To increase the speed of your software development through faster feedback, shorter reruns, and more efficient use of resources, configure Workflows. For example, if only one job in your Workflow fails, you will know it is failing in real-time and you can rerun just the failed job instead of wasting time and resources waiting for the entire build to fail or rerunning the entire set of jobs. This document describes the Workflows feature and provides example configurations in the following sections:
- Rerunning a Workflow from a Failed Job
- Workflows Configuration Examples
- Holding a Workflow for a Manual Approval
- Using Contexts and Filtering in Your Workflows
- Using Workspaces to Share Data Among Jobs
- See Also
A workflow is a set of rules for defining a collection of jobs and their run order that shortens the feedback loop. The Workflows feature supports very complex job orchestration using a simple set of new configuration keys with a powerful user interface to help you resolve failures sooner, for example:
- Run and troubleshoot jobs independently with fast status feedback as each job runs
- Fan-out to run multiple jobs in parallel for efficient testing of versions
- Fan-in for deployment to separate platforms with increased speed
Rerunning a Workflow from a Failed Job
When you use workflows to orchestrate parts of your build, you increase your ability to respond to failures rapidly. Click the Workflows icon in the app and select a workflow to see the status of each job as shown in the next screenshot. Click the Rerun button and select From failed to restart only the failed job and continue the workflow. Only jobs after the failure will run, saving time and resources.
Workflows Configuration Examples
To run a set of parallel jobs, add a new
workflows: section to the end of your existing
.circleci/config.yml file with the version and a unique name for the workflow. The following sample
.circleci/config.yml file shows the default workflow orchestration with two parallel jobs. It is defined by using the
workflows: key named
build_and_test and by nesting the
jobs: key with a list of job names. The jobs have no dependencies defined, therefore they will run in parallel.
version: 2 jobs: build: docker: - image: circleci/<language>:<version TAG> steps: - checkout - run: <command> test: docker: - image: circleci/<language>:<version TAG> steps: - checkout - run: <command> workflows: version: 2 build_and_test: jobs: - build - test
See the Sample Parallel Workflow config for a full example.
Sequential Job Execution Example
The following example shows a workflow with four sequential jobs. The jobs run according to configured requirements, each job waiting to start until the required job finishes successfully as illustrated in the diagram.
config.yml snippet is an example of a workflow configured for sequential job execution:
workflows: version: 2 build-test-and-deploy: jobs: - build - test1: requires: - build - test2: requires: - test1 - deploy: requires: - test2
The dependencies are defined by setting the
requires: key as shown. The
deploy: job will not run until the
test2 jobs complete successfully. A job must wait until all upstream jobs in the dependency graph have run. So, the
deploy job waits for the
test2 job, the
test2 job waits for the
test1 job and the
test1 job waits for the
See the Sample Sequential Workflow config for a full example.
Fan-Out/Fan-In Workflow Example
The illustrated example workflow runs a common build Job, then fans-out to run a set of acceptance test Jobs in parallel, and finally fans-in to run a common deploy Job.
config.yml snippet is an example of a workflow configured for sequential job execution:
workflows: version: 2 build_accept_deploy: jobs: - build - acceptance_test_1: requires: - build - acceptance_test_2: requires: - build - acceptance_test_3: requires: - build - acceptance_test_4: requires: - build - deploy: requires: - acceptance_test_1 - acceptance_test_2 - acceptance_test_3 - acceptance_test_4
In this example, as soon as the
build job finishes successfully, all four acceptance test jobs start. The
deploy job must wait for all four acceptance test jobs to complete successfully before it starts.
See the Sample Fan-in/Fan-out Workflow config for a full example.
Holding a Workflow for a Manual Approval
Workflows may be configured to wait for manual approval of a job before continuing by using the
type: approval key. The
type: approval key is a special job and type that is only added under in your
workflow key. This enables you to configure a job with
type:approval in the workflow before a set of parallel jobs that must all wait for manual approval. Jobs run in the order defined until the workflow processes a job with the
type: approval key followed by a job on which it depends as in the following
workflows: version: 2 build-test-and-approval-deploy: jobs: - build - test1: requires: - build - test2: requires: - test1 - hold: type: approval requires: - test2 - deploy: requires: - hold
In this example, the
deploy: job will not run until you click the Approve button on the
hold job in the Workflows page of the CircleCI app. Notice that the
hold job must have a unique name that is not used by any other job. The workflow will wait with the status of On Hold until you click the button. After you approve the job with
type: approval, the jobs which require the approval job will start. In this example, the purpose is to wait for approval to begin deployment. To configure this behavior, the
hold job must be
type: approval and the
deploy job must require
Using Contexts and Filtering in Your Workflows
The following sections provide example for using Contexts and filters to manage job execution.
Using Job Contexts to Share Environment Variables
The following example shows a workflow with four sequential jobs that use shared environment variables.
config.yml snippet is an example of a sequntial job workflow configured to use the resources defined in the
workflows: version: 2 build-test-and-deploy: jobs: - build - test1: requires: - build context: org-global - test2: requires: - test1 context: org-global - deploy: requires: - test2
The environment variables are defined by setting the
context key as shown to the default name
test2 jobs in this workflows example will use the same shared environment variables when initiated by a user who is part of the organization. By default, all projects in an organization have access to contexts set for that organization.
See the Contexts document for detailed instructions on this setting in the application.
Branch-Level Job Execution
The following example shows a workflow configured with jobs on three branches: Dev, Stage, and Pre-Prod. Workflows will ignore
branches keys nested under
jobs configuration, so if you use job-level branching and later add workflows, you must remove the branching at the job level and instead declare it in the workflows section of your
config.yml, as follows:
config.yml snippet is an example of a workflow configured for branch-level job execution:
workflows: version: 2 dev_stage_pre-prod: jobs: - test_dev: filters: branches: only: - dev - /user-.*/ - test_stage: filters: branches: only: stage - test_pre-prod: filters: branches: only: /pre-prod(?:-.+)?$/
In the example,
filters is set with the
branches key and the
only key with the branch name. Any branches that match the value of
only will run the job. Branches matching the value of
ignore will not run the job. See the Sample Sequential Workflow config with Branching for a full example.
Git Tag Job Execution
CircleCI treats tag and branch filters differently when deciding whether a job should run.
- For a branch push unaffected by any filters, CircleCI runs the job.
- For a tag push unaffected by any filters, CircleCI skips the job.
Item two above means that a job must have a
tags section to run as a part of a tag push and all its transitively dependent jobs must also have a
build job example will run for all branches, and all tags, except those starting with
workflows: version: 2 build-workflow: jobs: - build: filters: tags: ignore: /^testing-.*/
The following example runs
buildjob for all branches, and all tags.
deployjob for no branches, and all tags starting with
workflows: version: 2 build-n-deploy: jobs: - build: filters: tags: only: /.*/ - deploy: requires: - test filters: tags: only: /^config-test.*/ branches: ignore: /.*/
The following example runs
testjobs for all branches and only
workflows: version: 2 build-n-deploy: jobs: - build: filters: tags: only: /^config-test.*/ - test: requires: - build filters: tags: only: /^config-test.*/ - deploy: requires: - test filters: tags: only: /^config-test.*/ branches: ignore: /.*/
Using Workspaces to Share Data Among Jobs
Each workflow has an associated workspace which can be used to transfer files to downstream jobs as the workflow progresses. The workspace is an additive-only store of data. Jobs can persist data to the workspace. This configuration archives the data and creates a new layer in an off-container store. Downstream jobs can attach the workspace to their container filesystem. Attaching the workspace downloads and unpacks each layer based on the ordering of the upstream jobs in the workflow graph.
Use workspaces to pass along data that is unique to this run and which is needed for downstream jobs. Workflows that include jobs running on multiple branches may require data to be shared using workspaces. Workspaces are also useful for projects in which compiled data are used by test containers.
For example, Scala projects typically require lots of CPU for compilation in the build job. In contrast, the Scala test jobs are not CPU-intensive and may be parallelised across containers well. Using a larger container for the build job and saving the compiled data into the workspace enables the test containers to use the compiled Scala from the build job.
A second example is a project with a
build job that builds a jar and saves it to a workspace. The
build job fans-out into the
code-coverage to run those tests in parallel using the jar.
To persist data from a job and make it available to other jobs, configure the job to use the
persist_to_workspace key. Files and directories named in the
paths: property of
persist_to_workspace will be uploaded to the workflow’s temporary workspace and made available for subsequent jobs (and re-runs of the workflow) to use.
Configure a job to get saved data by configuring the
attach_workspace key. The following
config.yml file defines two jobs where the
downstream job uses the artifact of the
flow job. The workflow configuration is sequential, so that
flow to finish before it can start.
defaults: &defaults working_directory: /tmp docker: - image: buildpack-deps:jessie version: 2 jobs: flow: <<: *defaults steps: - run: mkdir -p workspace - run: echo "Hello, world!" > workspace/echo-output - persist_to_workspace: # Must be an absolute path, or relative path from working_directory root: workspace # Must be relative path from root paths: - echo-output downstream: <<: *defaults steps: - attach_workspace: # Must be absolute path or relative path from working_directory at: /tmp/workspace - run: | if [[ `cat /tmp/workspace/echo-output` == "Hello, world!" ]]; then echo "It worked!"; else echo "Nope!"; exit 1 fi workflows: version: 2 btd: jobs: - flow - downstream: requires: - flow
defaults: key in this example is arbitrary. It is possible to name a new key and define it with an arbitrary
&name to create a reusable set of configuration keys.
For procedural instructions on how to add Workflows your configuration as you are migrating from a 1.0
circle.ymlfile to a 2.0
.circleci/config.ymlfile, see the Steps to Configure Workflows section of the Migrating from 1.0 to 2.0 document.
For details about the
workflows:key requirements, see the Workflows section of the Writing Jobs with Steps document.
For frequently asked questions and answers about Workflows, see the Workflows section of the Migration FAQ.
For demonstration apps configured with Workflows, see the CircleCI Demo Workflows on GitHub.
For troubleshooting a workflow with Waiting for Status in GitHub, see Workflows Waiting for Status in GitHub.