Running scalable workloads on Kubernetes

#todayilearned #kubernetes #programming

TL;DR notes from articles I read today.

You must set resource requests & limits so the Kubernetes scheduler can ensure workloads are spread across nodes evenly.
The scheduler can use configured affinities & anti-affinities as another hint of which node is best to assign your pod to.
In Kubernetes, readinessProbe tells that a pod is ready to start receiving requests and livenessProbe tells that a pod is running as expected. Setting these ensure that requests to service always go to a container that can process the request.
It is common for nodes in Kubernetes to disappear and you should configure a pod disruption budget to ensure you always have a minimum number of ready pods for deployment.

Full post here, 13 mins read

To develop quality code, you should focus on clean code structure, the correctness of implementation and maintainability.
Define a company-wide META architecture for every project to follow, allowing more reuse of code. Use the KISS principle and avoid excessive detailing or heavy customizing.
To keep clean code, always activate the compiler warning, no matter what language you use; define (and monitor) naming, line length, and API documentation conventions like Javadoc; and maintain test coverage of least 85% with not only unit tests but also automated acceptance tests, and regular manual code inspections, especially when working with external vendors.
Rather than using pull requests as a quality gate, use a simpler model such as release branch lines and automate visualization of coupling between classes and modules. Light coupling is easier to maintain and lets you reutilize the module/class more readily.
Avoid third-party libraries, and try to solve any given problem with only one library/technology to reduce security risks.

Full post here, 10 mins read

DEV Community