Advanced Workflows with Parallel steps
Learn how to create complex workflows in Codefresh with step dependencies
Codefresh is very flexible when it comes to pipeline complexity and depth. You can easily create:
- Sequential pipelines where step order is same as the listing order in yaml (simple)
- Sequential pipelines that have some parallel parts (intermediate)
- Parallel pipelines where step order is explicitly defined (advanced)
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.
Notice that in Codefresh parallel execution is unrelated with stages. Stages are only a way to visually organize your pipeline steps. The actual execution is independent from the visual layout in the logs view.
Before going any further make sure that you are familiar with the basics of Codefresh pipelines.
Codefresh offers two modes of execution
- Sequential mode (which is the default)
- Parallel mode
Sequential execution mode
The sequential mode is very easy to understand and visualize.
In Sequential mode the Codefresh execution engine starts from the first step defined at the top of the
codefresh.yml file and executes all steps one by one going down to the end of the file. A step is either executed or skipped according to its conditions. The condition for each step is only examined once.
Here we have two steps, one that creates a Docker image and a second one that runs unit tests inside it. The order of execution is the same order of the steps in the YAML file. This means that unit tests will always run after the Docker image creation.
Notice that the line
mode: sequential is shown only for illustration purposes. Sequential mode is the default, and therefore this line can be omitted.
Inserting parallel steps in a sequential pipeline
You don’t have to activate parallel execution mode for the whole pipeline if only a part of it needs to run in parallel. Codefresh allows you insert a parallel phase inside a sequential pipeline with the following syntax:
In this case tasks 2A and 2B will run in parallel.
The step name that defines the parallel phase (
my_parallel_tasks in the example above), is completely arbitrary.
The final order of execution will be:
- Task 1
- Task 2A and Task2B at the same time
- Task 3
This is the recommended way to start using parallelism in your Codefresh pipelines and it will be enough for most scenarios that require parallelism.
Notice that step names should be unique within the same pipeline. The parent and child steps should NOT share the same name.
Example: pushing multiple Docker images in parallel
Let’s see an example where a Docker image is created and then we push it to more than one registry. This is a perfect candidate for parallelization. Here is the
The order of execution is the following
- MyAppDockerImage (build step)
- jfrog_PushingTo_jfrog_BintrayRegistry, PushingToGoogleRegistry, PushingToDockerRegistry (push steps)
The pipeline view for this yaml file is the following.
As you can see we have also marked the steps with stages so that we get a visualization that matches the execution.
Example: running multiple test suites in parallel
All types of steps can by placed inside a parallel phase. Another common use case would be the parallel execution of freestyle steps for unit/integration tests.
Running different types of tests (unit/integration/load/acceptance) in parallel is a very common use case for parallelism inside an otherwise sequential pipeline.
Defining success criteria for a parallel step
By default, any failed step in a Codefresh pipeline will fail the whole pipeline. There are ways to change this behavior (the
fail_fast property is explained later in this page), but specifically for parallel steps you can define exactly when the whole step succeeds of fails.
You can define steps that will be used to decide if a parallel step succeeds with this syntax:
In the example above, if integration and/or acceptance tests fail, the whole pipeline will continue, because we have defined that only the results of unit test matter for the whole parallel step.
The reverse relationship (i.e. defining steps to be ignored) can be defined with the following syntax
In the example above we have explicitly defined that even if the integration or acceptance tests fail the whole pipeline will continue.
Shared Codefresh volume and race conditions
In any pipeline step, Codefresh automatically attaches a shared volume that is used to transfer artifacts between steps. The same volume is also shared between steps that run in parallel.
Here is an example where two parallel steps are writing two files. After they finish execution we list the contents of the project folder.
The results from the
MyListing step is the following:
This illustrates the side effects for both parallel steps that were executed on the same volume.
It is therefore your responsibility to make sure that steps that run in parallel place nice with each other. Currently Codefresh performs no conflict detection at all. If there are race conditions between your parallel steps (e.g. multiple steps writing at the same files) the final behavior is undefined. It is best to start with a fully sequential pipeline and use parallelism in a gradual manner if you are unsure about the side effects of your steps
Parallel pipeline mode
To activate advanced parallel mode for the whole pipeline you need to declare it explicitly at the root of the
version: '1.0' mode: parallel steps: [...]
Note that full parallel mode is a way to run pipelines that is incompatible with the parallel steps shown in the previous section (which used the
type: parallelattribute). The two modes cannot be mixed together. You must use one or the other in a single CI/CD pipeline but not both at the same time.
In full parallel mode, the order of steps inside the
codefresh.yml is not affecting the order of execution at all. The Codefresh pipeline engine instead:
- Evaluates all steps conditions at the same time
- Executes those that have their requirements met
- Starts over with the remaining steps
- Stops when there no more steps to evaluate
This means that in parallel mode the conditions of a step are evaluated multiple times as the Codefresh execution engine is trying to find which steps it should run next. This implication is very important when you try to understand the order of step execution.
Notice also that in parallel mode, if you don’t define any step conditions, Codefresh will try to run all steps at once, which is probably not what you want in most cases.
With parallel mode you are expected to define the order of steps in the yaml file, and the Codefresh engine will create a graph of execution that satisfies your instructions. This means that writing the
codefresh.yml file requires more effort on your part, but on the other hand allows you to define the step order in ways not possible with the sequential mode. You also need to define which steps should depend on the automatic cloning of the pipeline (which is special step named
In the next sections we describe how you can define the steps dependencies in a parallel pipeline.
Single Step dependencies
At the most basic level, you can define that a step depends on the execution of another step. This dependency is very flexible as Codefresh allows you run a second step once:
- The first step is finished with success
- The first step is finished with failure
- The first step was skipped
- The first completes (regardless of exit) status
The syntax for this is the following post-condition
If you want to run the second step only if the first one fails the syntax is :
If you want to run the second step only if the first one was skipped (because its own condition said so) :
Finally if you don’t care about the completion status the syntax is:
skipped/success is the default behavior so if omit the last two lines (i.e. the
on: part) the second step
will wait for the next step to either run successfully or be skipped.
Also notice that the name
main_cloneis reserved for the automatic clone that takes place in the beginning of pipelines that are linked to a git repository. You need to define which steps depend on it (probably the start of your graph) so that
git checkouthappens before the other steps.
As an example let’s assume that you have the following steps in a pipeline
- A build step that creates a docker image
- A freestyle step that runs unit tests inside the docker image
- A freestyle step that runs integrations tests After the unit tests, even if they fail
- A cleanup step that runs after unit tests if they fail
Here is the full pipeline. Notice the explicit dependency to the
main_clone step that checks out the code.
If you run the pipeline you will see that Codefresh automatically understands that
MyCleanupPhase can run in parallel right after the unit tests finish.
Also notice the
fail_fast: false line in the unit tests. By default if any steps fails in a pipeline the whole pipeline is marked as a failure. With the
fail_fast directive we can allow the pipeline to continue so that other steps that depend on the failed step can still run even.
Multiple Step dependencies
A pipeline step can also depend on multiple other steps. The syntax is:
In this case, the third step will run only when BOTH first and second are finished (and first is actually a success)
ALL is the default behavior so it can be omitted if this is what you need. The example above is example the same as below:
Codefresh also allows you to define ANY behavior in an explicit manner:
Here the third step will run when either the first one OR the second one have finished.
As an example let’s assume this time that we have:
- A build step that creates a docker image
- Unit tests that will run when the docker image is ready
- Integration tests that run either after unit tests or if the docker image is ready (contrived example)
- A cleanup step that runs when both kinds of tests are finished
Here is the full pipeline
In this case Codefresh will make sure that cleanup happens only when both unit and integration tests are finished.
Custom steps dependencies
For maximum flexibility you can define a custom conditional for a step.
For example run this step only if a PR is opened against the production branch:
Run this step only for the master branch and when the commit message does not include “skip ci”:
You can now add extra conditions regarding the completion state of specific steps. A global object called
steps contains all steps by name along with a
result property with the following possible completion states
- finished (regardless of status)
Finished is a shorthand for
You can mix and match completion states from any other step in your pipeline. Here are some examples:
You can also use conditions in the success criteria for a parallel step. Here is an example
Handling error conditions in a pipeline
It is important to understand the capabilities offered by Codefresh when it comes to error handling. You have several options in different levels of granularity to select what constitutes a failure and what not.
By default, any failed step in a pipeline will abort the whole pipeline and mark it as failure.
You can use the directive
- in a specific step to mark it as ignored if it fails
- at the root level of the pipeline if you want to apply it to all steps
Therefore if you want your pipeline to keep running to completion regardless of errors the following syntax is possible:
version: '1.0' fail_fast: false steps: [...]
You also have the capability to define special steps that will run when the whole pipeline has a special completion status. Codefresh offers a special object called
workflow that represents the whole pipeline and allows you to evaluate its status in a step.
For example, you can have a cleanup step that will run only if the workflow fails (regardless of the actual step that created the error) with the following syntax:
As an another example we have a special step that will send an email if the pipeline succeeds or if load-tests fail:
Notice that both examples assume that
fail_fast: false is at the root of the