Neha Gupta
Co-Founder, Keploy
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Co-Founder and core maintainer of Keploy API Testing Platform. Former Openlab Developer @CERN. Contributor to @xwiki, @jenkinsX. Led data and platform engineering team Fareye and Lenskart. Loves talking about new open-source tech, women in tech, testing automation.
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Increasing Application Robustness in Kubernetes at Scale: Service Mesh-Based Techniques
Enhancing app robustness in Kubernetes, especially when dealing with large scales, can seem tough. With this talk I'll make this easier to grasp. We'll zero in on how service meshes like Istio & Linkerd can be used to improve how we analyze traffic and spot regressions in big-scale operations.
From my own experiences, I'll show you how these service mesh techniques have really upped the game in app performance and reliability when things get big. Take, for example, how we used service mesh data to mimic really busy traffic times. This helped us find and fix weak spots early, making our systems lot tougher.
Key Takeaways:
- Understanding the nuts and bolts of boosting app toughness in Kubernetes at large scales.
- How service meshes can be a big help in handling traffic and testing for issues.
- Real-life stories that show how these methods made a difference.
- Practical steps to bring these strategies into your Kubernetes projects.
API Testing Without Writing Test Cases, Assertions and Data Mocks
Application testing is one of the biggest barriers to achieving truly Continuous Deployments because it's use-case specific. Developers often avoid writing test cases because its time consuming, needs to be maintained for every change and metrics like coverage don't necessarily guarantee quality.
In this session, we'll talk about how we could capture test cases from traffic data, how all infrastructure can be mocked automatically and how application writes could be safely replayed. We'll walk through examples of how keploy can work alongside existing testing frameworks and capture test cases quickly and mock infrastructure without needing to write Unit API test cases. We'll also cover how these test cases would evolve as the application grows.
The core contributors to Keploy will provide an overview of its features and capabilities, and how it is used at scale covering use-cases across microservices across various programming languages.
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