Cassandra consistency

As we said in an earlier chapter, Cassandra eventually becomes consistent and follows the AP principal of the CAP theorem. Consistency refers to how up to date the information across all data replicas in a Cassandra cluster is. Cassandra does eventually guarantee consistency. Now let's have a closer look; well, let's say I have five node Cassandra clusters and a replication factor of 3. This means if I have a data item1, it would be replicated to three nodes, let's say node1, node2, and node3; let's assume the key of this datum is key1. Now if the value of this key is to be rewritten and the write operation is performed on node1, then Cassandra internally replicates the values to other replicas, which are node2 and node3. But this update happens in the background and is not immediate; this is the mechanism of eventual consistency.

Cassandra provides the concept of offering the (read and write) client applications the decision of what consistency level they want to use...