Parallel Data Processing using VM Scale Sets

Question

Pulumi · Accepted Answer

In cloud computing, parallel data processing is a method used to quickly process vast quantities of data by running parallel computations in distributed environments. Azure Virtual Machine Scale Sets (VMSS) is one of the services that can be used to create and manage a group of load-balanced VMs for parallel processing tasks.

A VM Scale Set allows you to deploy and manage a set of identical, auto-scaling virtual machines. With VMSS, you can build large-scale services targeting big compute, big data, and containerized workloads – all with the benefits of automatic scaling when demand changes.

Below is an example of how you can use Pulumi to create a Virtual Machine Scale Set in Azure for parallel data processing. This program defines a scale set with a basic configuration, which you can customize depending on your specific workload requirements:

- It specifies the resource group to contain the VM Scale Set.
- It defines the VM Scale Set with a single VM instance as a starting point, which will automatically increase based on policies you define (e.g., CPU usage).
- It sets up a basic network profile so that the VM instances can communicate with each other and the outside world.
- It also defines an OS profile and storage profile which are required to create the VMs in the scale set.

The specific details like the size of the VMs, the type of OS image used, and number of instances in the scaling policies will depend on your workload needs and will need to be configured accordingly.

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup("resourceGroup")

# Create a Virtual Network
net = azure_native.network.VirtualNetwork(
    "serverNetwork",
    resource_group_name=resource_group.name,
    address_space=azure_native.network.AddressSpaceArgs(
        address_prefixes=["10.0.0.0/16"],
    ),
    subnets=[azure_native.network.SubnetArgs(
        name="default",
        address_prefix="10.0.1.0/24",
    )],
)

# Create a Subnet
subnet = azure_native.network.Subnet(
    "serverSubnet",
    resource_group_name=resource_group.name,
    virtual_network_name=net.name,
    address_prefix="10.0.2.0/24",
)

# Create a Network Interface for VMSS
network_interface = azure_native.network.NetworkInterface(
    "serverNetworkInterface",
    resource_group_name=resource_group.name,
    ip_configurations=[azure_native.network.NetworkInterfaceIPConfigurationArgs(
        name="WebServerIPConfig",
        subnet=azure_native.network.SubnetArgs(id=subnet.id),
    )],
)

# Create a Load Balancer for VMSS
load_balancer = azure_native.network.LoadBalancer(
    "serverLoadBalancer",
    resource_group_name=resource_group.name,
    frontend_ip_configurations=[azure_native.network.FrontendIPConfigurationArgs(
        name="LoadBalancerFrontEnd",
        subnet=azure_native.network.SubnetArgs(id=subnet.id),
    )],
)

# Create VM Scale Set
vmss = azure_native.compute.VirtualMachineScaleSet(
    "serverVMScaleSet",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=azure_native.compute.SkuArgs(
        capacity=1,
        name="Standard_D1_v2",
    ),
    overprovision=True,
    upgrade_policy=azure_native.compute.UpgradePolicyArgs(
        mode="Automatic",
    ),
    virtual_machine_profile=azure_native.compute.VirtualMachineProfileArgs(
        network_profile=azure_native.compute.NetworkProfileArgs(
            network_interface_configurations=[
                azure_native.compute.VirtualMachineScaleSetNetworkConfigurationArgs(
                    name="networkInterfaceConfiguration",
                    primary=True,
                    ip_configurations=[
                        azure_native.compute.VirtualMachineScaleSetIPConfigurationArgs(
                            name="ipConfiguration",
                            subnet=azure_native.compute.ApiEntityReferenceArgs(
                                id=subnet.id,
                            ),
                            load_balancer_backend_address_pools=[
                                azure_native.compute.SubResourceArgs(
                                    id=load_balancer.id.apply(
                                        lambda id: f"{id}/backendAddressPools/serverLoadBalancerBackEnd"
                                    ),
                                )
                            ],
                        )
                    ],
                )
            ]
        ),
        os_profile=azure_native.compute.OSProfileArgs(
            computer_name_prefix="vmss",
            admin_username="adminuser",
            admin_password="Password1234!",
        ),
        storage_profile=azure_native.compute.StorageProfileArgs(
            image_reference=azure_native.compute.ImageReferenceArgs(
                publisher="Canonical",
                offer="UbuntuServer",
                sku="16.04-LTS",
                version="latest",
            )
        ),
    ),
)

pulumi.export("vmss_name", vmss.name)
```

Remember that creating and managing virtual machine scale sets also brings elements like cost, security, and maintenance into consideration. These are configured outside the scope of this script but are crucial for production deployments.

You can tweak the parameters like `sku.name` (which specifies the size of VM), the `capacity` (which specifies the initial instance count), and other configurations as per your processing requirements. The OS disk image referenced here is Ubuntu Server 16.04 LTS, which you can change to any other available OS image.

The code also exposes the name of the Virtual Machine Scale Set as an exported output, which is useful if you need to get the VMSS name to use in further operations or outputs.

Once you have your Pulumi program ready, run it using the Pulumi CLI as follows:

1. Install Pulumi if you haven't already done so.
2. Log in to the Pulumi service or use the local login option.
3. Configure your Azure credentials using the Azure CLI or by setting the appropriate environment variables.
4. Save the above code in a file called `__main__.py`.
5. From your terminal, navigate to the directory containing the file.
6. Run `pulumi up` to execute the script and create the resources.

This will start the provisioning process, and you will be prompted to review the changes before they are applied. If everything looks correct, confirm the changes to start the deployment on Azure.