Spaces Bucket Lifecycle Policies for AI Dataset Management

Question

Pulumi · Accepted Answer

In order to manage AI datasets effectively, applying lifecycle policies to your storage buckets can be quite beneficial. These policies help to transition older or infrequently accessed data to cheaper storage classes or automatically delete data that's no longer needed. This can lead to significant cost savings and improved data management practices.

Pulumi allows you to manage cloud resources with programming languages like Python. For managing a Spaces bucket (which is DigitalOcean's object storage service, similar to AWS S3), Pulumi doesn't have a dedicated DigitalOcean Spaces provider. However, since Spaces is S3-compatible, we can use the pulumi_aws provider to set up lifecycle policies for Spaces.

Below is a Pulumi Python program that creates an S3 bucket with lifecycle rules applied. In the context of DigitalOcean Spaces, you would need to configure the pulumi_aws provider to point to DigitalOcean's Spaces endpoint.

I'll walk you through the lifecycle policy setup. Here's how you might set it up using pulumi_aws:

Bucket Creation: Create a new S3 bucket which in the context of DigitalOcean Spaces corresponds to creating a new Space.
Lifecycle Policy: Define rules to manage the lifecycle of objects in the bucket. These rules can specify how objects are transitioned between storage classes or when they are deleted.

Let's write the Pulumi program.

import pulumi
import pulumi_aws as aws

# Configure Pulumi to use DigitalOcean Spaces by setting the appropriate endpoint.
# Replace <REGION> with your DigitalOcean Spaces region, for example 'nyc3'.
aws.config.region = "us-east-1"  # Spaces uses this region for AWS compatibility.
aws.config.s3 = {
    "endpoint": "https://<REGION>.digitaloceanspaces.com"
}

# Create a new private bucket (Space).
bucket = aws.s3.Bucket('ai-dataset-bucket',
    acl='private')

# Apply the lifecycle policy to the bucket.
lifecycle_policy = aws.s3.BucketLifecycleConfiguration('ai-dataset-lifecycle-policy',
    bucket=bucket.id,
    rules=[aws.s3.BucketLifecycleConfigurationRuleArgs(
        id="expire-old-versions",
        enabled=True,
        noncurrent_version_expiration=aws.s3.BucketLifecycleConfigurationRuleNoncurrentVersionExpirationArgs(
            noncurrent_days=30
        ),
        abort_incomplete_multipart_upload=aws.s3.BucketLifecycleConfigurationRuleAbortIncompleteMultipartUploadArgs(
            days_after_initiation=7
        ),
    )])

# Export the bucket name
pulumi.export('bucket_name', bucket.id)

In the program above:

We first configure Pulumi to use the S3-compatible API endpoint provided by DigitalOcean Spaces.
A new S3 bucket is created, which is private, similar to creating a new Space.
A lifecycle configuration is applied, where old versions of objects are set to expire after 30 days. Additionally, incomplete multipart uploads are aborted after 7 days. These settings help manage storage costs for datasets that may frequently change.

Make sure to replace "<REGION>" with your DigitalOcean Spaces region, like "nyc3".

Run this Pulumi program to create an S3 bucket with the desired lifecycle policies on DigitalOcean Spaces. Remember that you will need to have the Pulumi CLI installed and configured with the appropriate access keys that DigitalOcean provides for your Spaces access.

This example demonstrates how you can effectively utilize cloud services to handle large datasets typically required for AI workloads, in a cost-effective and scalable manner.