Inherently, Kubernetes clusters are multi-user. As a result, organizations want to ensure that cross-communication is protected via role-based access control, logical isolation and network policies. A container orchestration system such as Kubernetes brings information technology operations and developers (DevOps) closer together, making it easier for teams to collaborate effectively and efficiently with each other.

Kubernetes is quickly becoming the de facto way to deploy workloads on distributed systems. In this post, I will help you develop a deeper understanding of Kubernetes by revealing some of the principles underpinning its design.

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.

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).

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.

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.

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.

Google Kubernetes Engine (GKE) is a managed Kubernetes service that makes it possible to run Kubernetes clusters without managing the underlying infrastructure. With GKE, DevOps teams can scale and deploy applications faster with Kubernetes, while spending less time on cluster maintenance and configuration. Obtaining enough insight into GKE is key to proactively preventing downtime and maximizing application performance.

There’s no denying it — Kubernetes has become the de-facto industry standard for container orchestration. In 2018, AWS, Oracle, Microsoft, VMware and Pivotal all joined the CNCF as part of jumping on the Kubernetes bandwagon. This adoption by enterprise giants is coupled by a meteoric rise in usage and popularity. Yet despite all of this, the simple truth is that Kubernetes is hard.