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Unit Testing In Ci Cd Pipeline
Step Functions enables users to easily create highly available, serverless, and intuitive workflows. Step Functions natively integrates with a variety of services, including but not limited to Lambda, Batch, Fargate, and Amazon SageMaker. It provides the ability to add error handling, retry logic, and complex branching, all through an easy-to-use JSON-based language called Amazon States Language.
Ci Cd Test Automation
CodePipeline is a fully managed Continuous Delivery System that enables easy and highly configurable approaches to automating the release process. CodePipeline enables end users to reliably and repeatably build, test, and deploy their most mission-critical applications and infrastructure.
CodeCommit is a secure and fully managed source control repository service. It eliminates the need to support and scale infrastructure to support mission-critical and highly available code repository systems.
This blog post demonstrates how to create a CI/CD pipeline to comprehensively test a Step Function state machine from start to finish using CodeCommit, CodeBuild, CodePipeline, and Python.
To start building this CI/CD pipeline, you must meet several prerequisites:
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The following screenshot depicts what a CodePipeline project looks like, including a set of run phases to safely, reliably, and confidently deploy a Step Function state machine to Production.
In this example, the repository is named CalculationStateMachine because it contains the contents of the state machine definition, Python tests, and CodeBuild configuration.
Inside the CodeCommit repository, create a State Machine Definition file named sm_def.json to define the state machine using Amazon State Language.
This example creates a state machine that calls a set of Lambda functions to perform calculations on given input values. Please note that it also performs a check against a specific value and through the use of the Selection state it will either continue the process or exit it.
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The CI/CD pipeline uses a set of CodeBuild BuildSpec files linked together via CodePipeline. The following sections demonstrate what these BuildSpec files look like and how they can be used to link together and build a full CI/CD pipeline.
To determine if the State Machine Definition is valid, inject a stage into your CodePipeline configuration to evaluate it. Through the use of a Ruby Gem called statelint, you can verify the validity of your state machine definition as follows:
If your configuration is valid, you should not see any output messages. If the configuration is invalid, you will receive a message informing you that the definition is invalid and the process ends.
To test Lambda function code, you need to evaluate whether it passes a series of tests. You can test each individual Lambda function deployed and used inside the state machine. You can feed various inputs into your Lambda function and confirm that the output is what you expect. In this case, you use Python pytest to start testing and validate the results.
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Please note that in the CodeCommit repository there is a folder called tests/unit, which includes a set of unit tests that are run and validated against your Lambda function code. Another very important part of this BuildSpec file is the reporting section, which generates reports and metrics about the results, trends, and overall success of your tests.
After running a unit test, you can see a report of the results of the run. Note the reports section of the BuildSpec file, along with the –junitxml=reports/unit.xml command that runs alongside the pytest command. This generates a set of reports that can be displayed in CodeBuild.
Navigate to the specific CodeBuild project you want to test and click on the specific implementation of interest. There is a tab called Reports, as seen in the following screenshot:
Select the specific report you’re interested in to see detailed information about the tests that were run, as shown in the following screenshot:
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With the Reporting Group, you can also see an aggregated list of tests that have been run over time. This report includes various features such as average number of test cases run, average duration, and overall pass rate, as shown in the following screenshot:
The following BuildSpec file is used to create a CloudFormation template that injects a State Machine Definition into CloudFormation.
The Python script that creates the CloudFormation template to deploy a State Machine Definition based on the sm_def.json file in your repository is as follows:
To verify the full functionality of the entire state machine, you should stand it up so you can test it appropriately. This is an exact replica of what you will deploy to Production: a stack completely separate from the actual production stack deployed after passing appropriate end-to-end tests and approvals . You can leverage the CloudFormation target supported by CodePipeline. Please note the configuration in the following screenshot, which illustrates how to configure this step in the console:
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To validate that the entire state machine works and executes without any problems due to any particular change, feed it some sample inputs and issue assertions on the input values out specifically. If the specific validations pass and you get the expected results, you can proceed to the manual approval phase.
To continue in the CI/CD pipeline, a formal approval phase is required before deploying to Production. Using Manual Approval Stages in CodePipeline, you can configure the pipeline to pause and send messages to an Amazon SNS topic before continuing. An SNS topic can have many subscribers, but in this case, register the approver’s email address for the topic so they can be notified whenever approval is requested. Once the approver approves the pipeline to Production, the pipeline proceeds to deploy the production version of the Step Function state machine.
This Manual Approval stage can be configured in the console using a configuration similar to the following:
After passing the linting, unit testing, comprehensive testing, and Manual Approval stages, you can move on to deploying the Step Function state machine to Production. This phase is similar to the Deployment Test phase, except the name of your CloudFormation stack is different. In this case, you also leverage the CloudFormation target for CodeDeploy:
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After validating that the test state machine and Lambda functions work, add a CloudFormation step to tear down the existing test infrastructure (as it is no longer needed). You can configure this step as a new CodePipeline step similar to the configuration below:
You’ve found bugs and validated your State Language definition, unit tested your Lambda function code, deployed a test state machine, run comprehensive tests, received Manual Approval to deploy to Production exported and deployed to Production. This gives you and your team confidence that any changes made to the state machine and surrounding Lambda function code work correctly in Production.Update June 2022: In this article, I use AWS CodePipeline to do Continuous Deployment but I have since switched to using GitHub Actions for both CI and CD. However, the testing concepts I discuss in this article and how they fit into different stages of the process are still very relevant no matter which CI/CD service you use.
Designing a testing strategy and CI/CD pipeline for your serverless application goes hand in hand. You cannot do one without the other. And if you don’t have a dedicated DevOps expert on your team, it’s hard to know what your process will look like.
In this article, I’ll help you answer these questions by walking through two relatively simple CI/CD pipelines I’ve used with my customers. For each workflow, we will look at different types of tests or checks that run at different stages.
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The goal of this workflow is to check the quality of the code changes the developer wants to merge before performing any human code reviews and to respond to any violations back to the developer in a timely manner. as quickly and accurately as possible.
Additionally: The steps I ran here are CI only without CD. This decision is a trade-off in the complexity involved in setting up an ephemeral AWS environment for each Pull Request instead of a persistent environment of
. That said, you may very well find it worth investing this effort into your team to provide feedback on the failure to integrate/test E2E before the merge.
(See this article for details on the technique I used here to cache NPM settings node_modules between executions, which is the slowest step in
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