Cassandra vs RocksDB: What are the differences?

Introduction

Cassandra and RocksDB are both popular database management systems, but they have key differences in their design and functionality. This Markdown code will showcase those differences, providing specific details to understand their distinctions.

1. Storage Architecture: Cassandra follows a distributed architecture that allows data to be stored across multiple nodes, ensuring high availability and fault tolerance. On the other hand, RocksDB is a local storage engine that operates as a single-node database, providing high performance for read-heavy workloads.

2. Data Model: Cassandra is a columnar NoSQL database that enables flexible schema design and supports a wide variety of data types. It uses a distributed key-value store model, where data is structured using column families and tables. In contrast, RocksDB is a key-value store optimized for solid-state drives (SSDs), offering faster data retrieval but with a fixed schema and limited data type support.

3. Consistency Model: Cassandra implements tunable consistency, allowing clients to choose between strong consistency and eventual consistency based on their application requirements. This provides trade-offs between data consistency and availability. Meanwhile, RocksDB guarantees strong consistency since it operates as a single-node database and does not support distributed transactions.

4. Concurrency Control: Cassandra adopts an optimistic concurrency control mechanism, utilizing conflict resolution to handle concurrent writes and updates. It uses a versioned write model to maintain data consistency. In contrast, RocksDB employs a single-threaded model by default but also supports multi-threading for concurrent read and write operations.

5. Durability and Write Performance: Cassandra achieves durability and fault tolerance through its distributed architecture and replication factor, ensuring data availability even if a node fails. However, this replication incurs additional write overhead, affecting write performance. On the other hand, RocksDB offers high write performance due to its local storage nature, but it lacks built-in replication for fault tolerance.

6. Use Cases and Scalability: Cassandra is designed for high scalability and can handle massive amounts of data and concurrent requests across multiple nodes. It is well-suited for applications requiring high availability and scalability, such as large-scale web applications and time-series data storage. In comparison, RocksDB is more suitable for embedded applications, edge devices, and scenarios with limited storage capacities where low-latency data access is vital.

In Summary, Cassandra excels in distributed architectures, flexible data modeling, tunable consistency, high availability, and scalability, making it ideal for large-scale applications. In contrast, RocksDB is optimized for local storage systems, providing high-performance read-heavy workloads, strong consistency, and low-latency data access.