CI/CD pipeline

A CI/CD pipeline is a series of steps that automate the process of software delivery. CI stands for continuous integration, which means that developers can merge their code changes frequently and check them for errors. CD stands for continuous delive…


This content originally appeared on DEV Community and was authored by Pranav Bakare

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A CI/CD pipeline is a series of steps that automate the process of software delivery. CI stands for continuous integration, which means that developers can merge their code changes frequently and check them for errors. CD stands for continuous delivery or continuous deployment, which means that the software can be released to customers or users at any time.

A typical CI/CD pipeline consists of the following stages:

Source: The code is stored in a version control system, such as Git, and any change triggers the pipeline.

Build: The code and its dependencies are compiled or packaged into a runnable form, such as an executable file or a container image.

Test: The code is tested for functionality, performance, security and quality using automated tools and frameworks.

Deploy: The code is deployed to a staging or production environment, where it can be accessed by customers or users.

Monitor: The code is monitored for errors, feedback and usage metrics, which can inform further improvements or fixes.

A CI/CD pipeline helps software teams to deliver software faster, more reliably and more frequently. It also reduces manual errors, improves feedback loops and enables faster iterations.

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This breakdown simplifies the key stages of a Continuous Integration/Continuous Deployment (CI/CD) pipeline, highlighting how automation and testing make the software delivery process more reliable and efficient:

  1. Code Changes:
    Developers make changes to the codebase to introduce new features, bug fixes, or improvements.

  2. Code Repository:
    The modified code is pushed to a version control system (e.g., Git). This triggers the CI/CD pipeline to start.

  3. Build:
    The CI server pulls the latest code from the repository and initiates the build process.
    Compilation, dependency resolution, and other build tasks are performed to create executable artifacts.

  4. Predeployment Testing:
    Automated tests (unit tests, integration tests, etc.) are executed to ensure that the changes haven't introduced errors.
    This phase also includes static code analysis to check for coding standards and potential issues.

  5. Staging Environment:
    If the pre deployment tests pass, the artifacts are deployed to a staging environment that closely resembles the production environment.

  6. Staging Tests:
    Additional tests, specific to the staging environment, are conducted to validate the behavior of the application in an environment that mirrors production.

  7. Approval/Gate:
    In some cases, a manual approval step or a set of gates may be included, requiring human intervention or meeting specific criteria before proceeding to the next stage.

  8. Deployment to Production:
    If all tests pass and any necessary approvals are obtained, the artifacts are deployed to the production environment.

  9. Post deployment Testing
    After deployment to production, additional tests may be performed to ensure the application's stability and performance in the live environment.

  10. Monitoring:
    Continuous monitoring tools are employed to track the application's performance, detect potential issues, and gather insights into user behaviour.

  11. Rollback (If Necessary):
    If issues are detected post deployment, the CI/CD pipeline may support an automatic or manual rollback to a previous version.

  12. Notification:
    The CI/CD pipeline notifies relevant stakeholders about the success or failure of the deployment, providing transparency and accountability.

This iterative and automated process ensures that changes to the codebase can be quickly and reliably delivered to production, promoting a more efficient and consistent software delivery lifecycle. It also helps in catching potential issues early in the development process, reducing the risk associated with deploying changes to production.


This content originally appeared on DEV Community and was authored by Pranav Bakare


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