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.
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 reengineered 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.
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’ 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.
Docker uses cache to speed up builds significantly. Every command in Dockerfile is build in another docker container and it’s results are stored in separate layer. Layers are built on top of each other.
Docker scans Dockerfile and try to execute each steps one after another, before executing it probes if this layer is already in cache. When cache is hit, building step is skipped and from user 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 would experience instant builds.
You can learn more about caching in docker in this article.
Another way of amazingly fast building docker images is using good base image – which you specify in
FROM command, you can then only make necessary changes, not rebuild everything from scratch. This way, build will be quicker. It’s especially beneficial if you have a host without the cache like 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 can completely eliminate provisioning, by using ready to use configured images such stgresus.
$ docker run --name some-postgres -d postgres No installing postgres at all - it's ready to run.
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 new container for every http callback, however it’s not very practical.
At work I develop using set of five different types of containers linked together.
In production some of them are actually replaced by real machines or even clusters of machine – however settings on application level don’t change.Here you can read more about linking containers.
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 environment. In python it would be:
import os VARIABLE = os.environ.get('VARIABLE')
It depends how your production environment looks like.Example deploy process may look like this:
docker build .in the code directory.
docker push myorg/myimage.
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.
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 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.
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.
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 forcefully kill it. 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 problem, try the following:
ENTRYPOINTin your Dockerfile.
bashwhich doesn’t handle signals properly. Compose always uses the JSON form, so don’t worry if you override the command or entrypoint in your Compose file.
stop_signalto a signal which the application knows how to handle:
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.
To run an application in virtualized environment (e.g. vSphere), we first need to create a VM, install an OS inside and only then deploy the application.To run same application in docker all you need is to deploy that application in Docker. There is no need of additional OS layer. You just deploy the application with its dependent libraries, the rest (kernel, etc.) is provided by Docker engine.This table from a Docker official website shows it in a quite clear way.
Another benefit of Docker, from my perspective, is speed of deployment. Lets imagine a scenario:ACME inc. needs to virtualize application GOOD APP for testing purposes.