Let’s look at a sample table first before we go into looking at some code. Since each node focuses on a particular partition and sends work items to other nodes rather than requesting raw data from other nodes, each node’s cache contains more of that node’s data, and less redundant data from other nodes. This means cache hit rates will be much higher, significantly reducing the need for slow disk accesses. Upon node failure, data is rebalanced from remaining nodes, automatically healing the data protection without intervention. In a zone failure, the rebalancer performs the same operation between nodes and remaining zones.

mariadb platform x3

On the back-end, changes made to the MariaDB Servers are sent through MaxScale streaming data adapters to ColumnStore, ensuring that ColumnStore remains up-to-date. If the load on MariaDB Xpand decreases, you can scale down by removing nodes. When you remove an Xpand node from the deployment, the rebalancing process redistributes data to the remaining nodes, ensuring fault tolerance. For read operations, the major part of the query is pushed down to Xpand where the query is evaluated and relevant portions of the query are then sent to the appropriate Xpand nodes.


MariaDB Managed Service supporting public and hybrid cloud deployments. The main challenge with implementing both transactional and analytical workloads is that they require different programming models and different types of data access. Transactional workloads typically require low-latency, point query access to data, while analytical workloads require large-scale scans and aggregations of data. Column https://www.globalcloudteam.com/ oriented data stores are more suitable for analytical workloads because the data format lends itself to faster query processing. These database systems have been shown to perform more than an order of magnitude better than traditional row-oriented database systems. The MaxScale CDC Streaming Data Adapter allows you to stream binary log events from MariaDB Servers to MariaDB ColumnStore clusters.

However, we don’t feel that you need to change everything in order to achieve that. We have a unique architecture with pluggable storage engines that allows the user to adapt the database to the use case and workload without changing the main characteristics and features. We believe that this flexibility serves the best interest of the user mariadb development and we will work on further advancing this with future versions of MariaDB. This architecture will enable both the community and our team to innovate further by adding storage engines designed for new hardware and new use cases. In MariaDB Server 10.3, we introduce two new storage engines that are declared stable, MyRocks and Spider.

New to MariaDB Server?

You can monitor this information to see the binary events its streaming from the MariaDB Servers to MariaDB ColumnStore. When your application issues queries to Platform X3 for HTAP operations, it doesn’t connect to either the MariaDB Servers or to the MariaDB ColumnStore User Modules directly. Instead, it connects to a MaxScale server configured to selectively routes queries, ensuring that OLTP operations execute on MariaDB Servers and OLAP operations execute on ColumnStore.

mariadb platform x3

This guide has been written for the DBA, developer and operator to help you stand up Platform X3 for HTAP queries, unleashing the ability to perform analysis across events as they are happening. It is also a deployment that can scale from the small cluster of the examples below to accommodate more transactions, larger analytical processing and high availability. The Spider storage engine allows you to shard a specific table across multiple nodes. It uses the partitioning protocol to define how the table should be split up and each individual shard will then reside on a remote MariaDB Server that will only handle queries for that particular shard. With Spider you get almost linear scaling for INSERTS and key lookup read queries.

How Does MariaDB Xpand Work?

OLTP data is used for logging, and analysis of OLAP data drives understanding of product losses, replenishment patterns, and equipment failures. Finally, performance is a bit better, in particular when there are many rows of data to INSERT. The ability to load data into MariaDB as program data arrays has several advantages, it is programmatically easier to deal with than a single array string, in particular if the latter consists of data for many rows. Aligning program data contained in classes or similar is also easier, allowing for better code integration. There are two API’s, one that is text-based and this is the original MariaDB API. In this API all data is sent and received as text.

mariadb platform x3

Now called Xpand, it extends MariaDB Enterprise Server with distributed data and transaction processing, transforming it into a distributed SQL database capable of scaling to millions of transactions per second with a shared-nothing architecture. However, Xpand is not an all or nothing, as DBAs can choose to use both replicated and distributed tables. Xpand is good for complex queries and analytics processing as it can perform parallel queries across the available nodes within the cluster. As the expectations of data-driven customers rise, transactional applications need access to more historical data and greater analytics. If you’ve outgrown your database, you shouldn’t have to settle for lightweight analytics. Our sample deployment calls for four servers running MariaDB Server to handle OLTP workloads, which we’ve named Server-1 to Server-4.

Configure for Replication

The MariaDB MaxScale server configuration above designates queries on the bank.loans table as analytical queries and routes them to the MariaDB ColumnStore User Modules rather than the MariaDB Servers. In order to better illustrate how MaxScale distributes queries between the servers, we are going to install a sample banking database and show how to process payments and analyze loan data. When you start streaming data, the mxs_adapter utility begins printing logging messages to stdout. As you add data to the MariaDB Servers, you can check this output to see binary events streaming over to ColumnStore. In MariaDB Replication, one server operates as the master receiving all writes from the application and replicating changes to the cluster.

When you add an Xpand node to the deployment, the rebalancing process redistributes data from the existing nodes. Once complete, the Xpand node can now handle both read and write operations from MariaDB Enterprise Servers. MariaDB ColumnStore, the columnar smart engine, cross engine JOINs are possible between replicated and distributed tables.

General Business Overview

It is part of most cloud offerings and the default in most Linux distributions. As customers, we expect businesses to provide us with useful information. And as our expectations rise, so too must the usefulness of the information.

It’s built for businesses whose customers demand more information and deeper insight. We are happy to announce the general availability of MariaDB Server 10.3! This release is a big milestone for the development of MariaDB Server and is the result of a huge effort by the development team and contributors – thanks to everyone involved! With our previous major release of MariaDB Server 10.2 last year, we started a journey of adding more enterprise-grade features to better close the gap with proprietary databases.

Platform X3 Scaleout

Finally, to terminate the cursor and connection to the database execute the following two lines of code. Covariance and correlation were added to the most recent MariaDB Platform X3 version These are two mathematical concepts which are quite commonly used in business analytics. Both are used to determine the relationship and dependency measure between two random variables.