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We began creating scripts (shell/python) that would serve as utilities to access data from each microservice and quickly ran into issues. ScalabilityĪs the scope of our testing expanded to cover a substantial number of microservices in the Netflix services pipeline, the issue of scalability became more prominent. In the following section, we talk about some of the obstacles we faced and how we developed a solution to overcome this. However, we began to hit the limits of our model much faster than we expected. In our previous post, we spoke about building a common set of utilities - shell/python scripts that would communicate with each microservice in the netflix service ecosystem - which gave us an easy way to fetch data and make data assertions on any part of the data service pipeline.
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We achieve these goals using a mix of manual and automated tests. Our team’s focus still remains on providing test coverage to HITs globally (before and after launch) and ensuring that any A/B test logic is verified thoroughly before test rollout. So in this post, we wanted to talk about our evolution since last year. Since then, we have made significant enhancements to our automation.
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All this while ensuring the pace of innovation at Netflix does not slow down. Less than a year ago, we shared our testing wins and the challenges that lie ahead while testing High Impact Titles (HITs) globally. Authors : Fazal Allanabanda and Vilas Veeraraghavan Introduction