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Docker-based Pipelines
with Codefresh
KOSTIS KAPELONIS
About Kostis Kapelonis
● Software engineer
● Technical writer
● Manning author
About Kostis Kapelonis
Agenda
1. Docker usage in Continuous Integration
2. Dockerizing build tools as pipeline steps
3. Upgrading build tools to new versions
4. Mixing multiple versions of the same tool in
the same pipeline
5. Creating new pipeline steps on the fly
● Demos for everything using Codefresh
https://github.com/containers101/docker-based-pipelines-webinar/
Theory: Docker-based Pipelines
“Docker-based” means 2 different things:
Using Docker as a
deployment package
(this is what most people think)
Using Docker for build Tooling
(this is not what most people think)
90% of cases: “We have migrated
to Docker in production”
The Basic Software Lifecycle
Before Docker – The Dark Ages
Docker-based Deployments - Better
Adding Docker-based Build Pipelines
Today’s Webinar
CI CD
Resources for Docker as Deployment artifact
Using Docker in Continuous Integration
● EVERY build tool is placed in a Docker
container
● The build node has only Docker installed
and nothing else
● A pipeline is a series of commands that run
inside a Docker context
● After each build the node reverts back to its
original state
Container per build step
● Codefresh requires ALL tools to be dockerized
● You can use any public or private Docker
image as tooling
● Each build step has a Docker image as context
● Pipelines are described in declarative YAML
About Codefresh
● Docker based CI/CD solution
● Each build step is a Docker image
● Native support for Docker, Helm,
Kubernetes deployments
● Includes built-in Docker registry and
Helm repository
● 20,000+ users
Demo 1:
Python/Node application
https://github.com/containers101/docker-based-pipelines-webinar/tree/master/01_simple_pipeline
Traditional VM based problems
Traditional CI Platform Questions:
● Do you support my favorite version of
Node/Java/Go/Ruby/Python?
● Do you support maven, yarn, gulp, sbt, gradle, rake?
● Can I run Ansible? Terraform? GCloud? AWS CLI?
● Can I run Kubectl? Helm? Draft?
Traditional CI/CD Platforms
Demo 2:
Adding Go and AWS CLI
https://github.com/containers101/docker-based-pipelines-webinar/tree/master/02_aws_cli
Does Codefresh Support…
● Node 10?
● Perl 6?
● Python2?
● Gradle?
● Vault?
● AWS cli?
● Sonar?
● Findbugs?
● Selenium?
● Snyk?
● Clair?
Does Codefresh Support…
● Node 10?
● Perl 6?
● Python2?
● Gradle?
● Vault?
● AWS cli?
● Sonar?
● Findbugs?
● Selenium?
● Snyk?
● Clair?
YES!
Because there is a Docker
image for it
Codefresh Pipelines are Future Proof
● You can use ANY existing Docker image from Dockerhub or
any other Registry
● Every time a new tool comes out, it can be used right away if
packaged in a Docker image
Tool Upgrades and Version
Clashes
Updating a Tool in a Traditional VM Pipeline
Traditional CI Solutions
Demo:
Updating Python to 3.7
https://github.com/containers101/docker-based-pipelines-webinar/tree/master/02_aws_cli
Using Tools from Different Versions
● Version clashes are a huge pain for both developers and operators
● Legacy projects need to still use old version
● Using different versions in the same pipeline is almost impossible
● Developers want to use latest version of tool, traditional CI/CD
platforms may not be able to keep up
Wasting Effort on “Version Managers”
Wasting Effort on “Version Managers”
● They allow developers to switch between different versions
● Tied to a specific technology/programming language
● Require they own installation/ maintenance
● Must be upgraded for new versions
The Problem with Python
● Different python versions are a
notorious problem
● Until recently you needed
dedicated support from your CI
platform
● What happens if I want to test
Python 2.5?
Replacing “version managers”
with Docker
● Works for any language/framework
● Already installed on the build node
● Its own version is independent from
the tools
● Can use any public and private image
Codefresh “Python Support”
● We support EVERY container
ever made
● We support EVERY container
that you can make in the future
Demo 3:
Multiple Python/Node versions
https://github.com/containers101/docker-based-pipelines-webinar/tree/master/03_multiple_versions
Data Sharing Between
Pipeline Steps
Data Sharing
● Steps need to communicate
● Output of one step is input for the
next
● Artifacts (node modules, ruby gems,
maven caches) need to persist
● Test reports/Coverage statistics
Caches and Artifacts (Traditional CI solutions)
● “Cache” directive
● Need to be setup explicitly
● Different for each build tool
● “Artifact” directive
● Developers defines exact path of
what needs to be archived
● Used for the result of the whole build
or as shared data between steps
All Steps Share a Volume in Codefresh
Project is on the Volume
● Project is checked out in the volume
● Volume is also persisted between builds
● Any build tools that use the project folder for
artifacts will gain automatic caching
● For other tools you just need to point their
cache to /codefresh/volume
● There is no need for special “artifact settings”.
Just place files in /codefresh/volume
Demo 4 – Node Modules
https://github.com/containers101/docker-based-pipelines-webinar/tree/master/04_volume
Dynamic Docker Images
Docker Tooling on Demand – A Unique Feature
Creating Docker Images On-demand
● Create a Docker image as a step
● Use image in a later step
● Maximum flexibility for build context
● Image contents are not known in
advance
● Codefresh is the only platform at the
moment that offers this capability
Creating Docker Images On-demand
● No need for multiple Docker
images
● “Create and forget” build steps
● Useful for integration tests
● Keep your Docker registry small
and tidy
Demo 5:
Dynamic Docker Images
https://github.com/containers101/docker-based-pipelines-webinar/tree/master/05_dynamic
Codefresh Plugins
Plugins in Traditional CI/CD Platforms
● Specific to the platform (vendor lock-in)
● Tied to a specific language (e.g. Groovy)
● Developer needs to learn proprietary API
● Testing and installing them is difficult
Codefresh Plugins = Docker Images
Codefresh Plugins
● Not tied to any programming language
● Require only Docker knowledge
● Easy to test, easy to search, easy to store
● Several plugins for Codefresh already available
Case study: bintray
● JFrog bintray integration
● There is no official docker image
● A Codefresh plugin with wrap the CLI
● Plugin will be used to query Bintray
Demo 6:
Codefresh Plugins
https://github.com/containers101/docker-based-pipelines-webinar/tree/master/06_plugin
Plugin Directory
https://codefresh.io/codefresh-plugins/
Summary
● Docker-based pipelines use Docker
images as build steps
● Upgrading tools is easy
● Using multiple versions of the same tool is
trivial
● Can dynamically create build steps
● Codefresh plugins are Docker images
Get 120 FREE builds/month!
Signup & schedule a 1:1 at:
Codefresh.io
Build Fast,
Deploy Faster
Q ?
Codefresh.io/events
U E

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