How is load balancing achieved in Cassandra?

Load balancing in Cassandra is primarily achieved by partitioning data evenly across nodes. This approach allows the system to distribute the workload evenly, ensuring that no single node becomes a bottleneck due to handling a disproportionate amount of data or requests.

Cassandra employs a hashing mechanism to determine which node will store a given piece of data, using a partitioner that uniformly distributes data across the cluster based on partition keys. This means that as new data is added or existing data is accessed, the requests are balanced out across all available nodes, promoting optimal resource utilization and high availability.

The other options, while related to Cassandra's operations, do not specifically describe how load balancing is performed. Replicating data across all nodes is about data redundancy and fault tolerance rather than distribution, a central coordinator node does not exist in Cassandra's decentralized architecture, and a round-robin algorithm for requests is not the mechanism used for distributing data across the nodes in the cluster. Instead, the focus is on an efficient partitioning strategy that ensures an even data distribution, which is crucial for maintaining performance and scalability in a distributed database system like Cassandra.