Automated Deployment of Spark Cluster on Bare Metal Cloud

Apache Spark is a widely used distributed computing framework for big data processing. It provides a flexible and scalable solution for processing large amounts of data in a timely manner. However, deploying and managing a Spark cluster can be a challenging task, especially for those who are new to the field of big data.

In recent years, Bare Metal Cloud (BMC) providers have emerged as a promising solution for running distributed systems. BMCs provide the benefits of cloud computing, such as flexible resource allocation, while also providing the performance benefits of dedicated hardware. This article discusses how to automate the deployment of a Spark cluster on a Bare Metal Cloud provider using open-source tools.

What is a Bare Metal Cloud (BMC)?

A Bare Metal Cloud provider offers access to dedicated physical servers that can be used to run virtualized or containerized workloads. BMCs provide the benefits of cloud computing, such as flexible resource allocation and easy scaling, while also providing the performance benefits of dedicated hardware.

BMCs are an excellent choice for running distributed systems, such as Apache Spark, that require high performance and low latency. BMCs can provide consistent performance, which is essential for running big data processing workloads.

Automated Deployment Architecture

Step-by-Step Deployment Process

Step 1: Provision Bare Metal Server Using Terraform

Terraform is an open-source tool that automates the deployment of infrastructure. We can use Terraform to provision a bare metal server on BMC. Terraform allows us to define the server configuration in a declarative way, making it easier to manage infrastructure as code.

resource "bmc_baremetal_server" "spark" {
   hostname = "spark-worker-1"
   plan     = "c2.medium.x86"
   region   = "us-west"
}

In this example, we define a bare metal server with hostname spark-worker-1 in the us-west region. We also specify the server plan, which determines the amount of CPU, RAM, and storage allocated to the server.

Step 2: Install Spark and Dependencies Using Ansible

Once the bare metal server is provisioned, we need to install Spark and its dependencies on the server. Ansible is an open-source tool that can automate configuration management of servers.

- name: Install Java
  apt:
    name: openjdk-8-jdk

- name: Download and extract Spark
  get_url:
    url: https://archive.apache.org/dist/spark/spark-3.2.0/spark-3.2.0-bin-hadoop3.2.tgz
    dest: /opt
    mode: 0755
    validate_certs: no

- name: Create symbolic link for Spark
  file:
    src: /opt/spark-3.2.0-bin-hadoop3.2
    dest: /opt/spark
    state: link

- name: Set environment variables for Spark
  lineinfile:
    path: /etc/environment
    line: "export SPARK_HOME=/opt/spark"

Step 3: Create Spark Cluster Image Using Packer

Packer is an open-source tool that can automate the creation of machine images. We can use Packer to create a machine image that contains Spark and its dependencies pre-installed, saving time when provisioning new Spark nodes.

{
  "builders": [
    {
      "type": "bmc-ssh",
      "ssh_username": "root",
      "ssh_password": "mypassword",
      "ssh_host": "{{ user `bmc_host` }}",
      "bmc_region": "{{ user `bmc_region` }}",
      "bmc_image": "{{ user `bmc_image` }}",
      "bmc_size": "{{ user `bmc_size` }}"
    }
  ],
  "provisioners": [
    {
      "type": "shell",
      "inline": [
        "apt-get update",
        "apt-get install -y openjdk-8-jdk",
        "wget https://archive.apache.org/dist/spark/spark-3.2.0/spark-3.2.0-bin-hadoop3.2.tgz",
        "tar -xzf spark-3.2.0-bin-hadoop3.2.tgz",
        "mv spark-3.2.0-bin-hadoop3.2 /opt/spark",
        "rm spark-3.2.0-bin-hadoop3.2.tgz",
        "echo 'export SPARK_HOME=/opt/spark' >> /etc/environment"
      ]
    }
  ]
}

Step 4: Provision Complete Spark Cluster

Finally, we can use Terraform and Ansible together to provision and configure the complete Spark cluster with one master node and multiple worker nodes.

resource "bmc_baremetal_server" "spark_master" {
  hostname = "spark-master"
  plan     = "c2.medium.x86"
  region   = "us-west"
}

resource "bmc_baremetal_server" "spark_worker" {
  count    = 3
  hostname = "spark-worker-${count.index + 1}"
  plan     = "c2.medium.x86"
  region   = "us-west"
}

module "spark_cluster" {
  source = "github.com/example/spark-cluster"
  
  spark_master_hostname = bmc_baremetal_server.spark_master.hostname
  spark_worker_hostnames = [
    bmc_baremetal_server.spark_worker[0].hostname,
    bmc_baremetal_server.spark_worker[1].hostname,
    bmc_baremetal_server.spark_worker[2].hostname
  ]
}

Key Benefits of Automation

Conclusion

Automating the deployment of a Spark cluster on Bare Metal Cloud using Terraform, Ansible, and Packer significantly reduces deployment time and eliminates human errors. This Infrastructure-as-Code approach provides consistent, scalable, and reproducible Spark cluster deployments while leveraging the high performance benefits of dedicated bare metal hardware.