Cassandra vs MongoDB vs MySQL: What are the differences?

Introduction

When it comes to choosing a database management system, Cassandra, MongoDB, and MySQL are popular choices. Each has its unique strengths and weaknesses that cater to different use cases.

1. Data Model: Cassandra is a distributed wide-column store NoSQL database that supports a partitioned row store model, making it highly scalable for big data applications. MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents. On the other hand, MySQL is a traditional relational database management system that organizes data into tables with predefined schemas, suitable for structured data storage.

2. Scalability: Cassandra is designed for linear scalability, allowing it to handle massive amounts of data across multiple nodes with ease. MongoDB also offers some scalability features, but it may not be as seamless as Cassandra when dealing with large datasets. In contrast, MySQL's scalability is limited by the capacity of a single server, making it less suitable for extensive growth without sharding or clustering.

3. Consistency Model: Cassandra uses an eventual consistency model, ensuring high availability and fault tolerance by sacrificing strict consistency. MongoDB offers tunable consistency, allowing developers to balance consistency and availability based on their requirements. MySQL, as a relational database, follows the ACID properties, providing strong consistency at the expense of availability in some scenarios.

4. Query Language: Cassandra uses CQL (Cassandra Query Language) for database operations, which closely resembles SQL syntax. MongoDB's query language is based on JSON-like documents and supports a rich set of operations for document retrieval and manipulation. In comparison, MySQL uses SQL (Structured Query Language), a powerful tool for managing relational databases with decades of refinement and optimization.

5. Indexing: Cassandra employs secondary indexes and SASI (SSTable Attached Secondary Indexes) for efficient querying across distributed data. MongoDB supports various types of indexes, including compound, unique, and geospatial indexes, enhancing query performance on document fields. MySQL relies on B-tree indexes for fast data retrieval in relational tables, with options for primary, unique, and full-text indexes to optimize queries.

6. Use Cases: Cassandra excels in write-heavy applications, such as real-time analytics and time-series data storage, where high availability and scalability are paramount. MongoDB is suitable for document-oriented storage and agile development due to its flexible schema and ease of scalability. MySQL shines in transactional applications, e-commerce platforms, and relational data management, providing strong consistency and robust transaction support.

In Summary, Cassandra, MongoDB, and MySQL offer distinct advantages in scalability, data modeling, consistency, query language, indexing, and use cases, catering to diverse application requirements in the database ecosystem.