Replication, by contrast, creates copies of data across separate databases. A common example is MySQL’s master-slave replication, where the primary database handles writes and asynchronously propagates changes to read-only replicas. Replicas can serve read queries, reducing load on the primary. Unlike clustering, replicas are often geographically distributed (e.g., a primary in the U.S. and replicas in Europe and Asia) to reduce latency for users. However, replication introduces eventual consistency—replicas might lag behind the primary. Some systems, like MongoDB’s replica sets, allow automatic failover by promoting a replica to primary if the original fails. Replication is simpler to implement for scaling reads or disaster recovery but doesn’t inherently distribute write workloads like clustering. Developers might combine both: using replication for backups and read scaling, while a cluster handles write-heavy tasks.