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How to Create a Kubernetes Preview Environment

A Kubernetes preview environment is an isolated environment that allows developers to test their code at any time without worrying about how others may be affected. While implementations and use cases may vary, simulating a production environment as closely as possible is the main goal. Imagine you're part of a team developing a complex API, and you've been tasked with adding a new endpoint that relies on features within the codebase currently being optimized by one of your team members. Although your team has a development environment with seeded databases and dev versions of dependencies, you run into issues when team members want to test their optimizations at the same time as you.

Data & Traffic Are Key to Kubernetes Preview Environments

Preview environments are temporary environments where developers can test code changes before deploying them to production, also called ephemeral environments, they’re temporary and should be discarded after testing changes. Carrying out tests using accurate data is a major challenge when creating and destroying environments. Put differently, you need realistic data and traffic in the preview environment to reflect the performance of code changes in production.

Continuous Load Testing | A Developer's Guide

Continuous load testing is a powerful way of preparing for surges in traffic, without needing real users. Imagine you’re a software engineer working on a website that’s seen a recent surge in traffic. Despite initial testing indicating that the website should be capable of handling the increased load, the website crashes during peak hours. Load Testing is the process of simulating real-world usage of a website or application.

Building a Mock Server from User Traffic in Kubernetes

A mock server can prove useful in many circumstances. Imagine you’re an engineer working on optimizing a feature inside of an existing API that relies on multiple other microservices to function properly. To fully test the optimizations, you’ll have to set up test versions of all the dependencies, which quickly proves to be quite a task in and of itself. This is where a mocks—a server that simulates the behavior of a real server—can be very beneficial.
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Testing Kubernetes Ingress with Production Traffic

Testing Kubernetes Ingress resources can be tricky, and can lead to frustration when bugs pop up in production that weren't caught during testing. This can happen for a variety of reasons, but with Ingress specifically, it often has to do with a misalignment between the data used in testing and the traffic generated in production. Tools like Postman can be a great way of generating traffic, but they have the drawback of being manually created. Not only is this unlikely to create all the needed variations for a single endpoint (different headers, different request bodies, etc.), it would be almost impossible to create all the needed variations, for all possible endpoints.

Optimize Kubernetes Performance Part 2: Creating Comparisons

The main idea behind Kubernetes is to create a standardized approach to running containers in the cloud. Whether you’re running AKS on Azure or EKS on Amazon, your cluster should still behave in more or less the same way. But that’s not to say you’re locked in to doing things one way; Kubernetes still offers a lot of flexibility in many cases. This is what experienced engineers take advantage of when trying to optimize Kubernetes performance.

Optimize Kubernetes Performance Part 1: Cluster Configurations

Kubernetes is a powerful platform that comes with many features to help engineers run their applications more efficiently. However, as you gain more experience and deploy more workloads, you’ll inevitably start looking for ways to optimize Kubernetes performance. There are many ways to approach optimization. On one hand, you could work exclusively with the tools and configurations provided by Kubernetes itself; on the other, you could reap the benefits of third-party tools.