Definition

In databases, clustering can refer to either grouping similar data together or deploying multiple nodes for scalability/HA. A database cluster is a set of database instances (often across multiple machines) working together to serve applications. Clustering typically implies replication and distribution of data across nodes.

Core concept

A cluster may use data sharding (partitioning data across nodes) or replication (copies on multiple nodes) to scale and provide high availability (HA). Clustering ensures that if one node fails, others can take over.

Use cases

Large-scale systems (e.g. distributed SQL databases, NoSQL systems like Cassandra or MongoDB) use clusters to handle massive data volumes and throughput. Clustering enables fault tolerance: if one node or shard goes down, the cluster remains available.

Trade-offs

Clustering adds complexity (data consistency, network coordination) and operational overhead. You must manage data distribution (sharding keys), replication lags, and avoid “split-brain” scenarios (see below). However, clustering is essential for horizontal scaling and high availability.

Example

A PostgreSQL or MySQL cluster might have one primary and two secondary nodes. Writes go to the primary; reads can come from any node. If the primary fails, a secondary can be promoted (failover).

References

Clustering defined as connecting multiple DB instances[[6]](https://www.harper.fast/resources/what-is-database-clustering "Blog | What is Database Clustering?"); cluster advantages (HA, load distribution) from distributed system literature.