Automated Deployment of Spark Cluster on Bare Metal Cloud

Introduction

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 field of big data.

In recent years, Bare Metal Cloud (BMC) providers have emerged as a promising solution for running distributed systems. BMCs provide benefits of cloud computing, such as flexible resource allocation, while also providing performance benefits of dedicated hardware. In this article, we will discuss how to automate 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 benefits of cloud computing, such as flexible resource allocation and easy scaling, while also providing 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 of Spark Cluster on BMC

Deploying a Spark cluster on a BMC can be a time-consuming and error-prone task. To simplify this process, we can use open-source tools such as Ansible, Terraform, and Packer to automate deployment process. Here's how we can automate deployment of a Spark cluster on a BMC −

Step 1: Provision Bare Metal Server Using Terraform

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

Here's an example of how to provision a bare metal server using Terraform −

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 us-west region. We also specify server plan, which determines amount of CPU, RAM, and storage allocated to server.

Step 2: Install Spark and Dependencies Using Ansible

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

Here's an example of how to install Spark and its dependencies using Ansible −

- 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"

In this example, we use Ansible to install Java, download and extract Spark, create a symbolic link for Spark, and set environment variables for Spark. This will ensure that Spark and its dependencies are installed and configured correctly on bare metal server.

Step 3: Create Spark Cluster Image Using Packer

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

Here's an example of how to create a Spark cluster image using Packer −

{
   "builders": [
      {
         "type": "bmc-ssh",
         "ssh_username": "root",
         "ssh_password": "mypassword",
         "ssh_host": "{{ user `bmc_host` }}",
         "ssh_port": 22,
         "bmc_user": "{{ user `bmc_user` }}",
         "bmc_password": "{{ user `bmc_password` }}",
         "bmc_project": "{{ user `bmc_project` }}",
         "bmc_instance": "{{ user `bmc_instance` }}",
         "bmc_domain": "{{ user `bmc_domain` }}",
         "bmc_region": "{{ user `bmc_region` }}",
         "bmc_image": "{{ user `bmc_image` }}",
         "bmc_size": "{{ user `bmc_size` }}",
         "bmc_network": "{{ user `bmc_network` }}",
         "bmc_subnet": "{{ user `bmc_subnet` }}"
      }
   ],
  "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"
         ]
      }
   ]
}

In this example, we use Packer to create a BMC machine image that contains Spark and its dependencies pre-installed. We use bmc-ssh builder to connect to bare metal server using SSH and execute provisioning commands. Once image is created, we can use it to provision new Spark nodes in cluster.

Step 4: Provision Spark Cluster Using Terraform and Ansible

Finally, we can use Terraform and Ansible to provision and configure Spark cluster. We can define cluster configuration using Terraform and use Ansible to install Spark and its dependencies on each node in cluster.

Here's an example of how to provision a Spark cluster using Terraform and Ansible −

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
   ]
}

In this example, we define a Spark master node and three Spark worker nodes using Terraform. We use `bmc_baremetal_server` resource to define each node's configuration. We also use `count` parameter to create three worker nodes.

We then use `module` parameter to define Spark cluster configuration using a separate module. `source` parameter points to GitHub repository that contains module's code.

Inside module, we use Ansible to install Spark and its dependencies on each node in cluster. We use `spark_master_hostname` parameter to configure Spark master node, and `spark_worker_hostnames` parameter to configure Spark worker nodes.

Conclusion

In this article, we discussed how to automate deployment of a Spark cluster on a Bare Metal Cloud provider using open-source tools such as Terraform, Ansible, and Packer. We showed how to provision a bare metal server, install Spark and its dependencies, create a Spark cluster image, and provision a Spark cluster using Terraform and Ansible.

Automating deployment of a Spark cluster on a BMC can save time and reduce errors compared to manual deployment. BMCs provide performance benefits of dedicated hardware, making them an excellent choice for running distributed systems such as Spark. With help of open-source tools, anyone can deploy and manage a Spark cluster on a BMC.