Cassandra vs Citus: What are the differences?
Introduction
In this article, we will discuss the key differences between Cassandra and Citus.
-
Scalability: One of the key differences between Cassandra and Citus is their approach to scalability. Cassandra is a distributed database that is designed to scale horizontally across multiple nodes. It achieves this through its partitioning strategy and peer-to-peer architecture, allowing it to handle large amounts of data and high write throughput. On the other hand, Citus is an extension to PostgreSQL that scales vertically by distributing the data across multiple machines using sharding. It leverages PostgreSQL's shared-nothing architecture to provide high scalability.
-
Data Model: Another significant difference between Cassandra and Citus is their data model. Cassandra is a NoSQL database that follows a column-oriented data model. It stores data in tables that are organized into column families, with each row consisting of multiple columns. This flexible schema allows for dynamic and fast data access. Citus, on the other hand, is an extension to PostgreSQL, which follows a relational data model. It uses tables with rows and columns to store data, and supports SQL queries for data retrieval and manipulation.
-
Consistency Model: Cassandra and Citus differ in their consistency models. Cassandra offers tunable consistency, allowing users to choose between strong consistency or eventual consistency. It uses the quorum-based replication strategy to provide high availability and fault tolerance. Citus, on the other hand, follows the strong consistency model by default. It ensures that every database transaction is atomic, consistent, isolated, and durable (ACID), which is important for applications that require strict consistency guarantees.
-
Replication: Replication is handled differently in Cassandra and Citus. Cassandra uses a masterless architecture with peer-to-peer replication, where each node in the cluster can accept write requests and handle read requests. It replicates data across multiple nodes using a replication factor defined for each keyspace. Citus, on the other hand, uses a distributed database model with a master node that coordinates writes and distributes data across multiple worker nodes. It provides automatic sharding and replicates data based on the primary key.
-
Query Language: Both Cassandra and Citus have different query languages. Cassandra uses CQL (Cassandra Query Language), which is similar to SQL but has some differences due to its NoSQL nature. CQL supports a wide range of queries and provides features like secondary indexes and materialized views. On the other hand, Citus uses SQL as its query language, as it is an extension to PostgreSQL. It supports the full range of SQL queries and provides advanced features like window functions, JSON functions, and common table expressions.
-
Use Cases: Cassandra and Citus are suited for different types of use cases. Cassandra is well-suited for applications that require high scalability, high availability, and fault tolerance, such as big data analytics, time series data, and IoT applications. It is designed to handle large volumes of writes and reads across multiple nodes. Citus, on the other hand, is suitable for applications that require horizontal scalability with strong consistency, such as transactional workloads, multi-tenant applications, and real-time analytics. It provides a familiar SQL interface and can leverage PostgreSQL's rich ecosystem of extensions and tools.
In summary, Cassandra and Citus differ in their scalability approaches, data models, consistency models, replication strategies, query languages, and use cases. While Cassandra excels in distributed scalability and offers eventual consistency, Citus provides vertical scalability with strong consistency using a relational data model and SQL capabilities.
