Cassandra vs MongoDB vs PostgreSQL: What are the differences?
Introduction
Cassandra, MongoDB, and PostgreSQL are all popular database management systems, each with its own strengths and use cases. Understanding their differences is crucial for selecting the right database for a specific application or project.
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Scalability: Cassandra is a distributed database known for its ability to handle massive amounts of data across multiple nodes. It is designed to be highly scalable and can easily handle big data workloads. In contrast, MongoDB and PostgreSQL are primarily designed for single-node setups, although they can also handle some level of scalability through sharding and replication.
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Data Model: MongoDB is a document-oriented database, where data is stored in flexible, JSON-like structures known as documents. It allows for dynamic schema, making it well-suited for applications with evolving data requirements. On the other hand, Cassandra and PostgreSQL follow the table-based relational model. Cassandra offers a flexible schema design, while PostgreSQL requires a predefined schema.
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Consistency: In terms of consistency, PostgreSQL provides strong consistency guarantees and ensures that each read operation returns the most recent write. MongoDB, on the other hand, offers eventual consistency by default, meaning that reads may not immediately reflect the latest writes. Cassandra falls between the two, offering tunable consistency levels that enable trade-offs between availability and consistency.
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Query Language: PostgreSQL uses SQL as its primary query language, making it a good fit for applications that rely heavily on complex queries and relational operations. MongoDB introduces its own query language called MongoDB Query Language (MQL), which supports document-based queries and aggregation pipelines. Cassandra, however, requires the use of Cassandra Query Language (CQL), which is similar to SQL but lacks certain relational features.
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Data Storage: Cassandra employs a distributed data storage model based on a consistent hashing algorithm, ensuring data is evenly distributed across the cluster. In MongoDB, data is stored in a flexible binary representation known as BSON (Binary JSON). PostgreSQL stores data in a relational manner using tables with rows and columns.
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Data Integrity and Transactions: PostgreSQL emphasizes data integrity and supports full ACID (Atomicity, Consistency, Isolation, Durability) compliance. It allows for complex transactions and enforces referential integrity through foreign key constraints. While MongoDB provides some level of atomicity and isolation, it does not support multi-document transactions. Similarly, Cassandra also lacks full ACID compliance, prioritizing high availability and partition tolerance.
In summary, Cassandra offers excellent scalability and is ideal for big data workloads, while MongoDB provides flexibility with its document-oriented data model. PostgreSQL, on the other hand, excels in complex querying and transactional integrity.
