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Containers

Top 7 Docker Alternatives

A container is a separate unit of software that encapsulates the software and libraries of an application, including its dependencies and its code. As a means of developing and managing stable applications, many organisations are adopting containers. Millions of applications currently use Docker, one of the most useful tools in this space. A Docker container can be used to build, run, inspect, and manage container images for developing applications.

Kubernetes Load Testing: Speedscale vs NeoLoad

In this article, you’ll be introduced to two tools: Speedscale and NeoLoad. Both of these tools offer you a way to load test your applications. This post will compare their ease of setup, development experience, fit within a modern infrastructure, and integration into CI/CD. Load testing is not a new concept in any way: the term was common even before Google Trends started recording data in 2004.
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High Scale Postman Load Testing for Kubernetes

In this Postman load testing tutorial, you'll learn how to run a large scale load test in Kubernetes using your existing Postman collections. Because HTTP services don't have a graphical user interface, it's common to build collections of requests using Postman during the development process. These collections are useful for running quick functionality tests as you develop each endpoint. However, as the service grows you eventually need to test it in a more realistic way with larger volume. This is called a load or stress test. Speedscale is a Production Data Simulation Platform that includes this stress/load testing capability out of the box.

Production Data Simulation: Record in One Environment, Replay in Another

Have you ever experienced the problem where your code is broken in production, but everything runs correctly in your dev environment? This can be really challenging because you have limited information once something is in production, and you can’t easily make changes and try different code. Speedscale production data simulation lets you securely capture the production application traffic, normalize the data, and replay it directly in your dev environment.

Stop Using TCP Health Checks for Kubernetes Applications

As developers, one of the most important things we can consider when designing and building applications is the ability to know if our application is running in an ideal operating condition, or said another way: the ability to know whether or not your application is healthy. This is particularly important when deploying your application to Kubernetes. Kubernetes has the concept of container probes that, when used, can help ensure the health and availability of your application.

Considerations When You Mock APIs Inside of Kubernetes

Today it’s not unusual to see organizations having implemented mocking in their daily workflow, as mock APIs allow developers to speed up their development and not rely on external services. For those reasons and others, many engineers are looking to learn more about the mocked APIs and how they can best be implemented into their organization.

How to Test Autoscaling in Kubernetes

In an ideal world, you want to have precisely the capacity to manage the requests of your users, from peak periods to off-peak hours. If you need three servers to attend to all the requests at peak periods and just one server at off-peak hours, running three servers all the time is going to drive up expenses, and running just one server all the time is going to mean that during peak periods, your systems will be overwhelmed and some clients will be denied service.

10 Critical Kubernetes Tools and How to Debug Them

Kubernetes is both revolutionary and “diffusionary.” It is a complete restructuring demanding a whole new slew of companion and support tools to cover and prop up the entire ecosystem. There are literally hundreds of tools – both open-source and proprietary – designed specifically with k8s in mind.

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Datadog & Speedscale: Improve Kubernetes App Performance

By combining traffic replay capabilities from Speedscale with observability from Datadog, SRE Teams can deploy with confidence. It makes sense to centralize your monitoring data into as few silos as possible. With this integration, Speedscale will push the results of various traffic replay conditions into Datadog so it can be combined with the other observability data. Being able to preview application performance by simulating production conditions allows better release decisions. Moreover, a baseline to compare production metrics can provide even earlier signals on degradation and scale problems. Speedscale joined the Datadog Marketplace so customers can shift-left the discovery of performance issues.