How can Cassandra achieve high availability and horizontal scaling?
Key methods for achieving high availability and horizontal scalability in Cassandra include data replication, node failure detection and automatic failover, load balancing, and partition tolerance.
- Data Replication: Cassandra employs replication strategies to ensure the high availability of data. Each data copy is stored on different nodes, so even if one node fails, the data can still be recovered from other copies.
- Node failure detection and automatic failover: Cassandra uses the gossip protocol to monitor the health status of nodes and automatically perform failover. When a node is detected as unavailable, the system will automatically migrate data from that node’s replicas to other healthy nodes.
- Load balancing: Cassandra utilizes a consistent hashing algorithm to evenly distribute data to the various nodes in the cluster. This ensures that each node carries an equal load, thereby improving the overall performance of the system.
- Partition fault-tolerance: Cassandra’s data storage is designed with a distributed system, dividing data into multiple partitions that are stored on different nodes. This design ensures that even if some nodes fail, the entire system can still function.
By utilizing the aforementioned methods, Cassandra is able to achieve high availability and horizontal scalability, enabling the system to effectively handle issues such as node failures, increased load, and data expansion.
