Executor Migration from Docker to Machine
This document contains some general guidelines and considerations to make when moving from the Docker executor to machine, or vice versa.
- Pre-installed software
- Running docker containers on machine
- Why use docker executors at all?
- Further reading
Occasionally, the Docker executor isn’t quite the right fit for your builds. This can include a lack of memory or requiring more dedicated CPU power. Moving to a dedicated virtual machine can help alleviate some of these issues, but changing out an executor is not as easy as replacing a few lines of configuration. There are some other considerations to make, such as the tools and libraries required to be installed for your application and tests.
By default, the machine executor images come installed with useful utilities, but application specific requirements will need to be installed. If a dependency is not installed within Ubuntu 16.04 by default, or is not found on this list, it will need to be manually installed (note the most up to date list can be found here):
- python 2.7.12*
- python 3.5.2
- nodejs 6.1.0*
- golang 1.7.3
- ruby 2.3.1*
* global default
Additional packages can be installed with
sudo apt-get install <package>. If the package in question is not
sudo apt-get update may be required before installing it.
Running docker containers on machine
Machine executors come installed with Docker, which can be used to run your application within a container rather than installing additional dependencies. Note, it is recommended this is done with a customer Docker image rather than a CircleCI convenience image, which are built under the assumption they will be used with the Docker executor and may be tricky to work around. Since each machine executor enviornment is a dedicated virtual machine, commands to run background containers can be used is normal.
Note: if you have Docker Layer Caching (DLC) enabled for your account, machine executors can utilize this to cache your image layers for subsequent runs.
Why use docker executors at all?
While machine executors do offer twice the memory and a more isolated enviornment, there is some additional overhead regarding spin up time, and, depending on the approach taken for running the application, more time is taken to install the required dependencies or pull your Docker image. The Docker executor will also cache as many layers as possible from your image during spin-up, as opposed to the machine executor, where DLC will need to be enabled.
All executors have their pros and cons, which have been laid out here to help decide which is right for your pipelines.
We have more details on each specific executor here, which includes specific memory and vCPU allocation details, as well as how to implement each one in your own configuration.
Help make this document better
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