Our backend infrastructure at Tinder relies on Redis-based caching to fulfill the requests generated by more than 2 billion uses of the Swipe® feature per day and hosts more than 30 billion matches to 190 countries globally. Most of our data operations are reads, which motivates the general data flow architecture of our backend microservices.
Source: medium.com
What’s the best way to safely increase parallelism in a production Node service? That’s a question my team needed to answer a couple of months ago. We were running 4,000 Node containers (or ‘workers’) for our bank integration service.
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At LinkedIn, we like to say that experimentation is in our blood because no production release at the company happens without experimentation; by “experimentation,” we typically mean “A/B testing.” The company relies on employees to make decisions by analyzing data. Experimentation is a data-driven foundation of the decision-making process, which helps with measuring the precise impact of every change and release, and evaluating whether expectations meet reality.
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I started to compile a list of public failure/horror stories related to Kubernetes. It should make it easier for people tasked with operations to find outage reports to learn from. Since we started with Kubernetes at Zalando in 2016, we collected many internal postmortems.
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