Horizontal scalability

Horizontal scalability refers to the ability to expand the storage and processing capacity of a database by adding more servers to a database cluster. A traditional single-master database's storage capacity is limited by the capacity of the server that hosts the master instance. If the data set outgrows this capacity and a more powerful server isn't available, the data set must be shared among multiple independent database instances that know nothing of each other. Your application bears responsibility for knowing to which instance a given piece of data belongs.

Cassandra, on the other hand, is deployed as a cluster of instances that are all aware of each other. From the client application's standpoint, the cluster is a single entity; the application need not know, nor care, which machine a piece of data belongs to. Instead, data can be read or written to any instance in the cluster, referred to as a node; this node will forward the request to the instance where the data actually belongs. The result is that Cassandra deployments have an almost limitless capacity to store and process data. When additional capacity is required, more machines can simply be added to the cluster. When new machines join the cluster, Cassandra takes care of rebalancing the existing data so that each node in the expanded cluster has a roughly equal share. Also, the performance of a Cassandra cluster is directly proportional to the number of nodes within the cluster. As you keep on adding instances, the read and write throughput will keep increasing linearly.

Cassandra is one of the several popular distributed databases inspired by the Dynamo architecture, originally published in a paper by Amazon. Other widely used implementations of Dynamo include Riak and Voldemort. You can read the original paper at http://s3.amazonaws.com/AllThingsDistributed/sosp/amazon-dynamo-sosp2007.pdf.