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 Prometheus so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how MariaDB and Prometheus 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 vs Prometheus Breakdown


 
Database Model

Relational database

Time series database

Architecture

MariaDB can be deployed on-premises, in the cloud, or as a hybrid solution, and is compatible with various operating systems, including Linux, Windows, and macOS.

Prometheus uses a pull-based model where it scrapes metrics from configured targets at given intervals. It stores time series data in a custom, efficient, local storage format, and supports multi-dimensional data collection, querying, and alerting. It can be deployed as a single binary on a server or on a container platform like Kubernetes.

License

GNU GPLv2

Apache 2.0

Use Cases

Web applications, transaction processing, e-commerce

Monitoring, alerting, observability, system metrics, application metrics

Scalability

Supports replication and sharding for horizontal scaling, as well as query optimization and caching for improved performance

Prometheus is designed for reliability and can scale vertically (single node with increased resources) or through federation (hierarchical setup where Prometheus servers scrape metrics from other Prometheus servers)

Looking for the most efficient way to get started?

Whether you are looking for cost savings, lower management overhead, or open source, InfluxDB can help.

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.

Prometheus Overview

Prometheus is an open-source monitoring and alerting toolkit initially developed at SoundCloud in 2012. It has since become a widely adopted monitoring solution and a part of the Cloud Native Computing Foundation (CNCF) project. Prometheus focuses on providing real-time insights and alerts for containerized and microservices-based environments. Its primary use case is monitoring infrastructure and applications, with an emphasis on reliability and scalability.


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.

Prometheus for Time Series Data

Prometheus is specifically designed for time series data, as its primary focus is on monitoring and alerting based on the state of infrastructure and applications. It uses a pull-based model, where the Prometheus server scrapes metrics from the target systems at regular intervals. This model is suitable for monitoring dynamic environments, as it allows for automatic discovery and monitoring of new instances. However, Prometheus is not intended as a general-purpose time series database and might not be the best choice for high cardinality or long-term data storage.


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.

Prometheus Key Concepts

  • Metric: A numeric representation of a particular aspect of a system, such as CPU usage or memory consumption.
  • Time Series: A collection of data points for a metric, indexed by timestamp.
  • Label: A key-value pair that provides metadata and context for a metric, enabling more granular querying and aggregation.
  • PromQL: Prometheus uses its own query language called PromQL (Prometheus Query Language) for querying time series data and generating alerts.


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.

Prometheus Architecture

Prometheus is a single-server, standalone monitoring system that uses a pull-based approach to collect metrics from target systems. It stores time series data in a custom, highly compressed, on-disk format, optimized for fast querying and low resource usage. The architecture of Prometheus is modular and extensible, with components like exporters, service discovery mechanisms, and integrations with other monitoring systems. As a non-distributed system, it lacks built-in clustering or horizontal scalability, but it supports federation, allowing multiple Prometheus servers to share and aggregate data.

Free Time-Series Database Guide

Get a comprehensive review of alternatives and critical requirements for selecting yours.

MariaDB Features

Compatibility

MariaDB is fully compatible with MySQL, making it easy to migrate existing MySQL applications and databases.

Storage Engines

MariaDB supports multiple storage engines, allowing users to choose the best option for their specific use case.

Replication and Clustering

MariaDB offers built-in replication and supports Galera Cluster for high availability, fault tolerance, and load balancing. Security: MariaDB provides advanced security features such as data encryption, secure connections, and role-based access control.

Prometheus Features

Pull-based Model

Prometheus collects metrics by actively scraping targets, enabling automatic discovery and monitoring of dynamic environments.

PromQL

The powerful Prometheus Query Language allows for expressive and flexible querying of time series data.

Alerting

Prometheus supports alerting based on user-defined rules and integrates with various alert management and notification systems.


MariaDB Use Cases

Web Applications

MariaDB is a popular choice for web applications due to its compatibility with MySQL, performance improvements, and open-source nature.

Data Migration

Organizations looking to migrate from MySQL to an open-source alternative can easily transition to MariaDB, thanks to its compatibility and enhanced features.

OLTP Workloads

As a relational database MariaDB is a good fit for any application that requires strong transactional guarantees.

Prometheus Use Cases

Infrastructure Monitoring

Prometheus is widely used for monitoring the health and performance of containerized and microservices-based infrastructure, including Kubernetes and Docker environments.

Application Performance Monitoring (APM)

Prometheus can collect custom application metrics using client libraries and monitor application performance in real-time.

Alerting and Anomaly Detection

Prometheus enables organizations to set up alerts based on specific thresholds or conditions, helping them identify and respond to potential issues or anomalies quickly.


MariaDB Pricing Model

MariaDB is an open-source database, which means it is free to download, use, and modify. However, for organizations that require professional support, the MariaDB Corporation offers various subscription plans, including MariaDB SkySQL, a fully managed cloud database service. Pricing for support subscriptions and the SkySQL service depends on the chosen plan, service level, and resource usage.

Prometheus Pricing Model

Prometheus is an open-source project, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed Prometheus server. Additionally, several cloud-based managed Prometheus services, such as Grafana Cloud and Weave Cloud, offer different pricing models based on factors like data retention, query rate, and support.