pages:

-title:Introduction to Codefresh pipelines

url:"/introduction-to-codefresh-pipelines"

-title:Creatinga CI pipeline

-title:Creatingpipelines

url:"/pipelines"

-title:Steps in pipelines

url:"/steps"

url:"/stages"

-title:Caching for pipelines

url:"/pipeline-caching"

-title:DebuggingCIpipelines

-title:Debugging pipelines

url:"/debugging-pipelines"

-title:MonitoringCIpipelines

-title:Monitoring pipelines

url:"/monitoring-pipelines"

-title:Advanced workflows

-title:Advanced workflows for pipelines

url:"/advanced-workflows"

-title:Deployment environments

url:"/deployment-environments"

[C example]({{site.baseurl}}/docs/example-catalog/ci-examples/c-make/)

[Codefresh YAML for pipeline definitions]({{site.baseurl}}/docs/pipelines/what-is-the-codefresh-yaml/)

[Steps in pipelines]({{site.baseurl}}/docs/codefresh-yaml/steps/)

[Steps in pipelines]({{site.baseurl}}/docs/pipelinessteps/)

[Creating pipelines]({{site.baseurl}}/docs/pipelines/pipelines/)

[How Codefresh pipelines work]({{site.baseurl}}/docs/pipelines/introduction-to-codefresh-pipelines/)

title:"What is Codefresh"

title:"What is Codefresh?"

description:"Understand the features and benefits of Codefresh"

group:getting-started

toc:true

TBD

What is Codefresh?

The short answer: Complete CI/CD and GitOps-powered Argo CD in the same product.

Now for the long answer.

Codefresh is a_complete CI/CD solution_, not just CI.

We cover the full software lifecycle. View a release in the Kubernetes dashboard, click on it and go to the Docker image, click on the Docker image and go to the build that created it, all from a single interface.

Codefresh is turbo-charged CI/CD!

We work with all major Git platforms and cloud providers. There is no lock-in with any particular vendor. Unlike other CI/CD platforms which can be tightly coupled to a single Git provider, or a specific vendor or set of tools, Codefresh supports a fully programmtic implementation.

Everything in the pipeline is defined as code and applied with the command line. Storing the definitions in a code repo ensures consistenct. Upscaling or expanind Doing so means we can create all of our pipelines in a consistent way and store those definitions in a code repository. Taking this one step step deeper, we could then create a bootstrap pipeline in Codefresh that generates pipelines when new definitions are added to this repo. See our previous post Programmatic Creation of Codefresh Pipelines (part 2) for more on this.

Has pipelines with Has several unique features such as a distributed Docker layer cache, an auto-mounted shared volume, a private Docker registry and a private Helm repository.

* Built-in Kubernetes and Helm dashboards

* Helm charts browser, and Helm environment board

How does Codefresh do it?

Everything for CI/CD in Codefresh starts and ends with pipelines. In Codefresh, a pipeline can do pretty much anyhting, only CI, only CD, both CI and CD.

Completely programmatic approach

Pipelines are create pipelines and define the pipeline’s steps, triggers, and variables.

The Docker registry integrations and all cluster integrations are automatically available to all pipelines. You don’t need docker login commands or kubectl commands to set up a Kube context inside your pipeline.

To see how pipeline work, start with the[Introduction to Codefresh pipelines]({{site.baseurl}}/docs/configure-ci-cd-pipeline/introduction-to-codefresh-pipelines/), or jump to[pipeline creation]({{site.baseurl}}/docs/configure-ci-cd-pipeline/pipelines/).

Dashboards are key to providing information at the right time.

perational Dashboards ~ Exposing the most commonly needed application and environmental information to developers so that they can troubleshoot without needing assistance from the DevOps teams; even in production

Analytical Dashboards ~ Developers often need to reach out to the DevOps team to get statistics and metrics around builds and deployments. Codefresh automatically generates DORA metrics as well as many other key indicators of build and deployment efficiency, which can be easily viewed in seconds by product owners and management alike.

For a seamless CI/CD experience, Codefresh has native integrations with alomst every major provider.

Easily connect Git providers, registry providers, storage providers, secret stores, and notification channels

Docker registry and all cluster integrations are automatically available to all pipelines. You don’t need Docker login commands or`kubectl` commands to set up a Kube context inside your pipeline.

Go to Pipeline Integrations in the UI, and see how to

Here are several links we think wouuld be useful t

Quick starts

To get up and running, follow our quick starts. The quick start modules are a series of flows that guide you from setting up your first account in Codefresh, to creating a basic pipeline, and deploying to Kubernetes.

See XREF TBD

Example catalog

For those who are familiar with CI/CD, we have an extensive collection of examples, covering several CI and CD scenarios:

[CI examples]({{site.baseurl}}/docs/example-catalog/ci-examples/)

[CD examples]({{site.baseurl}}/docs/example-catalog/cd-examples/)

Guides

And finally, if you want more meat, dive in to our detailed guides.

XREF TBD

type: "git-clone"

repo: "anais-codefresh/react-article-display"

# CF_BRANCH value is auto set when pipeline is triggered

# Learn more at codefresh.io/docs/docs/codefresh-yaml/variables/

# Learn more at codefresh.io/docs/docs/pipelinesvariables/

revision: "${{CF_BRANCH}}"

git: "github"

stage: "clone"

With the parallel execution mode, you can define complex pipelines with fan-in/out configurations capable of matching even the most complicated workflows within an organization.

Before going any further make sure that you are familiar with the[basics of Codefresh pipelines]({{site.baseurl}}/docs/configure-ci-cd-pipeline/introduction-to-codefresh-pipelines/).

All types of steps can by placed inside a parallel phase. Another common use case would be the parallel execution of[freestyle steps]({{site.baseurl}}/docs/codefresh-yaml/steps/freestyle/) for unit/integration tests.

All types of steps can by placed inside a parallel phase. Another common use case would be the parallel execution of[freestyle steps]({{site.baseurl}}/docs/pipelines/steps/freestyle/) for unit/integration tests.

Let's say that you have a Docker image with a Python back-end and a JavaScript front-end. You could run both types of tests in parallel with the following yaml syntax:

* Pending

* Running

Finished is a shorthand for`success` or`failure` or`skipped`. It is only valid when used in[step dependencies]({{site.baseurl}}/docs/codefresh-yaml/advanced-workflows/#single-step-dependencies), and cannot be used in custom conditions.

Finished is a shorthand for`success` or`failure` or`skipped`. It is only valid when used in[step dependencies]({{site.baseurl}}/docs/pipelines/advanced-workflows/#single-step-dependencies), and cannot be used in custom conditions.

You can mix and match completion states from any other step in your pipeline. Here are some examples:

{% endraw %}

{% endhighlight %}

You can change this behavior by using the existing[fail_fast property]({{site.baseurl}}/docs/codefresh-yaml/what-is-the-codefresh-yaml/#execution-flow) inside an`on_elected` hook.

You can change this behavior by using the existing[fail_fast property]({{site.baseurl}}/docs/pipelines/what-is-the-codefresh-yaml/#execution-flow) inside an`on_elected` hook.

{% highlight yaml %}

* Compile and package code

* Build Docker images

* Push Docker images to any[Docker Registry]({{site.baseurl}}/docs/docker-registries/external-docker-registries/)

* Push Docker images to any[Docker Registry]({{site.baseurl}}/docs/integrations/docker-registries/)

* Deploy applications/artifacts to VMs, Kubernetes clusters, FTP sites, S3 buckets etc.

* Run[unit tests]({{site.baseurl}}/docs/testing/unit-tests/),[integration tests]({{site.baseurl}}/docs/testing/integration-tests/), acceptance tests etc.

* Any custom action that you define

%}

1. The first step runs under the context of a Node image that prepares the application and runs[unit tests]({{site.baseurl}}/docs/testing/unit-tests/).

1. The first step runs under the context of a Node image that prepares the application and runs unit tests.

1. The second step uses an image with s3 command line tools and uploads the test results to a bucket that holds test reports.

1. The helm step creates a Helm chart and pushes it to a Helm repository.

1. Finally,`/codefresh/volume` is an internal folder name, and you should use`{% raw %}${{CF_VOLUME_PATH}}{% endraw %}` in your codefresh.yml file

if you really want to reference this folder. You can also reference your project folder as`{% raw %}${{CF_VOLUME_PATH}}/${{CF_REPO_NAME}}{% endraw %}` if you need it.

Seethe[System Provided Variables]({{site.baseurl}}/docs/pipelines/variables/#system-provided-variables) section for more information.

See[system variables]({{site.baseurl}}/docs/pipelines/variables/#system-variables) for more information.

For the plugins in the[Step Marketplace](https://codefresh.io/steps/) we already give an example of the YAML part that must be included in your pipeline:

For the plugins in the[Step Marketplace](https://codefresh.io/steps/){:target="\_blank"} we already give an example of the YAML part that must be included in your pipeline:

{% include

image.html

Now we reach one of the most powerful features of Codefresh pipelines. We have already seen that[freestyle pipeline steps]({{site.baseurl}}/docs/pipelines/steps/freestyle/) are just a series of commands that run inside the context of a Docker container. In most cases the images used

for the freestyle steps are known in advance and come from public (e.g. Dockerhub) or[private Docker registries]({{site.baseurl}}/docs/docker-registries/external-docker-registries/).

Now we reach one of the most powerful features of Codefresh pipelines. We have already seen that[freestyle steps]({{site.baseurl}}/docs/pipelines/steps/freestyle/) in pipelines are just a series of commands that run inside the context of a Docker container. In most cases the images used for the freestyle steps are known in advance and come from public (e.g. Dockerhub) or[private Docker registries]({{site.baseurl}}/docs/integrations/docker-registries/).

Codefresh is one the few CI/CD solutions that not only offers easy Docker registry integration

accessible to all pipelines

Codefresh employs several caching mechanisms for both Dockerized and non-dockerized applications. The shared volume is also cached behind the scenes automatically. See our[caching guide]({{site.baseurl}}/docs/configure-ci-cd-pipeline/pipeline-caching/) for more details.

Codefresh employs several caching mechanisms for both Dockerized and non-dockerized applications. The shared volume is also cached behind the scenes automatically. See our[caching guide]({{site.baseurl}}/docs/pipelines/pipeline-caching/) for more details.