Error Budgets That Work for You. Plus Support for New Relic Metrics and NR Query Language Did you know that error budget policy is the key to making SLOs actionable? In fact, Twitter’s engineering team did not successfully adopt SLOs until they introduced error budgets. SLOs enable teams to quantify customer happiness, and error budgets enable teams to make data-backed tradeoffs between reliability and feature velocity. We believe that teams optimizing for reliability must adopt both.

The past week has been a turbulent time for Bitbucket Cloud's engineering and support teams as well as our customers. Some of you have expressed concern regarding our services’ performance and reliability in recent days. Fortunately, for the majority of Bitbucket users, our services have continued to perform smoothly. But given the number of developers who use Bitbucket daily, even a small percentage of our active users represents a lot of people.

Rootly is on a mission to create a world where maintaining reliability is frictionless, delightful, and accessible to anyone. Making resolving and learning from incidents every organizations superpower.

Adoption of service meshes like Istio is increasing. As a result, Speedscale has developed a webassembly plugin. We extended Envoy using Rust, and no changes are required to your Istio configuration. This allows us to leverage the same sidecars that you have deployed throughout your environment to inspect API traffic. Once we are listening through Istio, the typical Speedscale magic can take place. We can use the data to build integration/performance test suites and autogenerate service mocks.

In a relatively short amount of time, Kubernetes has evolved from an internal container orchestration tool at Google to the most important cloud-native technology across the world. Its rise in popularity has made Kubernetes the preferred way to build new software experiences and modernize existing applications at scale and across clouds.

Slack experienced meteoric growth between 2017 and 2020—but that level of growth came with growing pains. In his talk at the 2021 o11ycon+hnycon, Frank Chen (LinkedIn), a Slack Senior Staff Engineer, detailed one of Slack’s biggest pain points in that period: flaky tests. A flaky test returns both a passing and failing result despite no changes in the code. At one point, between 2017 and 2020, Slack’s flaky test rate reached as high as 50%.

In a previous post, we discussed what an Internal Developer Platform (IDP) is and some drivers behind IDP initiatives. If we go through our interactions with different organizations, we see teams embarking on the journey to build their IDPs mainly driven by the following requirements: While building an IDP may seem like an obvious choice and initiative, it is definitely not an easy task to accomplish. Building an IDP involves dealing with many moving components.

It’s possible to have more than one DHCP server on the same network and still have everything work right, with no conflicts and no dropped packets or IP requests. It’s really not that hard to pull together, either, but there are some things to know, and some things to consider before we investigate that situation. For this blog, we’ll put some of the overlooked facets of DHCP in bold text. Let’s take a look.

Whether you are downsizing your infrastructure within a single room or eliminating half of your data center sites, a data center consolidation is a complex, risk-prone project. Fortunately, you can mitigate many of the mistakes and unwelcome surprises that even the most experienced data center professionals find derailing their project and instead enjoy the many advantages of a data center consolidation.

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