M3 vs MariaDB
A detailed comparison
Compare M3 and MariaDB for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 M3 and MariaDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how M3 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.
M3 vs MariaDB Breakdown
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 |
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| Database Model | Time series database |
Relational database |
| Architecture | The M3 stack can be deployed on-premises or in the cloud, using containerization technologies like Kubernetes or as a managed service on platforms like AWS or GCP |
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. |
| License | Apache 2.0 |
GNU GPLv2 |
| Use Cases | Monitoring, observability, IoT, Real-time analytics, large-scale metrics processing |
Web applications, transaction processing, e-commerce |
| Scalability | Horizontally scalable, designed for high availability and large-scale deployments |
Supports replication and sharding for horizontal scaling, as well as query optimization and caching for improved performance |
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M3 Overview
M3 is a distributed time series database written entirely in Go. It is designed to collect a high volume of monitoring time series data, distribute storage in a horizontally scalable manner, and efficiently leverage hardware resources. M3 was initially developed by Uber as a scalable remote storage backend for Prometheus and Graphite and later open-sourced for broader use.
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.
M3 for Time Series Data
M3 is specifically designed for time-series data. It is a distributed and scalable time-series database optimized for handling large volumes of high-resolution data points, making it an ideal solution for storing, querying, and analyzing time-series data.
M3’s architecture focuses on providing fast and efficient querying capabilities, as well as high ingestion rates, which are essential for working with time-series data. Its horizontal scalability and high availability ensure that it can handle the demands of large-scale deployments and maintain performance as data volumes grow.
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.
M3 Key Concepts
- Time Series Compression: M3 has the ability to compress time series data, resulting in significant memory and disk savings. It uses two compression algorithms, M3TSZ and protobuf encoding, to achieve efficient data compression.
- Sharding: M3 uses virtual shards that are assigned to physical nodes. Timeseries keys are hashed to a fixed set of virtual shards, making horizontal scaling and node management seamless.
- Consistency Levels: M3 provides variable consistency levels for read and write operations, as well as cluster connection operations. Write consistency levels include One (success of a single node), Majority (success of the majority of nodes), and All (success of all nodes). Read consistency level is One, which corresponds to reading from a single nod
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.
M3 Architecture
M3 is designed to be horizontally scalable and handle high data throughput. It uses fileset files as the primary unit of long-term storage, storing compressed streams of time series values. These files are flushed to disk after a block time window becomes unreachable. M3 has a commit log, equivalent to the commit log or write-ahead-log in other databases, which ensures data integrity. Client Peer streaming is responsible for fetching blocks from peers for bootstrapping purposes. M3 also implements caching policies to optimize efficient reads by determining which flushed blocks are kept in memory.
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.
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M3 Features
Commit Log
M3 uses a commit log to ensure data integrity, providing durability for write operations.
Peer Streaming
M3’s client peer streaming fetches data blocks from peers for bootstrapping purposes, optimizing data retrieval and distribution.
Caching Mechanisms
M3 implements various caching policies to efficiently manage memory usage, keeping frequently accessed data blocks in memory for faster reads.
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.
M3 Use Cases
Monitoring and Observability
M3 is particularly suitable for large-scale monitoring and observability tasks, as it can store and manage massive volumes of time-series data generated by infrastructure, applications, and microservices. Organizations can use M3 to analyze, visualize, and detect anomalies in the metrics collected from various sources, enabling them to identify potential issues and optimize their systems.
IoT and Sensor Data
M3 can be used to store and process the vast amounts of time-series data generated by IoT devices and sensors. By handling data from millions of devices and sensors, M3 can provide organizations with valuable insights into the performance, usage patterns, and potential issues of their connected devices. This information can be used for optimization, predictive maintenance, and improving the overall efficiency of IoT systems.
Financial Data Analysis
Financial organizations can use M3 to store and analyze time-series data related to stocks, bonds, commodities, and other financial instruments. By providing fast and efficient querying capabilities, M3 can help analysts and traders make more informed decisions based on historical trends, current market conditions, and potential future developments.
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.
M3 Pricing Model
M3 is an open source database and can be used freely, although you will have to account for the cost of managing your infrastructure and the hardware used to run M3. Chronosphere is the co-maintainer of M3 along with Uber and also offers a hosted observability that uses M3 as the backend storage layer.
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.
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