MariaDB vs AWS Redshift
A detailed comparison
Compare MariaDB and AWS Redshift 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 MariaDB and AWS Redshift so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how MariaDB and AWS Redshift 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 AWS Redshift Breakdown
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 |
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| Database Model | Relational database |
Data warehouse |
| 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. |
AWS Redshift utilizes a columnar storage format for fast querying and supports standard SQL. Redshift uses a distributed, shared-nothing architecture, where data is partitioned across multiple compute nodes. Each node is further divided into slices, with each slice processing a subset of data in parallel. Redshift can be deployed in a single-node or multi-node cluster, with the latter providing better performance for large datasets. |
| License | GNU GPLv2 |
Closed source |
| Use Cases | Web applications, transaction processing, e-commerce |
Business analytics, large-scale data processing, real-time dashboards, data integration, machine learning |
| Scalability | Supports replication and sharding for horizontal scaling, as well as query optimization and caching for improved performance |
Supports scaling storage and compute independently, with support for adding or removing nodes as needed |
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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.
AWS Redshift Overview
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It was launched in 2012 as part of the AWS suite of products. Redshift is designed for analytic workloads and integrates with various data loading and ETL tools, as well as business intelligence and reporting tools. It uses columnar storage to optimize storage costs and improve query performance.
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.
AWS Redshift for Time Series Data
AWS Redshift can be used for time series data workloads, although Redshift is optimized for more general data warehouse use cases. Users can utilize date and time-based functions to aggregate, filter, and transform time series data. Redshift also offers ‘time-series tables’ which allow data to be stored in tables based on a fixed retention period.
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.
AWS Redshift Key Concepts
- Cluster: A Redshift cluster is a set of nodes, which consists of a leader node and one or more compute nodes. The leader node manages communication with client applications and coordinates query execution among compute nodes.
- Compute Node: These nodes store data and execute queries in parallel. The number of compute nodes in a cluster affects its storage capacity and query performance.
- Columnar Storage: Redshift uses a columnar storage format, which stores data in columns rather than rows. This format improves query performance and reduces storage space requirements.
- Node slices: Compute nodes are divided into slices. Each slice is allocated an equal portion of the node’s memory and disk space, where it processes a portion of the loaded data.
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.
AWS Redshift Architecture
Redshift’s architecture is based on a distributed and shared-nothing architecture. A cluster consists of a leader node and one or more compute nodes. The leader node is responsible for coordinating query execution, while compute nodes store data and execute queries in parallel. Data is stored in a columnar format, which improves query performance and reduces storage space requirements. Redshift uses Massively Parallel Processing (MPP) to distribute and execute queries across multiple nodes, allowing it to scale horizontally and provide high performance for large-scale data warehousing workloads.
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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.
AWS Redshift Features
Scalability
Redshift allows you to scale your cluster up or down by adding or removing compute nodes, enabling you to adjust your storage capacity and query performance based on your needs.
Performance
Redshift’s columnar storage format and MPP architecture enable it to deliver high-performance query execution for large-scale data warehousing workloads.
Security
Redshift provides a range of security features, including encryption at rest and in transit, network isolation using Amazon Virtual Private Cloud (VPC), and integration with AWS Identity and Access Management (IAM) for access control.
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.
AWS Redshift Use Cases
Data Warehousing
Redshift is designed for large-scale data warehousing workloads, providing a scalable and high-performance solution for storing and analyzing structured data.
Business Intelligence and Reporting
Redshift integrates with various BI and reporting tools, enabling organizations to gain insights from their data and make data-driven decisions.
ETL and Data Integration
Redshift supports data loading and extraction, transformation, and loading (ETL) processes, allowing you to integrate data from various sources and prepare it for analysis.
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.
AWS Redshift Pricing Model
Amazon Redshift offers two pricing models: On-Demand and Reserved Instances. With On-Demand pricing, you pay for the capacity you use on an hourly basis, with no long-term commitments. Reserved Instances offer the option to reserve capacity for a one- or three-year term, with a lower hourly rate compared to On-Demand pricing. In addition to these pricing models, you can also choose between different node types, which offer different amounts of storage, memory, and compute resources.
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