What are the design principles of Cassandra?

Cassandra’s design principles are based on several aspects:

1. Distributed – Cassandra is a distributed database system where data can be spread across multiple nodes. It utilizes consistent hashing algorithm to partition data onto different nodes for distributing and balancing the load.
2. Scalability – Cassandra has strong scalability, allowing nodes to be easily added or removed to accommodate growing data volume and workload. It utilizes a decentralized P2P architecture, enabling peer-to-peer communication between nodes with no single point of failure.
3. Cassandra is designed to handle large amounts of data and high-concurrency read/write operations by utilizing Memtable and SSTable structures for fast read/write operations, along with leveraging multi-threaded parallel processing to enhance performance.
4. High availability – Cassandra demonstrates high availability, as it is able to continue functioning smoothly even in the event of node failures or network partitions. It utilizes replication and fault detection mechanisms to ensure the reliability and persistence of data.
5. Elastic consistency – Cassandra utilizes an eventual consistency model, where there may be temporary inconsistencies during the data updating process, but eventually reaches a consistent state. It uses a mechanism based on vector clocks to solve concurrent update issues in distributed systems.
6. Cassandra offers a user-friendly data model and querying language, supporting flexible data models and complex query operations. It also provides features such as automatic data partitioning, load balancing, and fault recovery, making it easier for developers to build and manage applications.