SSL-Powered Secure Communication for GCP DataProc Clusters

Question

Pulumi · Accepted Answer

To achieve SSL-powered secure communication for GCP DataProc Clusters, we'll need to set up a DataProc Cluster with proper security configurations. On Google Cloud Platform (GCP), DataProc Clusters can be configured with SSL/TLS to secure data in transit. This is a common requirement for ensuring that sensitive data processed by the cluster is encrypted as it moves between nodes and services.

The security configuration in DataProc includes options for encryption, but for SSL/TLS specifically, you'd typically handle this at the level of individual applications running on the cluster, or through SSL/TLS certificates on load balancers or other networking services that interact with the cluster. Pulumi allows us to codify these infrastructure requirements using declarative code.

Below is a Pulumi program written in Python that sets up a DataProc Cluster with security configurations. In this example, we'll cover the essential parts of the DataProc Cluster configuration, including enabling HTTP port access and providing the Kerberos configuration, which can be used in conjunction with SSL/TLS for secure communication. This basic cluster can then be augmented with additional configurations for your specific SSL/TLS requirements.

Please note that actual certificate management and distribution are not covered in this example, but this code will get you started on the right path with secure communication needs.

Here's how the program is structured:

1. **Import Pulumi Packages**: Import required Pulumi GCP package to interact with the GCP resources.

2. **Cluster Security Configuration**: Define a security configuration for the DataProc cluster, including the Kerberos configuration that can increase your cluster's security alongside SSL/TLS.

3. **DataProc Cluster Creation**: Define and create a new DataProc cluster with the security configuration applied.

4. **Export Cluster Information**: At the end of the Pulumi program, we export the DataProc cluster's endpoint, which can be used to interact with the cluster securely.

Let's go through the Pulumi program:

```python
import pulumi
import pulumi_gcp as gcp

# Define the project and region for our resources
project = 'your-gcp-project'
region = 'your-region'

# Cluster configuration
cluster_config = {
    "region": region,
    "project": project,
    "cluster_config": {
        "gce_cluster_config": {
            "tags": ["allow-tls"],
            "metadata": {
                "enable-os-login": "true"
            },
            "service_account_scopes": [
                "https://www.googleapis.com/auth/cloud-platform"
            ],
        },
        "master_config": {
            "num_instances": 1,
            "instance_names": [],
            "machine_type": "n1-standard-4",
            "disk_config": {
                "boot_disk_size_gb": 500
            }
        },
        "worker_config": {
            "num_instances": 2,
            "instance_names": [],
            "machine_type": "n1-standard-4",
            "disk_config": {
                "boot_disk_size_gb": 500
            }
        },
        "security_config": {
            "kerberos_config": {
                "enable_kerberos": True,
                "root_principal_password_uri": "gs://your-kerberos-secret-bucket/root-password.encrypted",
                "kms_key_uri": "projects/your-gcp-project/locations/global/keyRings/your-kr/cryptoKeys/your-kms",
                "keystore_uri": "gs://your-kerberos-secret-bucket/keystore.jks",
                "truststore_uri": "gs://your-kerberos-secret-bucket/truststore.jks"
            }
        },
        "endpoint_config": {
            "enable_http_port_access": True
        },
        "software_config": {
            "image_version": "1.5-debian10",
        }
    }
}

# Creating a Dataproc cluster
cluster = gcp.dataproc.Cluster("secure-dataproc-cluster",
    project=cluster_config["project"],
    region=cluster_config["region"],
    cluster_config=cluster_config["cluster_config"])

# Exporting the Dataproc cluster endpoint
pulumi.export('dataproc_cluster_endpoint', cluster.endpoint)
```

**Explanation:**

- We specify the GCP project and region we are working in.
- A security configuration through Kerberos is provided for the DataProc cluster (you will need to replace placeholders with your actual KMS keys and GS bucket URIs).
- The master and worker node configurations are set, including the number of instances required and the boot disk size.
- We enable the HTTP port, which is needed for web interfaces of different applications to be accessible over the network.
- In the exported output, `dataproc_cluster_endpoint` will contain the endpoint for the created DataProc cluster.

Make sure to replace `your-gcp-project`, `your-region`, `gs://your-kerberos-secret-bucket/`, and other placeholders with actual values specific to your GCP configuration.

This program creates just a basic configuration for DataProc. Depending on your needs, you may require additional configuration related to networking, IAM roles, logging, monitoring, etc. For SSL/TLS specifically, you would typically handle certificate management outside the DataProc resource definition, and policies or configurations would be applied to the clusters depending on how you have set up your network and access policies on GCP.