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At PagerDuty, all of our computing infrastructure is automated using Chef. We push out features and changes to our Chef codebase very frequently – often multiple times a day – and this makes it crucial that we test our Chef code before we deploy it to our production environment. As we have learned, failures can surface from unfrozen dependencies, improper values in config file, logic errors, bugs in underlying libraries, etc.
In this post, we will walk through the 5 sequential testing tools/strategies that we use to create a predictable and stable infrastructure.
We have extracted some key bits and pieces from our specs and made it available here:
These solutions are based upon our current limitations by available tools and technologies we can adopt and integrate. Lot of these tooling deserve to go in their own respective code bases or have their own dedicated libraries, or merged against their corresponding mainline alternatives once the underlying stack is stabilized. But these should provide some concrete examples on top of which we can discuss, ideate and improve. Any feedback on this is welcomed.
Chef has been instrumental to our computing infrastructure. With automated testing, we can continuously deliver code. By decreasing our time to market and increasing the quality of our releases, PagerDuty customers can rely on our resilient, high availability infrastructure.
“Chaos Engineering is the discipline of experimenting on a distributed system in order to build confidence in the system’s capability to withstand turbulent conditions in...
We’re excited to share that we’re open-sourcing the tool we use to gather and transform the metrics from our managed DNS providers. We use DNSmetrics...
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