Comparative Study of Databases: MySQL, PostgreSQL, MongoDB, and Cassandra
- Type: Relational Database Management System (RDBMS)
- Data Model: Structured data stored in tables with predefined schemas (SQL-based).
- Consistency: Strong consistency following ACID (Atomicity, Consistency, Isolation, Durability) properties, making it ideal for applications where accuracy is critical, like financial transactions.
- Scalability: Primarily supports vertical scaling; horizontal scaling requires additional configuration (e.g., sharding or replication).
- Use Case: Traditional web apps, e-commerce platforms, and any system that needs structured data with complex relationships (e.g., banking).
- Performance: Excellent for read-heavy operations and small-to-medium datasets, but performance can degrade with extremely large datasets or write-heavy workloads.
- License: Open-source, with commercial versions available (MySQL Enterprise).
- Type: Relational Database with some NoSQL capabilities
- Data Model: Structured SQL-based schema, with support for JSON and XML data types (hybrid approach).
- Consistency: Full ACID compliance and strong consistency, like MySQL, but with additional features for more complex transactional systems.
- Scalability: Vertical scaling, with better support for horizontal scaling than MySQL. Extensions like Citus allow distributed workloads.
- Use Case: Advanced applications needing complex querying, such as analytical platforms, financial systems, and geospatial applications.
- Performance: Superior in handling complex queries, large datasets, and heavy read/write operations, particularly where relational integrity is key.
- License: Open-source.
- Type: NoSQL, Document-Oriented Database
- Data Model: Stores data in flexible, schema-less documents (JSON-like BSON format).
- Consistency: Eventually consistent by default, but allows tunable consistency. It supports single-document ACID transactions and multi-document ACID transactions from version 4.0 onwards.
- Scalability: Horizontal scaling is built-in, with sharding across clusters of servers.
- Use Case: Ideal for applications with unstructured data or rapid development cycles, such as content management systems (CMS), IoT applications, and real-time analytics.
- Performance: Optimized for fast reads and writes in distributed environments. It excels with large, write-heavy workloads and non-relational datasets.
- License: Open-source (with a server-side public license).
- Type: NoSQL, Wide-Column Store Database
- Data Model: Column-family model, where data is organized by rows and columns but without a fixed schema, making it highly flexible.
- Consistency: Uses eventual consistency with tunable consistency levels, allowing users to balance between availability and consistency.
- Scalability: Designed for high availability and horizontal scaling across multiple servers, making it ideal for large-scale distributed systems.
- Use Case: Big data, IoT applications, and services needing high write throughput, such as recommendation engines, sensor data storage, and real-time big data analytics.
- Performance: Highly efficient in write-heavy operations and distributed environments, but may not be optimal for complex queries compared to RDBMS solutions.
- License: Open-source (Apache License).
- MySQL: Best for traditional, structured applications where strong relational integrity is needed.
- PostgreSQL: A powerful option for complex querying, geospatial data, and hybrid data storage (SQL + JSON).
- MongoDB: Great for applications requiring flexible schemas and large, unstructured datasets.
- Cassandra: Ideal for distributed, high-availability systems with massive data throughput.
This comparison highlights the strengths and use cases of each database, helping developers choose based on their application needs.