Apache Cassandra vs MariaDB

Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Apache Cassandra and MariaDB so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Apache Cassandra and MariaDB perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

Apache Cassandra Overview

Apache Cassandra is a highly scalable, distributed, and decentralized NoSQL database designed to handle large amounts of data across many commodity servers. Originally created by Facebook, Cassandra is now an Apache Software Foundation project. Its primary focus is on providing high availability, fault tolerance, and linear scalability, making it a popular choice for applications with demanding workloads and low-latency requirements.

MariaDB Overview

MariaDB is an open-source relational database management system (RDBMS) that was created as a fork of MySQL in 2009 by the original developers of MySQL, led by Michael Widenius. The primary goal of MariaDB was to provide an open-source and community-driven alternative to MySQL, which was acquired by Oracle Corporation in 2008. MariaDB is compatible with MySQL and has enhanced features, better performance, and improved security. It is widely used by organizations worldwide and is supported by the MariaDB Foundation, which ensures its continued open-source development.

Apache Cassandra for Time Series Data

Cassandra can be used for handling time series data due to its distributed architecture and support for time-based partitioning. Time series data can be efficiently stored and retrieved using partition keys based on time ranges, ensuring quick access to data points.

MariaDB for Time Series Data

While MariaDB is not specifically designed for time series data, it can be used to store, process, and analyze time series data due to its flexible and extensible architecture. SQL support, along with analytics optimized storage engines like ColumnStore make it suitable for handling time series data at smaller levels of data volume.

Apache Cassandra Key Concepts

• Column Family: Similar to a table in a relational database, a column family is a collection of rows, each consisting of a key-value pair.
• Partition Key: A unique identifier used to distribute data across multiple nodes in the cluster, ensuring even distribution and fast data retrieval.
• Replication Factor: The number of copies of data stored across different nodes in the cluster to provide fault tolerance and high availability.
• Consistency Level: A configurable parameter that determines the trade-off between read/write performance and data consistency across the cluster.

MariaDB Key Concepts

• Storage Engines: MariaDB supports multiple storage engines, each optimized for specific types of workloads or data storage requirements. Examples include InnoDB, MyISAM, Aria, and ColumnStore.
• Galera Cluster: A synchronous, multi-master replication solution for MariaDB that allows for high availability, fault tolerance, and load balancing.
• MaxScale: A database proxy for MariaDB that provides advanced features such as query routing, load balancing, and security.
• Connectors: MariaDB provides a variety of connectors to allow applications to interact with the database using various programming languages and APIs.

Apache Cassandra Architecture

Cassandra uses a masterless, peer-to-peer architecture, in which all nodes are equal, and there is no single point of failure. This design ensures high availability and fault tolerance. Cassandra’s data model is a hybrid between a key-value and column-oriented system, where data is partitioned across nodes based on partition keys and stored in column families. Cassandra supports tunable consistency, allowing users to adjust the balance between data consistency and performance based on their specific needs.

MariaDB Architecture

MariaDB is a relational database that uses the SQL language for querying and data manipulation. Its architecture is based on a client-server model, with clients interacting with the server through various connectors and APIs. MariaDB supports multiple storage engines, allowing users to choose the most suitable engine for their specific use case. The database also offers replication and clustering options for high availability and load balancing.