This page shows how to configure liveness, readiness and startup probes for containers. The kubelet uses liveness probes to know when to restart a container. For example, liveness probes could catch a deadlock, where an application is running, but unable to make progress. Restarting a container in such a state can help to make the application more available despite bugs. A common pattern for liveness probes is to use the same low-cost HTTP endpoint as for readiness probes, but with a higher failureThreshold.
Website Grader - Check your Pages for Speed and SEO
Website Grader is a free online tool that grades your site against key metrics like performance, mobile readiness, SEO, and security. Test your URLs now!
The Web API Checklist -- 43 Things To Think About When Designing, Testing, and Releasing your API
When you’re designing, testing, or releasing a new Web API, you’re building a new system on top of an existing complex and sophisticated system. At a minimum, you’re building upon HTTP, which is built upon TCP/IP, which is built upon a series of tubes. You’re also building upon a web...
Learn how to design REST APIs to be easy to understand for anyone, future-proof, secure, and fast since they serve data to clients that may be confidential.
How do you use API specifications and standards, such as OpenAPI and JSON Schema?
Learn how to use OpenAPI and JSON Schema, two popular standards for describing and validating RESTful APIs and JSON data, in this article for back-end web developers.
Get a comprehensive GA4 audit with an actionable plan using GA4 Auditor - the ultimate Google Analytics 4 audit tool with a detaileda checklist for enhancing your website's tracking
A book about engineering shiny application that will later be sent to production. This book cover project management, structuring your project, building a solid testing suite, and optimizing your codebase. We describe in this book a specific workflow: design, prototype, build, strengthen and deploy.
This book will teach you how to use R to solve your statistical, data science and machine learning problems. Importing data, computing descriptive statistics, running regressions (or more complex machine learning models) and generating reports are some of the topics covered. No previous experience with R is needed.
Mastering Software… by Roger D. Peng et al. [PDF/iPad/Kindle]
This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)