Cassandra Partitioning, High Availability, and Consistency - Real-time Analytics with Storm and Cassandra - page 155

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 con-
sistency. 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 de-
cision of what consistency level they want to use to read and write to the data store.