When running a database as part of your application it’s important that you proactively monitor, perform maintenance and tune your queries on a regular basis.
Especially in cloud based installations, at the outset, you need to consider what services and subsciption levels or features you might receive that can give you fault tollerent infrastructure with load balanced or higher performance at scale out.
For example: There’s little point running your Live database on the Azure platform in ‘Basic’ mode when you want to use geo location recovery or need high guaranteed IOPS.
Monitoring the correct method will give you the correct insight into why your database sometimes is a bottleneck or you want you know when to build an addition instance for scalability.
Apache Messos and Kubernetes compared directly as container orchestrators to each other but they where built with different goals in mind.
Messos was designed to simply and manage fault tolerant elastic services, such as Kubernetes. Kubenetes however was designed to manage containers
Why does this matter for container orchestration? Ultimately, most
developers want the ease and feature set of a PaaS to deploy their
applications, but both developers and PaaS’s tend to be opinionated
about their technologies and workloads, so one size fits all PaaS’s
rarely succeed broadly.
The Mesos team recognised this early and designed Mesos so users
could build opinionated workflows on top of it without being
Kubernetes launched a great API and CLI that most developers love.Mesosphere saw the potential and invested in bringing the tools into Messos.
Mesos has put together a short history of mesos and container
orchestrators by focusing on different container orchestrators and
the companies who use them.
Atomist provides the framework for software delivery. It’s like Rails or Spring Boot for delivering your software.
Atomist automates your software deliver experience. Teams often deliver modern software using this tool.
Cloud native applications are different, with many small, fast-moving services. Each service has its own pipeline for steps such as code formatting, vulnerability scanning, tests against staging instances and production deployment. Each pipeline integrates various tools. All these steps and tools across many services quickly become overwhelming.