MariaDB ColumnStore user Andrew Ernst from the Institute for Health Metrics and Evaluation discusses how other storage engines have "failed miserably" and why he chose ColumnStore instead.
I am happy to announce the general availability of MariaDB ColumnStore 1.0!
MariaDB ColumnStore is a powerful open source columnar storage engine that unites transactional and analytic processing with a single ANSI SQL front end to deliver a solution that simplifies high-performance, big data analytics.
Today, MariaDB ColumnStore has reached a major milestone – MariaDB ColumnStore 1.0 is now GA with the release of MariaDB ColumnStore 1.0.6 GA. The journey of MariaDB ColumnStore began in January 2016 when our team started building ColumnStore. The support from our early alpha and beta adopters and community users has helped us take MariaDB ColumnStore from the first alpha release to the GA today.
In this blog post, I will outline MariaDB ColumnStore's architecture, which has the capacity to handle large datasets and scale out across multiple nodes as your data grows.
With the recent announcement of MariaDB ColumnStore, we get many questions on the architecture and functionality of MariaDB ColumnStore. This blog post describes the architecture of MariaDB ColumnStore.
Today, MariaDB ColumnStore, MariaDB's distributed columnar storage engine for analytics workload, is reaching its next major milestone – the availability of MariaDB ColumnStore 1.0.4 Beta software release.
MariaDB ColumnStore is built on a three-tier scalable architecture that supports the kind of growth that MariaDB users have grown accustomed to. Queries are processed by user modules, which assign tasks to parallel performance modules that access the columnar distributed storage layer below. Performance modules scale almost infinitely, providing both performance and capacity growth as you add servers. They also provide built-in data redundancy. These modules don’t process queries; they just take instructions from the user modules, which organize and deliver the results.
Relational databases store data in rows because a typical SQL query looks for multiple fields within a record. For example, if you ask for name, zip code and email address of all your customers in New York, the result is presented in rows, with each row containing several fields from a single record. Row structures are also well optimized to handle a lot of inserts and updates.
MariaDB’s new analytics engine – MariaDB ColumnStore - has been in the works for some time. What is it and how did it come about? This post outlines our thinking in choosing the engine and features we implemented.