Deploying Spark with Terraform: Simplify and Scale Your Data Platform

Introduction:

Terraform and Spark are two powerful tools that can revolutionize the way you deploy and manage your data platform. Terraform is an infrastructure as code (IaC) tool that allows you to define and provision infrastructure resources in a declarative way. On the other hand, Spark is a distributed data processing framework that enables scalable and efficient data processing. By combining the capabilities of Terraform and Spark, you can automate the deployment of Spark clusters, simplify management, and ensure scalability for your data platform.

Code Sample 1: Virtual Network (VPC)

resource "aws_vpc" "spark_vpc" {
  cidr_block = "10.0.0.0/16"

  # Additional VPC configurations
  enable_dns_support = true
  enable_dns_hostnames = true
  tags = {
    Name = "Spark VPC"
    Environment = "Production"
  }
}

In the first code sample, we defined an AWS Virtual Private Cloud (VPC) using the “aws_vpc” resource. By specifying the CIDR block and enabling DNS support and hostnames, we ensure a secure and functional network environment for the Spark cluster. Tags were added for better organization and management.

Code Sample 2: Subnet

resource "aws_subnet" "spark_subnet" {
  vpc_id     = aws_vpc.spark_vpc.id
  cidr_block = "10.0.1.0/24"

  # Additional subnet configurations
  availability_zone = "us-east-1a"
  tags = {
    Name = "Spark Subnet"
    Environment = "Production"
  }
}

Moving on to the second code sample, we created a subnet within the VPC using the “aws_subnet” resource. The subnet is associated with the Spark VPC and configured with a specific CIDR block. We also specified the availability zone and added tags to the subnet for easier identification and management.

Code Sample 3: Security Group

resource "aws_security_group" "spark_sg" {
  vpc_id = aws_vpc.spark_vpc.id

  # Define security group rules for inbound and outbound traffic
  ingress {
    from_port   = 0
    to_port     = 65535
    protocol    = "tcp"
    cidr_blocks = ["0.0.0.0/0"]
  }

  egress {
    from_port   = 0
    to_port     = 0
    protocol    = "-1"
    cidr_blocks = ["0.0.0.0/0"]
  }

  tags = {
    Name = "Spark Security Group"
    Environment = "Production"
  }
}

Code Sample 3 demonstrates the creation of a security group using the “aws_security_group” resource. The security group defines the rules for inbound and outbound traffic, allowing TCP traffic on any port for inbound connections and permitting all outbound traffic. These rules can be customized based on your security requirements. Tags are added for identification and management purposes.

Code Sample 4: Spark Cluster

resource "aws_emr_cluster" "spark_cluster" {
  name               = "my-spark-cluster"
  release_label      = "emr-6.4.0"
  instance_type      = "m5.xlarge"
  instance_count     = 3

  # Additional Spark cluster configurations
  applications = ["Spark"]
  bootstrap_action {
    path = "s3://my-bucket/bootstrap.sh"
    name = "My Bootstrap"
  }

  tags = {
    Name = "Spark Cluster"
    Environment = "Production"
  }
}

In this code block, we define the Spark cluster using the “aws_emr_cluster” resource. The cluster is named “my-spark-cluster” and uses the release label “emr-6.4.0”. We specify the instance type as “m5.xlarge” and set the instance count to 3.

To further configure the Spark cluster, we include additional configurations. We specify the applications to be installed, in this case, “Spark”. We also define a bootstrap action to run a script located in an S3 bucket.

Tags are added to the cluster resource for better organization and management.

Conclusion

These code samples showcase the setup of the security group and the Spark cluster using Terraform. With the security group, you can control the network traffic to and from your Spark cluster, ensuring the desired level of security. The Spark cluster resource allows you to define various configurations such as the cluster name, instance types, applications, and bootstrap actions. Customize these resources according to your specific requirements to create a resilient and efficient Spark cluster.

At Anant, we are committed to helping companies modernize and maintain their data platforms. As experts in data engineering and with a specialization in Cassandra consulting and professional services, we empower our clients with cutting-edge technology and comprehensive solutions. Contact us to see how we can support your data platform automation needs.

Photo by Josh Boot on Unsplash