MariaDB vs MongoDB

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 MariaDB and MongoDB so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how MariaDB and MongoDB 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.

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.

MongoDB Overview

MongoDB is a popular, open-source NoSQL database launched in 2009. Designed to handle large volumes of unstructured and semi-structured data, MongoDB offers a flexible, schema-less data model, horizontal scalability, and high performance. Its ease of use, JSON-based document storage, and support for a wide range of programming languages have contributed to its widespread adoption across various industries and applications.

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.

MongoDB for Time Series Data

Although MongoDB is a general-purpose NoSQL database, it can be used for storing and processing time series data. The flexible data model of MongoDB allows for easy adaptation to the evolving structure of time series data, such as the addition of new metrics or the modification of existing ones. MongoDB provides built-in support for time-to-live (TTL) indexes, which automatically expire old data after a specified time period, making it suitable for managing large volumes of time series data with a limited storage capacity. MongoDB has also recently added a custom columnar storage engine and time series collection for time series use cases, meant to improve performance over the default MongoDB storage engine in terms of data compression and query performance.

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.

MongoDB Key Concepts

Some key terminology and concepts specific to MongoDB include:

• Database: A MongoDB database is a container for collections, which are groups of related documents.
• Collection: A collection in MongoDB is analogous to a table in relational databases, holding a set of documents.
• Document: A document in MongoDB is a single record, stored in a JSON-like format called BSON (Binary JSON). Documents within a collection can have different structures.
• Field: A field is a key-value pair within a document, similar to an attribute or column in a relational database.
• Index: An index in MongoDB is a data structure that improves the query performance on specific fields within a collection.

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.

MongoDB Architecture

MongoDB’s architecture is centered around its flexible, document-based data model. As a NoSQL database, MongoDB supports a schema-less structure, which allows for the storage and querying of diverse data types, such as nested arrays and documents. MongoDB can be deployed as a standalone server, a replica set, or a sharded cluster. Replica sets provide high availability through automatic failover and data redundancy, while sharded clusters enable horizontal scaling and load balancing by distributing data across multiple servers based on a shard key.