Add the package as a dev dependency (note you'll need to authenticate with github packages).
npm install -D @space48/cloud-seed
Define your first cloud function:
src/myFirstFunction.ts
import { HttpFunction } from "@google-cloud/functions-framework";
import { GcpConfig } from "@space48/cloud-seed";
const myFunction: HttpFunction = (req, res) => {
console.log("Hello World");
return res.sendStatus(200);
};
export default myFunction;
export const runtimeConfig: GcpConfig = {
cloud: "gcp",
type: "http",
public: true,
};Then run your build command:
npx @space48/cloud-seed build
To apply the terraform config you'll be able to run the following:
terraform chdir=[buildDir] init
terraform chdir=[buildDir] plan -out=plan
terraform chdir=[buildDir] apply plan
Each Cloud Function you wish to define can live anywhere within the src directory, how you structure this is up to you.
The only thing you need to define for your function to work is the named export: runtimeConfig. Take a look at the RuntimeConfig type to see what options you have (thats in runtime.ts).
import { HttpFunction } from "@google-cloud/functions-framework";
import type { GcpConfig } from "@space48/cloud-seed";
const myFunction: HttpFunction = (req, res) => {
console.log("Hello World");
return res.sendStatus(200);
};
export default myFunction;
export const runtimeConfig: GcpConfig = {
cloud: "gcp",
type: "http",
// Configure if the cloud function should be publically executable or not.
public: true,
};import { CloudEventFunction } from "@google-cloud/functions-framework";
import type { GcpConfig } from "@space48/cloud-seed";
const fn: CloudEventFunction (data) => {
console.log("This is a event triggered function", data);
};
export default fn;
export const runtimeConfig: GcpConfig = {
cloud: "gcp",
type: "event",
// By defining the topicName it will create the topic for you.
topicName: "hello-world",
};import { CloudEventFunction } from "@google-cloud/functions-framework";
import type { GcpConfig } from "@space48/cloud-seed";
const fn: CloudEventFunction = (data) => {
console.log("This is a scheduled triggered function", data);
};
export default fn;
export const runtimeConfig: GcpConfig = {
cloud: "gcp",
type: "schedule",
schedule: "* * * * *",
};Supported only in gen 2 supported versions of cloud-seed
import { CloudEventFunction } from "@google-cloud/functions-framework";
import type { GcpConfig } from "@space48/cloud-seed";
const fn: CloudEventFunction = (data) => {
console.log("This is a scheduled triggered function, triggered directly from the scheduled job", data);
};
export default fn;
export const runtimeConfig: GcpConfig = {
cloud: "gcp",
schedule: "* * * * *",
type: "scheduledJob",
version: "gen2",
attemptDeadline: "1800s", // optional, this is set as 3 minutes by default
};import { HttpFunction } from "@google-cloud/functions-framework";
import type { GcpConfig } from "@space48/cloud-seed";
const fn: HttpFunction = (req, res) => {
console.log(req.body);
return res.sendStatus(200);
};
export default fn;
export const runtimeConfig: GcpConfig = {
cloud: "gcp",
type: "queue",
};import { CloudEventFunction } from "@google-cloud/functions-framework";
import type { GcpConfig } from "@space48/cloud-seed";
const fn: CloudEventFunction = (data) => {
console.log("This is a firestore triggered function", data);
};
export default fn;
export const runtimeConfig: GcpConfig = {
cloud: "gcp",
type: "firestore",
document: "collection/{doc}",
// Optional event type (defaults to 'write').
firestoreEvent: "create"
};import { CloudEventFunction } from "@google-cloud/functions-framework";
import type { GcpConfig } from "@space48/cloud-seed";
const fn: CloudEventFunction = (data) => {
console.log("This is a cloud storage triggered function", data);
};
export default fn;
export const runtimeConfig: GcpConfig = {
cloud: "gcp",
type: "storage",
bucket: {
default: "myBucket",
// optional environment-specific buckets
environmentSpecific: {
production: "myProdBucket",
...
},
},
// Optional event type (defaults to 'finalize').
storageEvent: "finalize"
};You can override any value in runtimeConfig for a specific environment by including an environmentOverrides object. For example:
import { GcpConfig } from "@space48/cloud-seed";
export const runtimeConfig: GcpConfig = {
cloud: "gcp",
type: "http",
public: true,
minInstances: 0,
environmentOverrides: {
production: {
minInstances: 1,
},
},
};You can set the cloud seed config by adding a cloudseed.json file in the project root directory. An example is provided below:
{
"$schema": "./node_modules/@space48/cloud-seed/schemas/cloudseed.schema.json",
"default": {
"cloud": {
"gcp": {
"region": "europe-west2"
}
},
"buildConfig": {
"dir": "./src",
"outDir": "./.build",
},
"secretVariableNames": [
"apiKey1",
"apiKey2"
]
},
"environmentOverrides": {
"staging": {
"cloud": {
"gcp": {
"project": "example-project-staging"
}
},
"tfConfig": {
"backend": {
"type": "gcs",
"backendOptions": {
"bucket": "example-backend-bucket",
"prefix": "path/to/staging/statefile/directory"
}
}
},
"runtimeEnvironmentVariables": {
"FOO": "Staging1",
"BAR": "Staging2"
}
},
"production": {
"cloud": {
"gcp": {
"project": "example-project-production"
}
},
"tfConfig": {
"backend": {
"type": "gcs",
"backendOptions": {
"bucket": "example-backend-bucket",
"prefix": "path/to/production/statefile/directory"
}
}
},
"runtimeEnvironmentVariables": {
"FOO": "Prod1",
"BAR": "Prod2"
}
}
}
}This package can also be called via a JS API.
import { build, BaseConfig } from "@space48/cloud-seed";
import GoogleProvider from "@cdktf/provider-google";
import { GcsBackend, TerraformStack } from "cdktf";
import { Construct } from "constructs";
class CustomStack extends TerraformStack {
constructor(scope: Construct, id: string, options: BaseConfig) {
super(scope, id);
options.tfConfig.backend.type === "gcs" && new GcsBackend(this, {
bucket: options.tfConfig.backend.backendOptions.bucket,
prefix: options.tfConfig.backend.backendOptions.prefix + "-custom-stack",
});
new GoogleProvider.GoogleProvider(this, "Google", {
region: options.cloud.gcp.region,
project: options.cloud.gcp.project,
});
/**
* Example infrastructure
*/
new GoogleProvider.StorageBucket(this, "CustomBucket", {
name: `my-custom-bucket-${process.env.ENVIRONMENT}`,
location: options.cloud.gcp.region.toUpperCase(),
storageClass: "STANDARD",
uniformBucketLevelAccess: true,
});
}
}
// build() is the same as per the CLI command, and returns the parsed Cloud Seed config and the app construct
const { config, app } = build({ environment: process.env.ENVIRONMENT });
new CustomStack(app, "CustomStack", config);
app.synth();It is recommended by google to use the gen 2 functions wherever possible.
The cloud-seed v1.3.6-alpha is an upgraded version to use gen 2 functions, which is still in alpha stage. This is currently being used in Sneakers-n-stuff.
If you upgrade a project, that is using an older version of cloud-seed (ex:v1.3.0 is the latest stable version) to the 1.3.6-alpha version, all the existing cloud functions (which use older csktf npm modules) will be deleted and recreated. This can lead to an issue as functions/topics/secrets can't be created with the same name for 7 days, when they get deleted. Therefore upgrading a project that uses cloud-seed, has to be done cautiously.
But for a new project it is recommended to use the 1.3.6-alpha version with gen 2 functions support.
The structure is similar to what is explained above. But in runtime config add version: "gen2"
export const runtimeConfig: GcpConfig = {
runtime: "nodejs18",
cloud: "gcp",
type: "schedule",
schedule: "0 0 * * *",
memory: 2048,
timeout: 540,
version: "gen2",
};If a version is not given, the function will be automatically added as a gen 1.
pythonandmakeshould be installed in the build server
- add to the bitbucket pipeline build step
- step: &build
name: "Build"
caches:
- npm
script:
# Install Python and Make
- chmod +x ./bitbucket-pipelines/install_python_and_make.sh
- ./bitbucket-pipelines/install_python_and_make.sh- Add a new bash script:
install_python_and_make.sh
#!/bin/bash
apt update && apt -y install python3 && apt -y install build-essential- Enable Eventarc API in GCP as gen2 cloud functions are built on Cloud Run and Eventarc
This project is licenced under the MIT License.
The authors wish to acknowledge our collaboration with the open-source Cloud Seed project by user MNahad, and that certain features in our project are derived from it.