Mockery VS kubernetes

Continuous Integration - Testify, sqlmock, Mockery, Github Actions

Mockery is another framework for creating test doubles. It can be used with PHPUnit, phpspec, Behat, or any other testing framework. I find it especially powerful when working with legacy code, due to its support for creating partial mocks or mocking hard dependencies.

This works great if a method is tested that modifies and returns an object; we'll get an instance of that object and we are able to run all sorts of assertions on it. But what if this is not the case. What if we have a situation where a value object is passed as a parameter, some modifications are done and the value object is passed to another object. Something like the middleware from the example earlier. Some testing frameworks like Mockery offer spies to test these situations. When using Prophecy this situation can be handled using Argument::that():

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.

Through the Fast Forward program, we give free services and support to open source projects and the nonprofits that support them. We support many of the world’s top programming languages (like Python, Rust, Ruby, and the wonderful Scratch), foundational technologies (cURL, the Linux kernel, Kubernetes, OpenStreetMap), and projects that make the internet better and more fun for everyone (Inkscape, Mastodon, Electronic Frontier Foundation, Terms of Service; Didn’t Read).

In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.

The single most important development in hosting since the invention of EC2 is defined by its own 3-letter acronym: k8s. Kubernetes has won the “container orchestrator” space, becoming the default way that teams across industries are managing their compute nodes and scheduling their workloads, from data pipelines to web services.

Kubernetes was first released on September 9, 2014. This release timeline is part of what helped it gain a foothold over Docker Swarm. It was an open source version of an internal Google project. Features of container orchestration were presented in a more modular fashion along with scaling functionality. You can chose how your networking stack works, your load balancing, container runtime, and filesystem interfaces. Availability of an API allowed for more programmatic interactions with orchestration, making it tie in very well with CI/CD solutions. However, the big issue it has is complexity of setup. Putting together a Kubernetes cluster with basic functionality is certainly no easy feat.