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The growth and innovation in the Kubernetes project, since it first launched just over four years ago, has been tremendous to see. In part 1 of my blog, I talked about how Red Hat has been a key contributor to Kubernetes since the launch of the project, detailed where we invested our resources and what drove those decisions. Today, that innovation continues and we are just as excited for what comes next.

Today, the Cloud Native Computing Foundation (CNCF) Technical Oversight Committee (TOC) voted to accept etcd as an incubation-level hosted project from KubeCon + CloudNativeCon Seattle. All Kubernetes clusters use etcd as their primary data store. As such, it handles storing and replicating data for Kubernetes cluster state and uses the Raft consensus algorithm to recover from hardware failure and network partitions. In addition to Kubernetes, Cloud Foundry also uses etcd as their distributed key-value store.

Usually, when you’re a leader in an open-source community like Kubernetes and there’s a big event (like this week’s KubeCon North America), that means launching a brand new project. Launches are exciting, but maintaining a successful project like Kubernetes requires sustained investment and maintenance. We find that what really distinguishes a successful open-source project is the day-in day-out nurturing that happens behind the scenes. And it’s more than coding—it’s things like keeping the project safe and inclusive, writing documentation, managing test infrastructure, responding to issues, working in project governance, creating mentoring programs, reviewing pull requests, and participating in release teams.

In the third post of our series on scaling service development, we dive into resilience engineering practices built into the standard service platform that powers the new Services Oriented Architecture atAirbnb. Airbnb is moving its infrastructure towards a Service Oriented Architecture. A reliable, performant, and developer-friendly polyglot service platform is an underpinning component in Airbnb’s architectural evolution. In Part 1 and Part 2 of our Building Services series, we shared how we used Thrift service IDL-centered service framework to scale the development of services; how a standardized service platform encourages and enforces infrastructure standards; and how to enforce best practices to for all new services without incurring additional development overhead.

Uber AI Labs introduces a method for making neural networks that process images faster and more accurately by leveraging JPEG representations. Neural networks, an important tool for processing data in a variety of industries, grew from an academic research area to a cornerstone of industry over the last few years. Convolutional Neural Networks (CNNs) have been particularly useful for extracting information from images, whether classifying them, recognizing faces, or evaluating board positions in Go.

It’s been almost a year since support for the Go programming language in AWS Lambda was announced at re:Invent 2017. Meanwhile the Serverless hype train has gained full steam, FaaS offerings of all major cloud providers have reached a respectable level of maturity and an increasing amount of teams are building applications based on the model while figuring out how to best manage software consisting of a set of managed cloud resources and individually deployable functions.

As we approach the end of another year for Red Hat OpenShift and Kubernetes, and another Kubecon, which I believe will be even bigger than the last, it’s a great time to reflect on both where we’ve been and where we’re going. In this blog I will look back over the past 4+ years since Red Hat first got involved in the Kubernetes project, where we have focused our contributions and the key decisions that got us to this point.

Earlier this year at KubeCon in Copenhagen, the message from the community was resoundingly clear: “this year, it’s about security”. If Kubernetes was to move into the enterprise, there were real security challenges that needed to be addressed. Six months later, at this week’s KubeCon in Seattle, we’re happy to report that the community has largely answered that call. In general, Kubernetes has made huge security strides this year, and giant strides on Google Cloud.

Knative, the open-source framework that provides serverless building blocks for Kubernetes, is on a roll, and GKE serverless add-on, the first commercial Knative offering that we announced this summer, is enjoying strong uptake with our customers. Today, we are announcing that we’ve updated GKE serverless add-on to support Knative 0.2. In addition, today at KubeCon, RedHat, IBM, and SAP announced their own commercial offerings based on Knative. We are excited for this growing ecosystem of products based on Knative.

Uber, like most large technology companies, relies extensively on metrics to effectively monitor its entire stack. From low-level system metrics, such as memory utilization of a host, to high-level business metrics, including the number of Uber Eats orders in a particular city, they allow our engineers to gain insight into how our services are operating on a daily basis. As our dimensionality and usage of metrics increases, common solutions like Prometheus and Graphite become difficult to manage and sometimes cease to work.