Scalable Analytics Workloads for AI with EMR Serverless

Question

Pulumi · Accepted Answer

Amazon EMR Serverless is a big data platform that allows you to run analytics and data processing workloads without managing clusters and servers. EMR Serverless scales resources up and down to meet your workload's demands, making it both cost-effective and simple to use for applications ranging from big data processing, data warehousing, to machine learning.

To create an EMR Serverless Application with Pulumi, you would typically define your application's configuration, including the type of application you're running (like Spark or Hive), your capacity and auto-scaling configurations, networking setup, and any specific job run parameters.

Below is a Pulumi program written in Python that defines an EMR Serverless Application. The program leverages the `aws-native` package as it provides a direct mapping to AWS resources:

```python
import pulumi
import pulumi_aws_native as aws_native

# Create an AWS EMR Serverless Application
emr_serverless_application = aws_native.emrserverless.Application(
    "aiAnalyticsApp",
    type="SPARK",  # The type of application (Spark, Hive, etc.)
    release_label="emr-6.5.0.0",  # The EMR release label
    initial_capacity=[
        # Define the initial capacity requirements with worker count and configurations
        {
            "key": "default",
            "value": {
                "worker_count": 2,  # Number of workers to start with
                "worker_configuration": {
                    "cpu": "4 vCPU",  # CPU configuration for each worker
                    "memory": "16 GB",  # Memory configuration for each worker
                },
            },
        },
    ],
    maximum_capacity={
        "cpu": "32 vCPU",      # Maximum CPU capacity to scale out to
        "memory": "128 GB",    # Maximum memory to scale out to
    },
    network_configuration={
        "subnetIds": ["subnet-0123456789abcdef0"],  # Specify the subnet IDs for network configuration
        "securityGroupIds": ["sg-0123456789abcdef0"],  # Specify the security group IDs
    },
    auto_stop_configuration={
        "enabled": True,        # Enable auto-stop configuration
        "idle_timeout_minutes": 15,  # Auto-stop the application after 15 minutes of inactivity
    }
)

# Export the EMR Serverless Application ID
pulumi.export("emr_serverless_application_id", emr_serverless_application.application_id)
```

In this program, we define a serverless application with a specific configuration tailored towards Spark workloads. We set initial and maximum capacity limits, network configuration, and auto-stop behavior to ensure the application stops automatically when not in use to save costs.

The `initial_capacity` parameter specifies the number and configuration of the workers that will be started with the application. You can specify multiple initial capacity entries for different types of workers.

The `maximum_capacity` parameter defines the absolute maximum resources your application can use when autoscaling.

The `network_configuration` provides network settings like subnet and security group that the application will use to ensure it resides within your VPC and adheres to your security requirements.

Finally, `auto_stop_configuration` enables and configures the auto-stop feature of the serverless application, which can help reduce costs by stopping the application after a certain period of idle time.

Once deployed, Pulumi exports the ID of the created EMR Serverless Application, which you can use to reference in other parts of your infrastructure or when setting up jobs and step functions for your data processing tasks.

For each of these properties, check the official [AWS Pulumi documentation](https://www.pulumi.com/registry/packages/aws-native/api-docs/emrserverless/application/) to understand the details of the configurations available to you when deploying EMR Serverless Applications using Pulumi.