service is like a project. It's where you define your Google Cloud Functions, the
events that trigger them and any
resources they require, all in a file called
To get started building your first Serverless Framework project, create a
In the beginning of an application, many people use a single Service to define all of the Functions, Events and Resources for that project. This is what we recommend in the beginning.
myService/ serverless.yml # Contains all functions and infrastructure resources
However, as your application grows, you can break it out into multiple services. A lot of people organize their services by workflows or data models, and group the functions related to those workflows and data models together in the service.
users/ serverless.yml # Contains 4 functions that do Users CRUD operations and the Users database posts/ serverless.yml # Contains 4 functions that do Posts CRUD operations and the Posts database comments/ serverless.yml # Contains 4 functions that do Comments CRUD operations and the Comments database
This makes sense since related functions usually use common infrastructure resources, and you want to keep those functions and resources together as a single unit of deployment, for better organization and separation of concerns.
To create a service, use the
create command. You can also pass in a path to create a directory and auto-name your service:
# Create service with Node.js template in the folder ./my-service serverless create --template google-nodejs --path my-service
Here are the available runtimes for Google Cloud Functions:
Check out the create command docs for all the details and options.
You'll see the following files in your working directory:
service configuration is managed in the
serverless.yml file. The main responsibilities of this file are:
Define one or more functions in the service
eventssection to automatically create the resources required for the event upon deployment
You can see the name of the service, the provider configuration and the first function inside the
functions definition. Any further service configuration will be done in this file.
# serverless.yml service: my-gcloud-service provider: name: google plugins: - serverless-google-cloudfunctions functions: first: handler: http events: - http: path
index.js file contains your exported functions.
When you deploy a Service, all of the Functions, and Events in your
serverless.yml are translated into calls to the Google Cloud API to dynamically define those resources.
To deploy a service, first
cd into the relevant service directory:
Then use the
To easily remove your Service from your Google Cloud project, you can use the
serverless remove to trigger the removal process.
Serverless will start the removal and informs you about it's process on the console. A success message is printed once the whole service is removed.
The removal process will only remove the service on your provider's infrastructure. The service directory will still remain on your local machine so you can still modify and (re)deploy it to another stage, region or provider later on.
The Serverless framework is usually installed globally via
npm install -g serverless. This way you have the Serverless CLI available for all your services.
Installing tools globally has the downside that the version can't be pinned inside package.json. This can lead to issues if you upgrade Serverless, but your colleagues or CI system don't. You can use a feature in your serverless.yml without worrying that your CI system will deploy with an old version of Serverless.
To configure version pinning define a
frameworkVersion property in your serverless.yaml. Whenever you run a Serverless command from the CLI it checks if your current Serverless version is matching the
frameworkVersion range. The CLI uses Semantic Versioning so you can pin it to an exact version or provide a range. In general we recommend to pin to an exact version to ensure everybody in your team has the exact same setup and no unexpected problems happen.
# serverless.yml frameworkVersion: '=1.0.3'
# serverless.yml frameworkVersion: '>=1.0.0 <2.0.0'
If you already have a Serverless service, and would prefer to lock down the framework version using
package.json, then you can install Serverless as follows:
# from within a service npm install serverless --save-dev
To execute the locally installed Serverless executable you have to reference the binary out of the node modules directory.
node ./node_modules/serverless/bin/serverless deploy