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Set Up a CI/CD Pipeline
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Many developers use continuous integration, delivery, and deployment pipelines to automatically test and publish their applications whenever they make changes. This is most common and useful for larger apps where multiple people work on the codebase in parallel using a shared version control system like git.
This guide covers the high level stages common to most CI/CD pipelines and describes what you might do in each stage. Further, it includes a list of common tasks and actions you might perform within your pipelines to configure and test your Atlas App Services Apps.
See a real example with GitHub Actions
If you'd like to see an example CI/CD pipeline that manages testing, deployment, and other tasks for a real application, check out the article How to Build CI/CD Pipelines for App Services Apps Using GitHub Actions on the MongoDB Developer Hub.
At a high level most pipelines share a common pattern of moving through multiple stages that each handle different concerns.
The Development stage is the first step for creating new features and fixing bugs in an App. In this stage you work with your application's configuration files and source code to implement your desired changes.
To develop new features for an existing app:
Fork the main app and deploy a new development copy. This instance will have a different App ID than your production app. You can also use environment value templates to use development data sources and other services that are not linked to production.
Develop your application. This could involve updating or adding a client app screen, adding a new database trigger, or any other application features. You can use Development Mode if you need to make changes to your synced Realm Object Schema.
Run automated tests locally to ensure that your code does not introduce any new errors. Tests that pass locally do not guarantee that your app is free of integration bugs, but increase confidence that your changes do not include any regressions or unintended behavior.
The Staging stage, which you might also call QA (Quality Assurance), Testing, or Pre-Production, is a step that simulates your development changes in an environment that is as similar to production as possible. This gives you a usable version of your app for review and can help you catch integration bugs with live services without affecting production data.
The specifics of your staging deployment depend on your application's needs. However, you can use the following high level procedure to set it up:
Set up your staging environment. Use separate, non-production services and data sources with configurations that mirror production as closely as possible. For example, you might use an Atlas cluster named
stagingthat otherwise has the same configuration as your
productioncluster. Depending on your use case, you may have a consistent App that you reuse for all staging builds or you may create a new App for each staging build.
Create or use an existing staging build. You can automatically create a staging build as part of your CI/CD process, such as when you create a new new pull request. You can use a new app for each staging build or you can reuse a prebuilt environment that you share across builds.
Verify that your app behaves as expected. This might involve running an automated test suite against your staging environment, manually checking behavior, or getting approval through a user-acceptance test.
The Production stage is the final deployment step where your modified app is deployed into your production environment. Ideally at this stage you have already tested your changes locally and in staging to confirm that they are safe to deploy. You can either deploy to production manually or automatically as part of your CI/CD workflow by updating your production app.
This section outlines common tasks that you will perform in your CI/CD pipeline. You may not always do all of these tasks depending on your use case and pipeline stage, but in general most pipelines will perform all of these at least once.
Configure the Environment
The configuration and code for your app should generally be similar between development stages. However, you'll want to change the value of certain configuration options depending on the environment.
Determine what stage you're building and set the appropriate configuration values. For example, you might configure the app with the App ID of a new app in the Development stage or use your production App ID in the Production stage.
# Use the production App ID for the main branch export REALM_APP_ID="myapp-abcde" # Use a staging App ID for the QA branch export REALM_APP_ID="myapp-staging-fghij" # Use a new App ID for development branches - you'll need to create the app first! export REALM_APP_ID="myapp-dev-zyxwv"
Find Your App ID
You might not always be able to hardcode your App ID. You can look up a specific App ID with Realm CLI. For an example, see Create an App.
Set Up Realm CLI
Realm CLI is the easiest way to programmatically create, configure, and manage App Services Apps. You should install and use the latest version in your deployment scripts.
npm install -g mongodb-realm-cli
You'll also need a MongoDB Atlas public/private API key pair to authenticate and use the CLI. For more information and a walkthrough of how to get an API key, see Programmatic API Keys.
To log in, save your API keys in a new named profile configuration and then log in with that profile:
<Profile Name>: public_api_key: "<MongoDB Atlas Public API Key>" private_api_key: "<MongoDB Atlas Private API Key>" atlas_base_url: "https://cloud.mongodb.com" realm_base_url: "https://realm.mongodb.com" telemetry_mode: ""
realm-cli login --profile="<Profile Name>"
Make sure to use the
--profile flag in all of your commands, otherwise
Realm CLI won't recognize that you're logged in.
Create an App
You can use Realm CLI to create new apps to use in development and testing. If your pipeline is in the Development or Staging phase, you should deploy and test changes with an app other than your live production app.
To use a new app for your development or staging branch:
Create a New App
Push a new app based on your branch of the app's configuration files:
cd path/to/realmApp realm-cli push -y --project="<MongoDB Atlas Project ID>" # e.g. --project="609ea544934fe445460219a2"
Save the App ID
The new app has a unique App ID value that you'll need to identify it later in your pipeline and in your client app. You should save the value to an environment variable, file, or other location.
# Save to an environment variable output=$(realm-cli app describe) app_id=$(echo $output | sed 's/^.*client_app_id": "\([^"]*\).*/\1/') export REALM_APP_ID=app_id # Save to a file echo $REALM_APP_ID > ./clients/ios/realm-app-id.txt
Update an App
You can use Realm CLI to update an existing app, like a shared staging app or your production deployment. The app already exists, so you should be able to look up its App ID.
To update an existing app, specify its App ID in the
realm-cli push --remote=$REALM_APP_ID -y
Run Tests Against the App
Your app should include automated unit and integration test suites that you can run to verify that everything works. The specifics of your test setup will vary depending upon your app, but you may need to run tests across multiple platforms using a variety of simulators.
If you have integration tests, you could checkout previous releases and run your integration tests against the current version of the App to ensure backwards compatibility.
Clean Up the Job
At the end of a CI/CD stage or pipeline you may want to clean up resources that you created specifically for that test. For example, if you create a new Development or Staging app, you might delete the apps and any databases associated with them once your changes are merged. Alternatively, you would not want to clean up your production app or a persistent staging app if you use one.
Before you clean up, consider what resources may be useful in future. For example, you could choose to skip deleting apps and their databases if your tests fail. That way you can manually investigate the issue and find any app settings or data that caused the failure.