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Brian Martin explores the issues of resolution in performance monitoring, covers sources of performance telemetry including hardware performance and eBPF, and talks about some tricks for getting high resolution telemetry without high costs. Source: infoq.com

Containers have become the norm, reaching 84% use in production this year, up from 23% in our first survey in 2016. Many new projects in serverless, service mesh, and storage are emerging as popular ways to manage or work in conjunction with containers. The use of cloud native projects in production continues to grow, with many projects reaching more than 50% use in production. This includes more than half of CNCF’s graduated projects: Kubernetes (78%), Prometheus (72%), CoreDNS (69%), Fluentd (64%), and contianerd (53%).

This article reflects on previous infrastructure solutions and describes a new deployment specification that enables us to collaborate with game shards operators around the world. What’s it like to be a part of Riot Tech? Explore everything from titles and personal development to tech challenges and philosophies. Welcome back to the Running Online Services series! This long-running series explores and documents how Riot Games develops, deploys, and operates its backend infrastructure.

In this article i’m going to introduce you to one useful technique for delivering automatically web application serenely : the Canary deployment method. Canary is a type of deployment method that reduce the risk of introducing a new software version in production by gradually shifting traffic to the new version while measuring metrics like Http request success rate and latency. It allows do to capacity testing of the new version in a production environment with a safe rollback strategy if issues are found.

Istio 1.5 introduces the Istiod binary, which significantly simplifies Istio’s architecture, improving operational experience. One thing we’ve heard from the mesh administrators and operators who use Istio is that its complexity makes it hard to adopt and integrate with their current stack. I’ve been involved with the development of Istio since Istio 0.6, and I’ve seen it become increasingly complex over time. Enter Istio 1.5. This release has many new features, but those features are dwarfed by a major improvement.

Uber developed Piranha to seamlessly delete code related to obsolete feature flags, leading to improved developer productivity and a cleaner codebase. At Uber, we use feature flags to customize our mobile app execution, serving different features to different sets of users. These flags allow us to, for example, localize the user’s experience in different regions where we operate and, more importantly, to gradually roll-out features to our users and experiment with different variations of the same functionality.

Multi-tenancy lets Uber tag requests coming into our microservice architecture, giving us the flexibility to route requests to specific components, such as during testing scenarios. The performance of Uber’s services relies on our ability to quickly and stably launch new features on our platform, regardless of where the corresponding service lives in our tech stack. Foundational to our platform’s power is its microservice-based architecture, a commonly used structural style where applications consist of interoperating services.

He’s been remarkably transparent with changes he’s made to make running it more cost-efficient—and since he’s running this in a personal account, this is real money rather than AWS Credits (motto: “Like Bitcoin but less obnoxious”). The entire system is open-sourced and, after a bit of bargaining, he let me into his account to take a look at the bill. In this post, we tear down what he’s running, how it could be optimized, and what it takes to get there.

Over the past few months, I’ve been helping Lyft build the initial version of our Figma design systems library, which contains all of the colors, text styles, and components used by designers across the company. As we worked on the project, we began to notice patterns of time-consuming tasks, some examples being: Generating helpful descriptions for our 500+ color styles. Optimizing each of the 900+ icons in our icon library.

Kubernetes 1.17 became generally available on Dec. 9 bringing with it a host of new stable enhancements, but what’s perhaps more interesting is not what’s in that release, but what’s missing. The release notes identify 22 enhancements in total, which is half what was originally expected to debut in the release. Among the enhancements that were originally planned for the release but didn’t end up making it into Kubernetes 1.