07 October, 2020
Ans: No, it is not. Different variations of containers technology were out there in *NIX world for a long time. Examples are:-Solaris container (aka Solaris Zones)-FreeBSD Jails-AIX Workload Partitions (aka WPARs)-Linux OpenVZ
Ans: Well, Docker is a quite fresh project. It was created in the Era of Cloud, so a lot of things are done much nicer than in other container technologies. The team behind Docker looks to be full of enthusiasm, which is of course very good. I am not going to list all the features of Docker here, but I will mention those which are important to me.
Docker can run on any infrastructure, you can run docker on your laptop, or you can run it in the cloud.
Docker has a Container HUB, it is a repository of containers which you can download and use. You can even share containers with your applications.
Docker is quite well documented.
|bridge||It is the default network all containers connect to if you don’t specify the network yourself|
|none||connects to a container-specific network stack that lacks a network interface|
|host||connects to the host’s network stack - there will be no isolation between the host machine and the container, as far as the network is concerned|
Ans: Docker container is the runtime instance of a docker image.
Ans: Well, I think, docker is extremely useful in development environments. Especially for testing purposes. You can deploy and re-deploy apps in a blink of an eye.
Also, I believe there are use cases where you can use Docker in production. Imagine you have some Node.js application providing some services on the web.
Ans: Eventually, if docker is good or not should be decided on an application basis. For some apps, it can be sufficient, for others not.
Another benefit of Docker, from my perspective, is the speed of deployment. Let's imagine a scenario:
ACME inc. needs to virtualize application GOOD APP for testing purposes.
The application should run in an isolated environment.
The application should be available to be redeployed at any moment in a very fast manner.
In the vSphere world what we would usually do, is:
Benefits: No need of deploying full OS for each instance of the application. Deploying a container takes seconds.
Ans: I came across Docker not long after Solomon open-sourced it. I knew a bit about LXC and containers (a past life includes working on Solaris Zones and LPAR on IBM hardware too), and so I decided to try it out. I was blown away by how easy it was to use. My prior interactions with containers had left me with the feeling they were complex creatures that needed a lot of tuning and nurturing. Docker just worked out of the box. Once I saw that and then saw the CI/CD-centric workflow that Docker was building on top I was sold.
Ans: I think it's the lightweight nature of Docker combined with the workflow. It's fast, easy to use and a developer-centric DevOps-ish tool. Its mission is basically: make it easy to package and ship code. Developers want tools that abstract away a lot of the details of that process. They just want to see their code working. That leads to all sorts of conflicts with Sys Admins when code is shipped around and turns out not to work somewhere other than the developer's environment. Docker turns to work around that by making your code as portable as possible and making that portability user-friendly and simple.
Ans: It's the build pipeline. I mean I see a lot of folks doing hyper-scaling with containers, indeed you can get a lot of containers on a host, and they are blindingly fast. But that doesn't excite me as much as people using it to automate their dev-test-build pipeline.
Ans: Docker is operating system-level virtualization. Unlike hypervisor virtualization, where virtual machines run on physical hardware via an inter-mediation layer ("the hypervisor"), containers instead run userspace on top of an operating system's kernel. That makes them very lightweight and very fast.
Ans: I think open-source software is closely tied to cloud computing. Both in terms of the software running in the cloud and the development models that have enabled the cloud. Open-source software is cheap, it's usually low friction both from an efficiency and a licensing perspective.
Ans: I think there are a lot of workloads that Docker is ideal for, as I mentioned earlier both in the hyper-scale world of many containers and in the dev-test-build use case. I fully expect a lot of companies and vendors to embrace Docker as an alternative form of virtualization on both bare-metal and in the cloud.
As for cloud technology's trajectory. I think we've seen a significant change in the last couple of years. I think they'll be a bunch more before we're done. The question of OpenStack and whether it will succeed as an IAAS alternative or DIY cloud solution. I think we've only touched on the potential for PAAS and there's a lot of room for growth and development in that space. It'll also be interesting to see how the capabilities of PAAS products develop and whether they grow to embrace or connect with consumer cloud-based products.
Ans: It's very much a crash course introduction to Docker. It's aimed at Developers and SysAdmins who want to get started with Docker in a very hands-on way. We'll teach the basics of how to use Docker and how to integrate it into your daily workflow.
Ans: That's mostly a joke related to my partner. Like a lot of geeks, I'm often on my computer, tapping away at a problem or writing something. My partner jokes that I have two jobs: my "real" job and my open-source job. Thankfully over the last few years, at places like Puppet Labs and Docker, I've been able to combine my passion with my paycheck.
Ans: It's OSCON time again, and this year the tech sector is abuzz with talk of cloud infrastructure. One of the more interesting startups is Docker, an ultra-lightweight containerization app that's brimming with potential
I caught up with the VP of Services for Docker, James Turnbull, who'll be running a Docker crash course at the con. Besides finding out what Docker is anyway, we discussed the cloud, open-source contributing, and getting a real job.
Ans: Compose stop attempts to stop a container by sending a
SIGTERM. It then waits for a default timeout of 10 seconds. After the timeout, a
SIGKILL is sent to the container to kill it forcefully. If you are waiting for this timeout, it means that your containers aren’t shutting down when they receive the
There has already been a lot written about this problem of processes handling signals in containers.
To fix this issue, try the following:
Make sure you’re using the JSON form of
ENTRYPOINT in your Dockerfile.
For example use
["program", "arg1", "arg2"] not
"program arg1 arg2". Using the string form causes Docker to run your process using
bash which doesn’t handle signals properly. Compose always uses the JSON form, so don’t worry if you override the command or entry point in your Compose file.
-If you are able, modify the application that you’re running to add an explicit signal handler for
stop_signal to a signal which the application knows how to handle:
-web: build: . stop_signal: SIGINT
-If you can’t modify the application, wrap the application in a lightweight init system (like s6) or a signal proxy (like dumb-init or tini). Either of these wrappers takes care of handling
Ans: Compose uses the project name to create unique identifiers for all of a project’s containers and other resources. To run multiple copies of a project, set a custom project name using the
-p command-line option or the
COMPOSE_PROJECT_NAME environment variable.
Ans: Typically, you want
docker-compose up. Use
up to start or restart all the services defined in a
docker-compose.yml. In the default “attached” mode, you’ll see all the logs from all the containers. In “detached” mode (
-d), Compose exits after starting the containers, but the containers continue to run in the background.
docker-compose run the command is for running “one-off” or “ad-hoc” tasks. It requires the service name you want to run and only starts containers for services that the running service depends on. Use
run to run tests or perform an administrative task such as removing or adding data to a data volume container. The
run command acts like
docker run -ti in that, it opens an interactive terminal to the container and returns an exit status matching the exit status of the process in the container.
docker-compose start the command is useful only to restart containers that were previously created but were stopped. It never creates new containers.
Ans: The base image depends on the tool or script. You can pursue available images by searching Docker Hub for the domain (e.g., “biology”, “science”) and read the documentation for specific images.
Most frequently used CyVerse base images:
Ans: Yes. Yaml is a superset of json so any JSON file should be valid Yaml. To use a JSON file with Compose, specify the filename to use, for example:
docker-compose -f docker-compose.json up
Ans: You can add your code to the image using
ADD directive in a
Dockerfile. This is useful if you need to relocate your code along with the Docker image, for example when you’re sending the code to another environment (production, CI, etc).
You should use a
volume if you want to make changes to your code and see them reflected immediately, for example when you’re developing code and your server supports hot code reloading or live-reload.
There may be cases where you’ll want to use both. You can have the image include the code using a, and use a
volume in your Compose file to include the code from the host during development. The volume overrides the directory contents of the image.
Ans: There are many examples of Compose files on github.
Ans: Last year, we encountered an organization that developed a modular application while allowing developers to “use what they want” to build individual components. It was a nice concept but a total organizational nightmare — chasing the ideal of modular design without considering the impact of this complexity on their operations.
The organization was then interested in Docker to help facilitate deployments, but we strongly recommended that this organization not use Docker before addressing the root issues. Making it easier to deploy these disparate applications wouldn’t be an antidote to the difficulties of maintaining several different development stacks for long-term maintenance of these apps.
Ans: Chances are that your application already has a framework for shipping logs and backing up data to the right places at the right times. To implement Docker, you not only need to replicate the logging behavior you expect in your virtual machine environment, but you also need to prepare your compliance or governance team for these changes. New tools are entering the Docker space all the time, but many do not match the stability and maturity of existing solutions. Partial updates, rollbacks, and other common deployment tasks may need to be re-engineered to accommodate a containerized deployment.
If it’s not broken, don’t fix it. If you’ve already invested the engineering time required to build a continuous integration/continuous delivery (CI/CD) pipeline, containerizing legacy apps may not be worth the time investment.
Ans: At AWS re: Invent last month, Amazon chief technology officer Werner Vogels spent a significant portion of his keynote on AWS Lambda, an automation tool that deploys infrastructure based on your code. While Vogels did mention AWS’s container service, his focus on Lambda implies that he believes dealing with zero infrastructure is preferable to configuring and deploying containers for most developers.
Containers are rapidly gaining popularity in the enterprise, and are sure to be an essential part of many professional CI/CD pipelines. But as technology experts and CTOs, it is our responsibility to challenge new methodologies and services and properly weigh the risks of early adoption. I believe Docker can be extremely effective for organizations that understand the consequences of containerization — but only if you ask the right questions.
Ans: Docker uses a cache to speed up builds significantly. Every command in Dockerfile is building in another docker container, and its results are stored in a separate layer. Layers are built on top of each other.
Docker scans Dockerfile and tries to execute each step one after another, before executing it probes if this layer is already in cache. When a cache is hit, the building step is skipped, and from the user, the perspective is almost instant.
When you build your Dockerfile in a way that the most changing things such as application source code are on the bottom, you will experience instant builds.
Another way of amazingly fast building docker images is by using a good base image - which you specify in
FROM command, you can then only make necessary changes, not rebuild everything from scratch. This way, the build will be quicker. It's especially beneficial if you have a host without the cache like a Continuous Integration server.
Summing up, building Docker images with Dockerfile is faster than provisioning with Ansible, because of using docker cache and good base images. Moreover, you eliminate provisioning, by using ready to use configured images such as stgresus.
$ docker run --name some-postgres -d postgres No installing postgres at all - it's ready to run.
Ans: It depends on your use case. You probably should split different components into separate containers. It will give you more flexibility.
Docker is very lightweight and running containers is cheap, especially if you store them in RAM - it's possible to spawn a new container for every Http callback, however, it's not very practical.
At work, I develop using a set of five different types of containers linked together.
In production some of them are replaced by real machines or even clusters of the machine - however, settings on the application-level don't change.
It's possible because everything is communicating over the network. When you specify links in docker
run command - docker bridges containers and injects environment variables with information about IPs and ports of linked
children into the
This way, in my app settings file, I can read those values from the environment. In python it would be:
import os VARIABLE = os.environ.get('VARIABLE')
There is a tool that greatly simplifies working with docker containers, linking included. It's called the fig, and you can read more about it here.
Ans: It depends on how your production environment looks like.
An example deploy process may look like this:
docker build .in the code directory.
docker push myorg/myimage.
You can consider using amazon elastic beanstalk with docker or dokku.
Elastic beanstalk is a powerful beast and will do most of the deployment for you and provide features such as auto-scaling, rolling updates, zero deployment deployments, and more.
Dokku is a very simple platform as a service similar to heroku.
Ans: Docker containers are easy to deploy in the cloud. It is capable of getting more applications running on the same hardware when compared with other technologies like Kubernete, Amazon Elastic Contain, etc. Thus making learners who take Kubernetes Training Hyderabad and developers create, ready-to-run containerized applications and make them manage, deploy and share easily.
Ans: Some among the most commonly used Docker Commands are as follows:
|Dockerd||Launch the Docker Daemon|
|Info||Displays information System-Wide|
|Version||Displays the Docker Version information|
|Build||Builds images for Docker files|
|Inspect||Returns low-level information on an image or container|
|History||Shows Image History|
|Commit||Creates new images from Container changes|
|Attach||Attaches to a running container|
|Load||Load an image from STDIN or tar archive|
|Create||Create a new container|
|Diff||Inspect changes on a container’s file system|
|Kill||Kill a running container|