Create clusters and services and pipelines on AWS using Terraform.
This is a incremental evolution from ecs-pipeline for big projects with more microservices and service discovery on AWS
AWS Stack Implementation:
- VPC
- ECS Fargate
- Codepipeline
- Codebuild
- Application Load Balancer
- CloudMap
This demo project search for a buildspec.yml on root path from repository. You can see an example here.
Edit main.tf
# Customize your AWS Region
variable "aws_region" {
description = "AWS Region for the VPC"
default = "us-east-1"
}
provider "aws" {
region = var.aws_region
}
data "aws_caller_identity" "current" {}Edit clusters.tf file to customize a cluster preferences. Give infos like ALB basic configurations, AZ's and etc.
module "cluster_example" {
source = "./modules/ecs"
vpc_id = module.vpc.vpc_id
cluster_name = var.cluster_name
listener = {
port = 8080
protocol = "HTTP"
certificate_arn = ""
ssl_policy = "" // Default "ELBSecurityPolicy-TLS-1-1-2017-01"
}
availability_zones = [
module.vpc.public_subnet_1a,
module.vpc.public_subnet_1b
]
}output "cluster_id" {}
output "alb" {}
output "listener" {}
Edit services.tf and customize an service configurations, like Github sources, containers preferences, alb route path and auto scale preferences.
module "service_whois" {
source = "./modules/service"
vpc_id = module.vpc.vpc_id
region = var.aws_region
is_public = true
# Service name
service_name = "service-whois"
service_base_path = "/whois*"
service_priority = 400
container_port = 8080
service_healthcheck = {
healthy_threshold = 3
unhealthy_threshold = 10
timeout = 10
interval = 60
matcher = "200"
path = "/healthcheck"
port = 8080
}
# Cluster to deploy your service - see in clusters.tf
cluster_name = var.cluster_name
cluster_id = module.cluster_example.cluster_id
cluster_listener = module.cluster_example.listener
cluster_mesh = module.cluster_example.cluster_mesh
cluster_service_discovery = module.cluster_example.cluster_service_discovery
# Auto Scale Limits
desired_tasks = 2
min_tasks = 2
max_tasks = 10
# Tasks CPU / Memory limits
desired_task_cpu = 256
desired_task_mem = 512
# CPU metrics for Auto Scale
cpu_to_scale_up = 30
cpu_to_scale_down = 20
cpu_verification_period = 60
cpu_evaluation_periods = 1
# Pipeline Configuration
build_image = "aws/codebuild/docker:17.09.0"
git_repository_owner = "msfidelis"
git_repository_name = "microservice-nadave-whois"
git_repository_branch = "master"
# AZ's
availability_zones = [
module.vpc.public_subnet_1a,
module.vpc.public_subnet_1b
]
}Just specify a value true on enable_container_insights parameter. (Default: false)
module "cluster_example" {
source = "./modules/ecs"
vpc_id = module.vpc.vpc_id
cluster_name = var.cluster_name
// ...
enable_container_insights = true
// ...
}
Just specify a value FARGATE_SPOT on service_launch_type parameter. (Default: FARGATE)
To change this value is necessary recreate a service. This is causes downtime on production.
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Create your Github Access Token to Command Line. This link have all information about this.
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Export Github Token as an environment variable.
export GITHUB_TOKEN=YOUR_TOKEN- Initialize Terraform
terraform init- Plan our modifications
terraform plan- Apply the changes on AWS
terraform apply-
Appmesh
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Multi Listeners
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Fargate Spot
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Private Services
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Workers
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Bitbucket integrations
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Gitlab integrations
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EFS Support