How does Cassandra handle data sharding and distributed storage?

Cassandra is a distributed database system that achieves high availability and scalability through data partitioning and distributed storage. Data partitioning involves breaking data into smaller chunks and storing them on multiple nodes. Distributed storage involves storing these data chunks on multiple nodes to achieve data redundancy and high availability.

In Cassandra, data sharding is achieved through Partitioner. The Partitioner shards data based on its distribution and evenly distributes the sharded data across multiple nodes in the cluster. Cassandra offers various Partitioner options such as RandomPartitioner, ByteOrderedPartitioner, and Murmur3Partitioner, allowing users to choose the one that best suits their needs.

Distributed storage is achieved through Replication, which involves copying backup data to multiple nodes to ensure data redundancy and high availability. In Cassandra, users can configure Replication strategies to specify the number and distribution of data backup replicas. With Replication, even if a node fails, data can still be accessed from other nodes, ensuring the reliability and availability of the data.

In summary, Cassandra achieves high availability and scalability through data sharding and distributed storage. Users can configure Partitioner and Replication strategies according to their needs to effectively manage and store data.