Few weeks back, I gave a presentation in Container conference, Bangalore comparing different solutions available to deploy Docker in the public cloud.
Slides are available here. I have also put the steps necessary along with short video for each of the options in the github page here.
Abstract of the talk:
Containers provide portability for applications across private and public clouds. Since there are many options to deploy Docker Containers in public cloud, customers get confused in the decision making process. I will compare Docker machine, Docker Cloud, Docker datacenter, Docker for AWS, Azure and Google cloud, AWS ECS, Google Container engine, Azure Container service. A sample multi-container application will be deployed using the different options. The deployment differences including technical internals for each option will be covered. At the end of the session, the user will be able to choose the right Docker deployment option for their use-case.
- I have focused mainly on Docker centric options in the comparison.
- There are few CaaS platforms like Tectonic, Rancher that I have not included since I did not get a chance to try them.
- Since all the solutions are under active development, some of the gaps will get covered by the solutions in the future.
In this blog, I have captured some of my learnings on Docker compose files and how they differ between versions. Docker compose is a tool used for defining and running multi-container Docker applications. I have used the famous multi-container voting application to illustrate the differences with compose versions.
Following are some questions that I have to tried to answer in this blog:
- What is the difference between Compose versions 1, 2 and 3?
- What is the difference between compose, stack and dab formats?
- What are different ways to run compose files with different compose versions?
- How does “docker stack deploy” really work?
Following table captures the main differences between Compose versions:
Continue reading Comparing Docker compose versions
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.
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:
Continue reading Kubernetes CRI and Minikube
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.
Continue reading Docker 1.13 Experimental features
Public clouds have grown tremendously over the last few years and there are very few companies who do not use public cloud at this point. Even traditional enterprises with in-house data centers have some presence in the public cloud. I was looking at Amazon’s re:Invent conference details and I was amazed by the number of new services and enhancements that were announced this year. It is very difficult for private clouds to keep up in pace with the new features of public cloud. There is no doubt that public clouds will overtake private clouds in the long term. Private clouds still have a wide deployment and there will be enough use cases for quite some time to deploy private cloud. The use cases includes regulated industries, compute needed in remote locations not having access to public cloud and some specialized requirements that public clouds cannot meet. For some enterprises, private cloud would make more sense from a costing perspective. Having hybrid cloud option is a safe bet for most companies as it provides the best of both worlds. I saw 2 recent announcements in hybrid cloud that captured my attention. One is Azure stack that allows running Azure stack in private cloud. Another is VMWare cloud on AWS that allows running entire VMware stack in AWS public cloud. I see these two services as 2 ends of the hybrid cloud spectrum. In 1 case, public cloud infrastructure software is made to run on private cloud(Azure stack) and in another case, private cloud infrastructure software is made to run on public cloud(Vmware cloud on AWS). In this blog, I have tried to capture more details on these 2 services.
There are predominantly 2 options currently to run Private cloud. 1 option is to use vendor based cloud management software along with hardware from same vendor.
Continue reading Hybrid cloud recent solutions from Microsoft and VMWare – 2 different ends of the hybrid cloud spectrum
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:
Continue reading Docker in Docker and play-with-docker
This link has the slides that I presented as part of lightning talk at Devops Days India, 2016. In the slides, I have tried to capture how automation in networking area is evolving. I attended first day of the conference and it had a pretty decent collection of talks in Devops area.