Key-value stores: an alternative to relational databases
Data Silos
When it comes to structured data storage, admins have viewed relational databases such as MySQL or PostgreSQL to be the ideal solution for decades. No other tool can store large volumes of data in such an efficient way, and virtually no other language can access databases as well as the powerful and versatile SQL. In the meantime, relational databases have become so popular that the top players in this genre have virtually become generic terms for databases themselves, to which the LAMP stack (e.g., Linux, Apache, PHP, MySQL) bears witness to this day.
However, just as LAMP has become less important over the years, the hype surrounding relational databases has died down, at least a little, for understandable reasons: The complexity of a relational database is by no means helpful or even necessary where data needs to be stored in a structured way, which quickly becomes clear when you take a closer look at where databases are used today.
Cloud computing environments have been very popular for many years, which explains why Kubernetes is ubiquitous: The idea of making all the functions of a state-of-the-art data center above the hardware level controllable by API, and of supporting automation by doing so, is too tempting from the perspective of many corporations. On request, Kubernetes will give you persistent storage along with dynamic IP addresses in the context of a software-defined networking (SDN) environment.
OpenStack [1], a project with similar goals and comparable functionality in part, opted for the legacy approach years ago, and it still relies on a relational database (usually MySQL) to store its persistent metadata. Kubernetes went down a different path from the outset. The metadata is stored in a database, as well, but it is not relational. Instead, etcd [2] is a mandatory component of state-of-the-art Kubernetes installations
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