When working with large-scale infrastructure deployments, managing the provisioning and orchestration of resources efficiently becomes crucial. Terraform, a widely-used Infrastructure as Code (IaC) tool, offers a feature known as parallelism to accelerate the deployment process. In this blog post, we’ll delve into parallelism in Terraform, how it affects the Directed Acyclic Graph (DAG), and how you can control and optimize its usage.

Understanding Parallelism and the DAG

Parallelism refers to the ability to execute multiple tasks simultaneously. In the context of Terraform, parallelism enables the provisioning of resources in parallel, speeding up the infrastructure deployment process. However, parallelism introduces complexities due to resource dependencies and potential conflicts.

The Directed Acyclic Graph (DAG) is a fundamental concept in Terraform. It represents the dependency relationships among resources in your infrastructure. Each resource is a node in the DAG, and edges represent dependencies. Terraform uses the DAG to determine the order in which resources are created or modified.

Effects of Parallelism on the DAG

Parallelism significantly impacts the DAG and its execution. When Terraform executes in parallel, it creates multiple resource instances concurrently, potentially leading to resource conflicts and race conditions. Therefore, understanding how parallelism affects the DAG is essential to avoid unexpected behavior.

Let’s consider a simplified example using an AWS infrastructure setup. Imagine you have three resources: a VPC, a subnet, and a security group. The subnet and security group depend on the VPC being created first. In a serial execution, Terraform ensures that the VPC is created before the subnet and security group. However, in a parallel execution, issues might arise if the subnet and security group tasks attempt to execute before the VPC is ready.

Controlling Parallelism in Terraform

Terraform allows you to control parallelism using the -parallelism flag or the parallelism configuration option. The value you set determines the maximum number of resource operations that can occur simultaneously.

Consider this example where you have a large number of EC2 instances to create:

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provider "aws" {
  region = "us-west-1"
}

resource "aws_instance" "example" {
  count = 20
  ami           = "ami-0c55b159cbfafe1f0"
  instance_type = "t2.micro"
}

To limit the number of parallel operations to 5, you can set the parallelism either in the CLI:

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terraform apply -parallelism=5

Or in the Terraform configuration:

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terraform {
  required_version = ">=0.12"
  required_providers {
    aws = ">=2.0"
  }

  parallelism = 5
}

Optimizing Parallelism for DAG

While parallelism speeds up deployment, improper use can lead to resource conflicts and degraded performance. Optimizing parallelism involves analyzing resource dependencies, understanding infrastructure topology, and carefully configuring parallelism settings.

  1. Analyze Dependencies: Identify resource dependencies and ensure that critical resources are provisioned before dependent resources.

  2. Resource Grouping: Group independent resources and apply parallelism settings based on logical groupings to avoid unnecessary dependencies.

  3. Module Level Parallelism: When using modules, control parallelism within each module to maintain resource ordering within the module’s scope.

  4. Performance Monitoring: Monitor infrastructure provisioning to identify performance bottlenecks and fine-tune parallelism settings.

In closing

Parallelism is a powerful feature in Terraform that accelerates infrastructure deployment. However, it must be used thoughtfully to avoid resource conflicts and ensure the integrity of the DAG. By understanding the implications of parallelism on the DAG and employing effective control strategies, you can harness its benefits while maintaining a reliable and consistent infrastructure provisioning process.