Consistent hashing 

Consistent hashing allows distribution of data across a cluster to minimize reorganization when nodes are added or removed.

Consistent hashing allows distribution of data across a cluster to minimize reorganization when nodes are added or removed. Consistent hashing partitions data based on the partition key. (For an explanation of partition keys and primary keys, see the Data modeling example in CQL for Cassandra 2.2 and later.)

For example, if you have the following data:

name age car gender
jim 36 camaro M
carol 37 bmw F
johnny 12   M
suzy 10   F

Cassandra assigns a hash value to each partition key:

Partition key Murmur3 hash value
jim -2245462676723223822
carol 7723358927203680754
johnny -6723372854036780875
suzy 1168604627387940318

Each node in the cluster is responsible for a range of data based on the hash value.

Hash values in a four node cluster

Cassandra places the data on each node according to the value of the partition key and the range that the node is responsible for. For example, in a four node cluster, the data in this example is distributed as follows:

Node Start range End range Partition key Hash value
A -9223372036854775808 -4611686018427387904 johnny -6723372854036780875
B -4611686018427387903 -1 jim -2245462676723223822
C 0 4611686018427387903 suzy 1168604627387940318
D 4611686018427387904 9223372036854775807 carol 7723358927203680754