Provision infrastructure with Dagger and AWS CloudFormation
In this guide, you will learn how to automatically provision infrastructure on AWS by integrating Amazon Cloudformation in your Dagger environment.
We will start with something simple: provisioning a new bucket on Amazon S3. But Cloudformation can provision almost any AWS resource, and Dagger can integrate with the full Cloudformation API.
Prerequisites
Reminder
Guidelines
The provisioning strategy detailed below follows S3 best practices. However, to remain agnostic of your current AWS level, it profoundly relies on S3 and Cloudformation documentation.
Relays
The first thing to consider when developing a plan based on relays is to read their universe reference: it summarizes the expected inputs and their corresponding formats. Here is the Cloudformation one.
Initialize a Dagger Project and Environment
(optional) Setup example app
You will need the local copy of the Dagger examples repository used in previous guides
git clone https://github.com/dagger/examples
Make sure to run all commands from the todoapp directory:
cd examples/todoapp
Organize your package
Let's create a new directory for our Cue package:
mkdir cloudformation
Create a basic plan
Let's implement the Cloudformation template and convert it to a Cue definition for further flexibility.
Setup the template and the environment
Setup the template
The idea here is to follow best practices in S3 buckets provisioning. Thankfully, the AWS documentation contains a working Cloudformation template that fits 95% of our needs.
1. Tweaking the template: output bucket name only
Create a file named template.cue
and add the following configuration to it.
package main
// inlined s3 cloudformation template as a string
template: """
{
"AWSTemplateFormatVersion": "2010-09-09",
"Resources": {
"S3Bucket": {
"Type": "AWS::S3::Bucket",
"Properties": {
"AccessControl": "PublicRead",
"WebsiteConfiguration": {
"IndexDocument": "index.html",
"ErrorDocument": "error.html"
}
},
"DeletionPolicy": "Retain"
},
"BucketPolicy": {
"Type": "AWS::S3::BucketPolicy",
"Properties": {
"PolicyDocument": {
"Id": "MyPolicy",
"Version": "2012-10-17",
"Statement": [
{
"Sid": "PublicReadForGetBucketObjects",
"Effect": "Allow",
"Principal": "*",
"Action": "s3:GetObject",
"Resource": {
"Fn::Join": [
"",
[
"arn:aws:s3:::",
{
"Ref": "S3Bucket"
},
"/*"
]
]
}
}
]
},
"Bucket": {
"Ref": "S3Bucket"
}
}
}
},
"Outputs": {
"Name": {
"Value": {
"Fn::GetAtt": ["S3Bucket", "Arn"]
},
"Description": "Name S3 Bucket"
}
}
}
"""
2. Cloudformation relay
As our plan relies on Cloudformation's relay, let's dissect the expected inputs by gradually incorporating them into our plan.
dagger doc alpha.dagger.io/aws/cloudformation
# Inputs:
# config.region string AWS region
# config.accessKey dagger.#Secret AWS access key
# config.secretKey dagger.#Secret AWS secret key
# source string Source is the Cloudformation template (JSON/YAML…
# stackName string Stackname is the cloudformation stack
# parameters struct Stack parameters
# onFailure *"DO_NOTHING" | "ROLLBACK" | "DELETE" Behavior when failure to create/update the Stack
# timeout *10 | >=0 & int Maximum waiting time until stack creation/update…
# neverUpdate *false | true Never update the stack if already exists
1. General insights
As seen above in the documentation, values starting with *
are default values. However, as a plan developer, we may need to add default values to inputs from relays without one: Cue gives you this flexibility.
2. The config value
The config values are all part of the aws
relay. Regarding this package, as you can see above, five of the required inputs miss default options (parameters
field is optional):
- config.region
- config.accessKey
- config.secretKey
- source
- stackName
Let's implement the first step, use the aws.#Config
relay, and request its first inputs: the region to deploy and the AWS credentials.
package main
import (
"alpha.dagger.io/aws"
)
// AWS account: credentials and region
awsConfig: aws.#Config
This defines:
awsConfig
: AWS CLI Configuration step using the packagealpha.dagger.io/aws
. It takes three user inputs: aregion
, anaccessKey
, and asecretKey
Setup the environment
1. Create a new environment
Let's create a project:
dagger init
Let's create an environment to run it:
dagger new 'cloudformation' -p ./cloudformation
2. Check plan
Pro tips: To check whether it worked or not, these three commands might help
dagger input list -e cloudformation # List our personal plan's inputs
# Input Value Set by user Description
# awsConfig.region string false AWS region
# awsConfig.accessKey dagger.#Secret false AWS access key
# awsConfig.secretKey dagger.#Secret false AWS secret key
dagger query -e cloudformation # Query values / inspect default values (Instrumental in case of conflict)
# {}
dagger up -e cloudformation # Try to run the plan. As expected, we encounter a failure because some user inputs haven't been set
# 4:11PM ERR system | required input is missing input=awsConfig.region
# 4:11PM ERR system | required input is missing input=awsConfig.accessKey
# 4:11PM ERR system | required input is missing input=awsConfig.secretKey
# 4:11PM FTL system | some required inputs are not set, please re-run with `--force` if you think it's a mistake missing=0s
Finish template setup
Now that we have the config
definition properly configured, let's modify the Cloudformation one:
package main
import (
"alpha.dagger.io/aws"
"alpha.dagger.io/dagger"
"alpha.dagger.io/random"
"alpha.dagger.io/aws/cloudformation"
)
// AWS account: credentials and region
awsConfig: aws.#Config
// Create a random suffix
suffix: random.#String & {
seed: ""
}
// Query the Cloudformation stackname, or create one with a random suffix for uniqueness
cfnStackName: *"stack-\(suffix.out)" | string & dagger.#Input
// AWS Cloudformation stdlib
cfnStack: cloudformation.#Stack & {
config: awsConfig
stackName: cfnStackName
source: template
}
This defines:
suffix
: random suffix leveraging therandom
relay. It doesn't have a seed because we don't care about predictabilitycfnStackName
: Name of the stack, either a default valuestack-suffix
or user inputcfnStack
: Cloudformation relay withAWS config
,stackName
andJSON template
as inputs
Configure the environment
Before bringing up the deployment, we need to provide the cfnStack
inputs declared in the configuration. Otherwise, Dagger will complain about missing inputs.
dagger up -e cloudformation
# 3:34PM ERR system | required input is missing input=awsConfig.region
# 3:34PM ERR system | required input is missing input=awsConfig.accessKey
# 3:34PM ERR system | required input is missing input=awsConfig.secretKey
# 3:34PM FTL system | some required inputs are not set, please re-run with `--force` if you think it's a mistake missing=0s
You can inspect the list of inputs (both required and optional) using dagger input list:
dagger input list -e cloudformation
# Input Value Set by user Description
# awsConfig.region string false AWS region
# awsConfig.accessKey dagger.#Secret false AWS access key
# awsConfig.secretKey dagger.#Secret false AWS secret key
# suffix.length *12 | number false length of the string
# cfnStack.onFailure *"DO_NOTHING" | "ROLLBACK" | "DELETE" false Behavior when failure to create/update the Stack
# cfnStack.timeout *10 | >=0 & int false Maximum waiting time until stack creation/update (in minutes)
# cfnStack.neverUpdate *false | true false Never update the stack if already exists
Let's provide the missing inputs:
dagger input text awsConfig.region us-east-2 -e cloudformation
dagger input secret awsConfig.accessKey yourAccessKey -e cloudformation
dagger input secret awsConfig.secretKey yourSecretKey -e cloudformation
Deploying
Finally ! We now have a working template ready to be used to provision S3 infrastructures. Let's deploy it:
- Normal deploy
- Debug deploy
dagger up -e cloudformation
#2:22PM INF suffix.out | computing
#2:22PM INF suffix.out | completed duration=200ms
#2:22PM INF cfnStack.outputs | computing
#2:22PM INF cfnStack.outputs | #15 1.304 {
#2:22PM INF cfnStack.outputs | #15 1.304 "Parameters": []
#2:22PM INF cfnStack.outputs | #15 1.304 }
#2:22PM INF cfnStack.outputs | #15 2.948 {
#2:22PM INF cfnStack.outputs | #15 2.948 "StackId": "arn:aws:cloudformation:us-east-2:817126022176:stack/stack-emktqcfwksng/207d29a0-cd0b-11eb-aafd-0a6bae5481b4"
#2:22PM INF cfnStack.outputs | #15 2.948 }
#2:22PM INF cfnStack.outputs | completed duration=35s
dagger output list -e cloudformation
# Output Value Description
# suffix.out "emktqcfwksng" generated random string
# cfnStack.outputs.Name "arn:aws:s3:::stack-emktqcfwksng-s3bucket-9eiowjs1jab4" -
dagger up -l debug -e cloudformation
#Output:
# 3:50PM DBG system | detected buildkit version version=v0.8.3
# 3:50PM DBG system | spawning buildkit job localdirs={
# "/tmp/infra-provisioning/.dagger/env/infra/plan": "/tmp/infra-provisioning/.dagger/env/infra/plan"
# } attrs=null
# 3:50PM DBG system | loading configuration
# ... Lots of logs ... :-D
# Output Value Description
# suffix.out "abnyiemsoqbm" generated random string
# cfnStack.outputs.Name "arn:aws:s3:::stack-abnyiemsoqbm-s3bucket-9eiowjs1jab4" -
dagger output list -e cloudformation
# Output Value Description
# suffix.out "abnyiemsoqbm" generated random string
# cfnStack.outputs.Name "arn:aws:s3:::stack-abnyiemsoqbm-s3bucket-9eiowjs1jab4" -
The deployment went well!
In case of a failure, the Debug deploy
tab shows the command to get more information.
The name of the provisioned S3 instance lies in the cfnStack.outputs.Name
output key, without arn:aws:s3:::
With this provisioning infrastructure, your dev team will easily be able to instantiate aws infrastructures: all they need to know is
dagger input list -e cloudformation
anddagger up -e cloudformation
isn't that awesome? :-D
Cue Cloudformation template
This section will convert the inlined JSON template to CUE to take advantage of the language features.
To do so quickly, we will first transform the template from JSON format to Cue format, then optimize it to leverage Cue's forces.
1. Create convert.cue
We will create a new convert.cue
file to process the conversion
package main
import "encoding/json"
s3Template: json.Unmarshal(template)
This defines:
s3Template
: contains the unmarshalled template.
You need to empty the plan and copy the convert.cue
file to the plan for Dagger to reference it
mv cloudformation/source.cue ~/tmp/
2. Retrieve the Unmarshalled JSON
Then, still in the same folder, query the s3Template
value to retrieve the Unmarshalled result of s3
:
dagger query s3Template -e cloudformation
# {
# "AWSTemplateFormatVersion": "2010-09-09",
# "Outputs": {
# "Name": {
# "Description": "Name S3 Bucket",
# "Value": {
# "Fn::GetAtt": [
# "S3Bucket",
# "Arn"
# ...
The commented output above is the cue version of the JSON Template, copy it
3. Remove convert.cue
rm cloudformation/convert.cue
4. Store the output
Open cloudformation/template.cue
and append below elements with copied Cue definition of the JSON:
// Add this line, to make it part to the cloudformation template
package main
import "encoding/json"
// Wrap exported Cue in previous point inside the `s3` value
s3: {
AWSTemplateFormatVersion: "2010-09-09"
Outputs: Name: {
Description: "Name S3 Bucket"
Value: "Fn::GetAtt": [
"S3Bucket",
"Arn",
]
}
Resources: {
BucketPolicy: {
Properties: {
Bucket: Ref: "S3Bucket"
PolicyDocument: {
Id: "MyPolicy"
Statement: [
{
Action: "s3:GetObject"
Effect: "Allow"
Principal: "*"
Resource: "Fn::Join": [
"",
[
"arn:aws:s3:::",
{
Ref: "S3Bucket"
},
"/*",
],
]
Sid: "PublicReadForGetBucketObjects"
},
]
Version: "2012-10-17"
}
}
Type: "AWS::S3::BucketPolicy"
}
S3Bucket: {
DeletionPolicy: "Retain"
Properties: {
AccessControl: "PublicRead"
WebsiteConfiguration: {
ErrorDocument: "error.html"
IndexDocument: "index.html"
}
}
Type: "AWS::S3::Bucket"
}
}
}
// Template contains the marshalled value of the s3 template
template: json.Marshal(s3)
We're using the built-in json.Marshal
function to convert CUE back to JSON, so Cloudformation still receives the same template.
You can inspect the configuration using dagger query -e cloudformation
to verify it produces the same manifest:
dagger query template -f text -e cloudformation
Now that the template is defined in CUE, we can use the language to add more flexibility to our template.
Let's define a re-usable #Deployment
definition in todoapp/cloudformation/deployment.cue
:
package main
#Deployment: {
// Bucket's output description
description: string
// index file
indexDocument: *"index.html" | string
// error file
errorDocument: *"error.html" | string
// Bucket policy version
version: *"2012-10-17" | string
// Retain as default deletion policy. Delete is also accepted but requires the s3 bucket to be empty
deletionPolicy: *"Retain" | "Delete"
// Canned access control list (ACL) that grants predefined permissions to the bucket
accessControl: *"PublicRead" | "Private" | "PublicReadWrite" | "AuthenticatedRead" | "LogDeliveryWrite" | "BucketOwnerRead" | "BucketOwnerFullControl" | "AwsExecRead"
// Modified copy of s3 value in `todoapp/cloudformation/template.cue`
template: {
AWSTemplateFormatVersion: "2010-09-09"
Outputs: Name: {
Description: description
Value: "Fn::GetAtt": [
"S3Bucket",
"Arn",
]
}
Resources: {
BucketPolicy: {
Properties: {
Bucket: Ref: "S3Bucket"
PolicyDocument: {
Id: "MyPolicy"
Statement: [
{
Action: "s3:GetObject"
Effect: "Allow"
Principal: "*"
Resource: "Fn::Join": [
"",
[
"arn:aws:s3:::",
{
Ref: "S3Bucket"
},
"/*",
],
]
Sid: "PublicReadForGetBucketObjects"
},
]
Version: version
}
}
Type: "AWS::S3::BucketPolicy"
}
S3Bucket: {
DeletionPolicy: deletionPolicy
Properties: {
AccessControl: "PublicRead"
WebsiteConfiguration: {
ErrorDocument: errorDocument
IndexDocument: indexDocument
}
}
Type: "AWS::S3::Bucket"
}
}
}
}
template.cue
can be rewritten as follows:
package main
import "encoding/json"
s3: #Deployment & {
description: "Name S3 Bucket"
}
// Template contains the marshalled value of the s3 template
template: json.Marshal(s3.template)
Verify template
Double-checks at the template level can be done with manual uploads on Cloudformation's web interface or by executing the below command locally:
tmpfile=$(mktemp ./tmp.XXXXXX) && dagger query template -f text -e cloudformation > "$tmpfile" && aws cloudformation validate-template --template-body file://"$tmpfile" ; rm "$tmpfile"
Let's make sure it yields the same result:
dagger query template -f text -e cloudformation
# {
# "description": "Name S3 Bucket",
# "indexDocument": "index.html",
# "errorDocument": "error.html",
# "version": "2012-10-17",
# "deletionPolicy": "Retain",
# "accessControl": "PublicRead",
# "template": {
# "AWSTemplateFormatVersion": "2010-09-09",
# "Outputs": {
# "Name": {
# "Description": "Name S3 Bucket",
# "Value": {
Reimplement source.cue
:
package main
import (
"alpha.dagger.io/aws"
"alpha.dagger.io/dagger"
"alpha.dagger.io/random"
"alpha.dagger.io/aws/cloudformation"
)
// AWS account: credentials and region
awsConfig: aws.#Config
// Create a random suffix
suffix: random.#String & {
seed: ""
}
// Query the Cloudformation stackname, or create one with a random suffix for uniqueness
cfnStackName: *"stack-\(suffix.out)" | string & dagger.#Input
// AWS Cloudformation stdlib
cfnStack: cloudformation.#Stack & {
config: awsConfig
stackName: cfnStackName
source: template
}
And we can now deploy it:
dagger up -e cloudformation
#2:22PM INF suffix.out | computing
#2:22PM INF suffix.out | completed duration=200ms
#2:22PM INF cfnStack.outputs | computing
#2:22PM INF cfnStack.outputs | #15 1.304 {
#2:22PM INF cfnStack.outputs | #15 1.304 "Parameters": []
#2:22PM INF cfnStack.outputs | #15 1.304 }
#2:22PM INF cfnStack.outputs | #15 2.948 {
#2:22PM INF cfnStack.outputs | #15 2.948 "StackId": "arn:aws:cloudformation:us-east-2:817126022176:stack/stack-emktqcfwksng/207d29a0-cd0b-11eb-aafd-0a6bae5481b4"
#2:22PM INF cfnStack.outputs | #15 2.948 }
#2:22PM INF cfnStack.outputs | completed duration=35s
Name of the deployed bucket:
dagger output list -e cloudformation
# Output Value Description
# suffix.out "ucwcecwwshdl" generated random string
# cfnStack.outputs.Name "arn:aws:s3:::stack-ucwcecwwshdl-s3bucket-gaqmj8rzsl08" -
The name of the provisioned S3 instance lies in the cfnStack.outputs.Name
output key, without arn:aws:s3:::
PS: This plan could be further extended with the AWS S3 example. It could provide infrastructure and quickly deploy it.
PS1: As it could be an excellent first exercise for you, this won't be detailed here. However, we're interested in your imagination: let us know your implementations :-)