MariaDB has had support for histograms as part of Engine Independent Table Statistics since 10.0. As part of Google Summer of Code (MDEV-21130), Michael Okoko, together with his mentor Sergey Petrunia, have implemented a new format (using JSON) for histograms that significantly improves the accuracy and flexibility of histograms. For those just interested in the feature details, you can skip to the “New format”, however if one is unfamiliar with the purpose of histograms, read on.
Histograms are important for queries where the WHERE clause uses columns that are not indexed.
MindsDB is an AutoML framework that lets software engineers do machine learning, without having to go through the whole data science pipeline. Additionally, MindsDB has done a seamless integration with MariaDB, by making use of the Connect Storage Engine.
If you want to learn more about how you can do AI straight from inside MariaDB, register for the webinar on 18th of May 16:00 GMT. MariaDB Foundation, together with MindsDB will cover the following topics in detail:
- Why AI inside the database makes sense
- How MariaDB is built to facilitate AI integrations.
You might have heard the story of how we picked a sea-lion as our logo. Now that this lovely beast has been with us for a while, we think it’s high time to give it a name and of course we turn to our wonderful community for suggestions.
The rules are simple: complete this form, telling us what you think we should name the sea lion and why. Later, Maria Widenius will pick her favorite from the submissions, and the winner will get a collection of MariaDB t-shirts and other swag, an artwork by Maria, and of course eternal fame!
Machine learning is one area that cannot succeed without data. Traditionally, machine learning frameworks read it from CSV files or similar data sources. This brings an interesting set of challenges because in most cases the data is stored in databases, not simple raw files. It takes time and effort to move data from one format to another. Additionally, one needs to write some code (usually python) to prepare the data just like the ML framework expects it.
My primary motivation to contribute to the open source community is because I strongly believe in the idea that software is free, to copy, modify and study.
FOSDEM gives energy. FOSDEM gives ideas. FOSDEM opens up opportunities, FOSDEM allows you to connect with old friends and colleagues. Hence, no big surprise that MariaDB Foundation attended FOSDEM, in order to promote Open Source and to get ourselves closer to the community.
Starting from a pre-FOSDEM dinner with Member of the European Parliament Nils Torvalds, over Open Source Diva Danese Cooper’s keynote about “Open Source is Art”, to the MySQL, MariaDB and Friends Devroom and Sunday’s MariaDB Day, concluding with OpenForum Europe’s meeting on Monday, my previous weekend was packed with encounters, discussions, and ideas around Open Source.
Galera Cluster for MySQL is a 100% synchronized cluster in regards to data modification operations (DML). It is ensured by the optimistic locking model and ability to rollback a transaction, which cannot be applied on all nodes. However, schema changes (DDL operations) are not transactional in MySQL, which adds complexity when you need to perform an upgrade or change schema of the database.
Changes made by DDL may affect results of the queries. Therefore all modifications must replicate on all nodes prior next data access. For operations which run momentarily it can be easily achieved, but schema changes may take hours to apply.
Galera R&D team is currently finalizing new features targeted for the next MariaDB 10.5 release. This presentation is a high level overview of the most prominent Galera clustering features under work, such as:
* Non Blocking DDL – for less turbulence of schema upgrades in a cluster
* Cluster Error Voting – for immediate recovery of node failures
* XA transaction Support – for executing XA transactions in a multi-master cluster
* GTID consistency – for consistent GTIDs in hybrid cluster / replication topologies
* Black Box – cluster hardening through more detailed diagnostics