TL;DR notes from articles I read today.
Tips for running scalable workloads on Kubernetes
- 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
Non-functional requirements: Quality
- 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
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