Tag Archives: Containers & Kubernetes

Kubernetes CRI and Minikube

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Kubernetes CRI(Container runtime interface) is introduced in experimental mode in Kubernetes 1.15 release. Kubernetes CRI introduces a common Container runtime layer that allows for Kubernetes orchestrator to work with multiple Container runtimes like Docker, Rkt, Runc, Hypernetes etc. CRI makes it easy to plug in a new Container runtime to Kubernetes. Minikube project simplifies Kubernetes installation for development and testing purposes. Minikube project allows Kubernetes master and worker components to run in a single VM which facilitates developers and users of Kubernetes to easily try out Kubernetes. In this blog, I will cover basics of Minikube usage, overview of CRI and steps to try out CRI with Minikube.

Minikube

Kubernetes software is composed of multiple components and beginners normally get overwhelmed with the installation steps. It is also easier to have a lightweight Kubernetes environment for development and testing purposes. Minikube has all Kubernetes components in a single VM that runs in the local laptop. Both master and worker functionality is combined in the single VM.

Following are some major features present in Minikube:

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Docker 1.13 Experimental features

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Docker 1.13 version got released last week. Some of the significant new features include Compose support to deploy Swarm mode services, supporting backward compatibility between Docker client and server versions, Docker system commands to manage Docker host and restructured Docker CLI. In addition to these major features, Docker introduced a bunch of experimental features in 1.13 release. In every release, Docker introduces few new Experimental features. These are features that are not yet ready for production purposes. Docker puts out these features in experimental mode so that it can collect feedback from its users and make modifications when the feature gets officially released in the next set of releases. In this blog, I will cover the experimental features introduced in Docker 1.13.

Following are the regular features introduced in Docker 1.13:

  • Deploying Docker stack on Swarm cluster with Docker compose.
  • Docker cli with Docker daemon backward compatibility. This allows newer Docker CLI to talk to older Docker daemons.
  • Docker cli new options like “docker container”, “docker image” to collect related commands in docker sub-keyword.
  • Docker system details using “docker system” – This helps in maintaining Docker host for cleanup and to get Container usage details
  • Docker secret management
  • docker build with compress option for slow connections

Following are the 5 features introduced in experimental mode in Docker 1.13:

  • Experimental daemon flag to enable experimental features instead of having separate experimental build.
  • Docker service logs command to view logs for a Docker service. This is needed in Swarm mode.
  • Option to squash image layers to the base image after successful builds.
  • Checkpoint and restore support for Containers.
  • Metrics (Prometheus) output for basic container, image, and daemon operations.

Experimental Daemon flag

Docker released experimental features prior to 1.13 release as well. In earlier release, users needed to download a new Docker image to try out experimental features. To avoid this unnecessary overhead of having different images, Docker introduced a experimental flag or option to Docker daemon so that users can start the Docker daemon with or without experimental features. With Docker 1.13 release, Docker experimental flag is in experimental mode.

By default, experimental flag is turned off. To see the experimental flag, check Docker version.

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Docker in Docker and play-with-docker

Containers & Kubernetes,

For folks who want to get started with Docker, there is the initial hurdle of installing Docker. Even though Docker has made it extremely simple to install Docker on different OS like Linux, Windows and Mac, the installation step prevents folks from getting started with Docker. With Play with Docker, that problem also goes away. Play with Docker provides a web based interface to create multiple Docker hosts and be able to run Containers. This project is started by Docker captain Marcos Nils and is an open source project. Users can run regular containers or build Swarm cluster between the Docker hosts and create container services on the Swarm cluster. The application can also be installed in the local machine. This project got me interested in trying to understand the internals of the Docker host used within the application. I understood that Docker hosts are implemented as Docker in Docker(Dind) containers. In this blog, I have tried to cover some details on Dind and Play with Docker.

Docker in Docker(Dind)

Docker in Docker(Dind) allows Docker engine to run as a Container inside Docker. This link is the official repository for Dind. When there is a new Docker version released, corresponding Dind version also gets released. This link from Jerome is an excellent reference on Docker in Docker that explains issues with Dind, cases where Dind can be used and cases where Dind should not be used.

Following are the two primary scenarios where Dind can be needed:

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Vault – Use cases

Cloud Platforms, Containers & Kubernetes, DevOps & Automation, Distributed Systems, Security, , , ,

This blog is a continuation of my previous blog on Vault. In the first blog, I have covered overview of Vault. In this blog, I will cover some Vault use cases that I tried out.

Pre-requisites:

Install and start Vault

I have used Vault 0.6 version for the examples here. Vault can be used either in development or production mode. In development mode, Vault is unsealed by default and secrets are stored only in memory. Vault in production mode needs manual unsealing and supports backends like Consul, S3.

Start Vault server:

Following command starts Vault server in development mode. We need to note down the root key that will be used later.

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Service Discovery and Load balancing Internals in Docker 1.12

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Docker 1.12 release has revamped its support for Service Discovery and Load balancing. Prior to 1.12 release, support for Service discovery and Load balancing was pretty primitive in Docker. In this blog, I have covered the internals of Service Discovery and Load balancing in Docker release 1.12. I will cover DNS based load balancing, VIP based load balancing and Routing mesh.

Technology used

Docker service discovery and load balancing uses iptables and ipvs features of Linux kernel. iptables is a packet filtering technology available in Linux kernel. iptables can be used to classify, modify and take decisions based on the packet content. ipvs is a transport level load balancer available in the Linux kernel.

Sample application

Following is the sample application used in this blog:

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Comparing Swarm, Swarmkit and Swarm Mode

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One of the big features in Docker 1.12 release is Swarm mode. Docker had Swarm available for Container orchestration from 1.6 release. Docker released Swarmkit as an opensource project for orchestrating distributed systems few weeks before Docker 1.12(RC) release. I had some confusion between these three projects. In this blog, I have tried to put my perspective on the similarities and differences between these three software components. I have also created a sample application and deployed it using the three approaches which makes it easier to compare.

Docker Swarm mode is fundamentally different from Swarm and it is confusing to use the same name Swarm. It would have been good if Docker could have renamed this to something different. Another point adding to the confusion is that native Swarm functionality will continue to be supported in Docker 1.12 release, this is done to preserve backward compatibility. In this blog, I have used the term “Swarm” to refer to traditional Swarm functionality, “SwarmNext” to refer to new Swarm mode added in 1.12, “Swarmkit” to refer to the plumbing open source orchestration project.

Swarm, SwarmNext and Swarmkit

Following table compares Swarm and SwarmNext:

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Mesos and Mesosphere – FAQ

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The most popular Container Orchestration solutions available in the market are Kubernetes, Swarm and Mesos. I have used Kubernetes and Swarm, but never got a chance to use Mesos or DC/OS. There were a bunch of questions I had about Mesos and DC/OS and I never got the time to explore that. Recently, I saw the announcement about Mesosphere opensourcing DC/OS and I found this as a perfect opportunity for me to try out Opensource DC/OS. In this blog, I have captured the answers to questions I had regarding Mesos and DC/OS. In the next blog, I will cover some hands-on that I did with Opensource DC/OS.

What is the relationship between Apache Mesos, Opensource DC/OS and Enterprise DC/OS?

Apache Mesos is the Opensource distributed orchestrator for Container as well as non-Container workloads. Both Opensource DC/OS and Enterprise DC/OS are built on top of Apache Mesos. Opensource DC/OS adds  Service discovery, Universe package for different frameworks, CLI and GUI support for management and Volume support for persistent storage. Enterprise DC/OS adds enterprise features around security, performance, networking, compliance, monitoring and multi-team support that the Opensource DC/OS project does not include. Complete list of differences between Opensource and Enterprise DC/OS are captured here.

What does Mesosphere do and how it is related to Apache Mesos?

Mesosphere company has products that are built on top of Apache Mesos. Lot of folks working in Mesosphere contribute to both Apache Mesos and Opensource DC/OS. Mesosphere has the following products currently:

  • DC/OS Enterprise – Orchestration solution
  • Velocity- CI, CD solution
  • Infinity – Big data solution

Why DC/OS is called OS?

Sometimes folks get confused thinking Mesos being a Container optimized OS like CoreOS, Atomic. Mesos is not a Container optimized OS. Similar to the way Desktop OS provides resource management in a single host, DC/OS provides cluster management across entire cluster. Mesos master(including first level scheduler) and agent are perceived as kernel components and user space components include frameworks, user space applications, dns and load balancers. The kernel provides primitives for the frameworks.

What are Mesos frameworks and why they are needed?

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Looking inside Container Images

Containers & Kubernetes, Networking & SDN, , , , ,

This blog is a continuation of my previous blog on Container standards. In this blog, we will look inside a Container image to understand the filesystem and manifest files that describes the Container. We will cover Container images in Docker, APPC and OCI formats. As mentioned in previous blog, these Container images will converge into OCI format in the long run.

I have picked two Containers for this blog: “nginx”which is a standard webserver and “smakam/hellocounter” which is a Python web application.

Docker format:

To see Container content in Docker format, do the following:

docker save nginx > nginx.tar
tar -xvf nginx.tar

Following files are present:

  • manifest.json – Describes filesystem layers and name of json file that has the Container properties.
  • <id>.json – Container properties
  • <layer directory> – Each “layerid” directory contains json file describing layer property and filesystem associated with that layer. Docker stores Container images as layers to optimize storage space by reusing layers across images.

Following are some important Container properties that we can see in the JSON file:

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Container Standards

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In this blog, I will cover some of the standardization effort that is happening in the Containers area. I will cover some history, current status and also mention how the future looks like. In the next blog, we will look inside ACI and OCI Container images.

Container Standards

Lot of developments in Container area are done as Open source projects. That still does not automatically mean that these projects will become standards. Following are the areas where Container standardization is important:

  • Container image format – Describes how an application is packaged into a Container. The application can be an executable from any programming language. As you would know, Containers packages an application along with all its application dependencies.
  • Container runtime – Describes the environment(namespaces, cgroups etc) necessary to run the Container and the APIs that Container runtime should support.
  • Image signing – Describes how to create Container image digest and to sign these so that Container images can be trusted.
  • Image discovery – Describes alternate approaches to discover Container images other than using registry.
  • Container Networking – This is a pretty complex area and it describes ways to network Containers in same host and across hosts. There are different implementations based on the use-case.

Having common Container standards would allow things like this:

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