Today Rancher Labs is announcing a new open source project, k3s, which is a lightweight, easy to install Kubernetes distribution geared towards resource-constrained environments and low touch operations. Some use cases in which k3s really shines are edge, ARM, IoT, and CI. The work for k3s started as a component of Rio, an experimental project we started last year.

Peanut butter and jelly, donuts and coffee, Wendy’s Frosties and french fries — these combinations just work. We don’t ask why; we accept nature’s gift and enjoy. We hope you also accept and enjoy our gift of another great pair: Sentry and GitLab. In fact, Sentry and GitLab already go together so well that users who have installed this integration successfully resolve issues triaged in-platform 73% of the time.

On LinkedIn, there are more than thirty thousand U.S.-based DevOps positions. That number may not be as high as it is for software developers, but it’s still higher than sysadmins or systems engineers. Maybe this demand has you interested in switching career paths, or maybe you want to change jobs. If that’s the case, you’re in luck. Today’s post will present some common interview questions that you’ll probably get when interviewing for a DevOps position.

Deploying an application on Kubernetes can require a number of related deployment artifacts or spec files: Deployment, Service, PVCs, ConfigMaps, Service Account — to name just a few. Managing all of these resources and relating them to deployed apps can be challenging, especially when it comes to tracking changes and updates to the deployed application (actual state) and its original source (authorized or desired state).

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we explore serverless computing and how it can impact business operations.

Redis (which stands for REmote DIctionary Server) is an open source, in-memory datastore, often used as a database, cache or message broker. It can store and manipulate high-level data types like lists, maps, sets, and sorted sets. Because Redis accepts keys in a wide range of formats, operations can be executed on the server, which reduces the client’s workload. It holds its database entirely in memory, only using the disk for persistence.

As we explained in Part 1, it’s important to monitor task status and resource use at the level of ECS constructs like clusters and services, while also paying attention to what’s taking place within each host or container. In this post, we’ll show you how Datadog can help you: Automatically collect metrics from every layer of your ECS deployment, Track data from your ECS cluster, plus its hosts and running services in dashboards, and more.

Amazon Elastic Container Service (ECS) is an orchestration service for Docker containers running within the Amazon Web Services (AWS) cloud. You can declare the components of a container-based infrastructure, and ECS will deploy, maintain, and remove those components automatically. The resulting ECS cluster lends itself to a microservice architecture where containers are scaled and scheduled based on need.

In Part 1, we introduced a number of key metrics that you can use for ECS monitoring. Monitoring ECS involves paying attention to two levels of abstraction: the status of your services, tasks, and containers, as well as the resource use from the underlying compute and storage infrastructure, monitored per EC2 host or Docker container. In this post, we’ll survey some techniques you can use to monitor both levels of your ECS deployment.