Articles

Debunking Four Metadata Myths

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Data is being accumulated at an alarming rate due to the growth of unstructured data. Within that is metadata, which allows us to quickly find data files by identifying certain properties. It wasn’t until recently that developers had to pay closer attention due to the overwhelming effect it was having on our data engines. Metadata used to just be a thing that could be stored in memory behind the scenes without any worry, but its expansion presents the growing concern of where to store it and how to manage it.

Every database system, whether SQL or NoSQL, uses a data engine (aka storage engine), embedded or not, to manage how data is stored. Our ever-growing volume of data has outgrown our data engines’ infrastructures and with that, they are stretched to their limits and forced to make painful trade-offs necessary to support the scale and performance requirements of modern datasets.

Source de l’article sur DZONE

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Uses for MariaDB and the Spider Storage Engine

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Spider is a storage engine for the MariaDB Platform that allows you to build distributed databases from a standard MariaDB setup. The technology is not complicated, although the implementation is. This blog will explain how the Spider storage engine works, what it does and will also show some of the use cases.

MariaDB Storage Engines

Before we look at the Spider storage engine, let’s have a quick look at the storage engine concept. A storage engine is the implementation of code that manages the low level of data access in MariaDB. The storage engine handles things such as reading and writing data, row-level locking, if supported, multi-versioning and transaction management, among other things.

Source de l’article sur DZONE

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Making It Easier to Manage a Production PostgreSQL Database

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Manage a Production PostgreSQL Database

The past several years have seen increasing adoption for PostgreSQL. PostgreSQL is an amazing relational database. Feature-wise, it is up there with the best, if not the best. There are many things I love about it — PL/ PG SQL, smart defaults, replication (that actually works out of the box), and an active and vibrant open source community. However, beyond just the features, there are other important aspects of a database that need to be considered.

If you are planning to build a large 24/7 operation, the ability to easily operate the database once it is in production becomes a very important factor. In this aspect, PostgreSQL does not hold up very well. In this blog post, we will detail some of these operational challenges with PostgreSQL. There is nothing fundamentally unfixable here, just a question of prioritization. Hopefully, we can generate enough interest in the community to prioritize these features.

Source de l’article sur DZONE

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