Automated ML Data Archive with S3 Lifecycle Policies

Question

Pulumi · Accepted Answer

To build an automated Machine Learning (ML) data archive with S3 Lifecycle Policies, we will use Amazon S3 to store the ML data and apply lifecycle rules to manage the data over time.

Amazon S3 (Simple Storage Service) is a scalable object storage service offered by AWS, which is very suitable for storing vast amounts of data such as ML datasets. S3 Lifecycle Policies allow you to automatically move objects between different storage classes or archive/delete them after a certain period or under specific conditions.

Here's how you would use Pulumi to create an Amazon S3 Bucket and apply a Lifecycle Policy to it:

- **S3 Bucket**: This will be the primary storage location for your ML data.
- **Bucket Lifecycle Configuration**: This specifies the lifecycle rules for objects in the bucket. For example, objects could transition to a cheaper storage class after a period of inactivity or be deleted after they're no longer needed.

Let's create a program that sets up an S3 bucket with a Lifecycle Policy to transition objects to Glacier after 90 days (where storage is cheaper, but data retrieval is slower) and delete them after one year, which is a common pattern for data that's accessed infrequently but should be retained for a period of time before deletion for compliance or historical analysis.

Here's the Pulumi Python program to accomplish this:

```python
import pulumi
import pulumi_aws as aws

# Create an AWS S3 bucket.
ml_data_bucket = aws.s3.Bucket("mlDataBucket",
    # Bucket name is autogenerated to ensure uniqueness across AWS.
    # Note: Bucket names must be unique within an AWS region.
)

# Define S3 Lifecycle Policy for the ML data bucket.
bucket_lifecycle = aws.s3.BucketLifecycleConfiguration("mlDataBucketLifecycleConfiguration",
    bucket=ml_data_bucket.id,  # Associating lifecycle configuration with our bucket.
    rules=[aws.s3.BucketLifecycleConfigurationRuleArgs(
        id="log",
        enabled=True,
        filter=aws.s3.BucketLifecycleConfigurationRuleFilterArgs(
            prefix="log/",  # Applying the rule to the 'log/' prefix.
        ),
        transitions=[
            aws.s3.BucketLifecycleConfigurationRuleTransitionArgs(
                # Transition to Glacier after 90 days.
                days=90,
                storage_class="GLACIER",
            ),
        ],
        expiration=aws.s3.BucketLifecycleConfigurationRuleExpirationArgs(
            # Delete the object after 365 days.
            days=365,
        ),
    )]
)

# Export the name of the bucket.
pulumi.export("bucket_name", ml_data_bucket.id)

# Export the bucket endpoint.
pulumi.export("bucket_endpoint", pulumi.Output.concat("https://", ml_data_bucket.website_endpoint))

```

In the above program, we created a new S3 bucket and associated a lifecycle policy with it. The lifecycle policy contains one rule with a prefix filter set to "log/", meaning it will only apply to objects within the "log/" directory of the bucket. This rule sets up two stages for the objects: first, after 90 days, they transition to the Glacier storage class, which is cost-effective for infrequently accessed data. Then, after 365 days, the objects are deleted.

The `pulumi.export` lines at the end output the name of the bucket and its endpoint after the Pulumi program is successfully run. This information can be critical for accessing the bucket and checking the objects later on.