Illumina Innovates with Rancher and Kubernetes
Since we announced Project Longhorn last year, there has been a great deal of interest in running Longhorn storage on a Kubernetes cluster.
Today, I am very excited to announce the availability of Project Longhorn v0.2, which is a persistent storage implementation for any Kubernetes cluster. Once deployed on a Kubernetes cluster, Longhorn automatically clusters all available local storage from all the nodes in the cluster to form replicated and distributed block storage.
Recently, we announced our second milestone release of Rancher 2.0 Tech Preview 2. This includes the possibility to add custom nodes (nodes that are already provisioned with a Linux operating system and Docker) by running a generated docker run command to launch the rancher/agent container, or by connecting over SSH to that node. In this post, we will explore how we can automate the generation of the command to add nodes using the docker runcommand.
Last month I had the great pleasure of attending Kubecon 2017, which took place in Austin, TX. The conference was super informative, and deciding on what session to join was really hard as all of them were great. But what deserves special recognition is how well the organizers respected the attendees’ diversity of Kubernetes experiences. Support is especially important if you are new to the project and need advice (and sometimes encouragement) to get started.
Today, Amazon announced a managed Kubernetes service called Elastic Container Service for Kubernetes (EKS). This means that all three major cloud providers—AWS, Azure, and GCP—now offer managed Kubernetes services. This is great news for Kubernetes users. Even though users always have the option to stand up their own Kubernetes clusters, and new tools like Rancher Kubernetes Engine (RKE) make that process even easier, cloud-managed Kubernetes installations should be the best choice for the majority of Kubernetes users.
Today, we are announcing a new open-source project called the Rancher Kubernetes Engine (RKE), our new Kubernetes installer. RKE is extremely simple, lightning fast, and works everywhere.
Why a new Kubernetes installer? In the last two years, Rancher has become one of the most popular ways to stand up and manage Kubernetes clusters. Users love Rancher as a Kubernetes installer because it is very easy to use. Rancher fully automates etcd, the Kubernetes master, and worker node operations.
Installing Kubernetes can be one of the toughest problems for operations and DevOps. Learn more about Rancher's lightweight tool for installing Kubernetes.
Rancher 2.0 is out and odds are, you’re wondering what’s so shiny and new about it. Well, here’s a huge selling point for the next big Rancher release; Kubernetes cluster adoption! That’s right, we here at Rancher wanted more kids, so we decided it was time to adopt. In all seriousness though, this feature helps make Rancher more relevant to developers who already have Kubernetes clusters deployed and are looking for a new way to manage them.
Kubernetes is designed to address some of the difficulties that are inherent in managing large-scale containerized environments. However, this doesn’t mean Kubernetes can scale in all situations all on its own. There are steps you can and should take to maximize Kubernetes’ ability to scale—and there are important caveats and limitations to keep in mind when scaling Kubernetes. I’ll explain them in this article.
Scale versus Performance The first thing that must be understood about scaling a Kubernetes cluster is that there is a tradeoff between scale and performance.
In Kubernetes, we often hear terms like resource management, scheduling and load balancing. While Kubernetes offers many capabilities, understanding these concepts is key to appreciating how workloads are placed, managed and made resilient. In this short article, I provide an overview of each facility, explain how they are implemented in Kubernetes, and how they interact with one another to provide efficient management of containerized *workloads. *If you’re new to Kubernetes and seeking to learn the space, please consider reading our case for Kubernetes article.
One of the first questions you are likely to come up against when deploying containers in production is the choice of orchestration framework. While it may not be the right solution for everyone, Kubernetes is a popular scheduler that enjoys strong industry support. In this short article, I’ll provide an overview of Kubernetes, explain how it is deployed with Rancher, and show some of the advantages of using Kubernetes for distributed multi-tier applications.
Note: You can find an updated comparison of Kubernetes vs. Docker Swarm in a recent blog post here.
Recent versions of Rancher have added support for several common orchestration engines in addition to the standard Cattle. The three newly supported engines, Swarm (soon to be Docker Native Orchestration), Kubernetes and Mesos are the most widely used orchestration systems in the Docker community and provide a gradient of usability versus feature sets.
Elasticsearch is a Lucene-based search engine developed by the open-source vendor, elastic. With principal features like scalability, resiliency, and top-notch performance, it has overtaken Apache Solr, one of its closest competitors. Nowadays, Elasticsearch is almost everywhere where a search engine is involved: it’s the E of the well-known ELK stack, which makes it straightforward for your project to process analytics (the L stands for Logstash which is used to process data like logs, streams, metrics; K stands for Kibana, a data visualization platform – projects also managed by elastic).
*Note: Since publishing this post, we’ve created a guide comparing Kubernetes with Docker Swarm. You can read the details in the blog post here..* Over the last six months, Rancher has grown very quickly, and now includes support for multiple orchestration frameworks in addition to Cattle, Rancher’s native orchestrator. The first framework to arrive was Kubernetes, and not long after, Docker Swarm was added. This week, the team at Rancher added support for Mesos.
Elasticsearch is one of the most popular analytics platform for large datasets. It is useful for a range of use-cases ranger from log aggregation, business intelligence as well as machine learning. Elasticsearch is popular because of its simple REST based API which makes it trivial to create indices, add data and make complex queries. However, before you get up and running building your dataset and running queries you need to setup a elasticsearch cluster, which can be a somewhat daunting prospect.
*Quentin Hamard is one of the founders of Octoperf, and is based in Marseille, France. * Octoperf is a full-stack cloud load testing SaaS platform. It allows developers to test the design performance limits of mobile apps and websites in a realistic virtual environment. As a startup, we are attempting to use containers to change the load testing paradigm, and deliver a platfrom that can run on any cloud, for a fraction of the cost of existing approaches.
Visit us to learn more about using Ansible with Docker to deploy a Wordpress service on Rancher. For more tutorials and to request a demo, visit Rancher today.
I have blogged about monitoring docker deployments a couple times now (here & here), however, up to this point we have been monitoring container stats without looking at the bigger picture. How do these containers fit into a larger unit and how we get insights into the deployment as a whole rather than individual containers. In this post I will cover leveraging docker labels and Rancher’s projects and services support to provide monitoring information that understands the deployment structure.
Hussein Galal is a Linux System Administrator, with experience in Linux, Unix, Networking, and open source technologies like Nginx, Apache, PHP-FPM, Passenger, MySQL, LXC, and Docker. You can follow Hussein on Twitter @galal_hussein.
I recently used Docker and Rancher to set up a Redis cluster on Digital Ocean. Redis clustering provides a way to share data across multiple Redis instances, keys are distributed equally across instances using hash slots. Redis clusters provide a number of nice features, such as data resharding and availability between instances.