Serverless Video Stream Archival for AI Training on AWS S3 via MediaPackage

Question

Pulumi · Accepted Answer

To create a serverless video stream archival system for AI training on AWS, we will use several AWS services including Amazon S3 for storage, and AWS MediaPackage for video processing and packaging. Here's how each component will work together to achieve your goal:

1. **Amazon S3 (Simple Storage Service)**: This is a scalable storage service on AWS. We'll use an S3 bucket to store the video streams that you're archiving. This is the final destination for your content where it can be kept for AI training.

2. **AWS Elemental MediaPackage**: This is a video processing service that allows you to reliably prepare and protect your video for delivery over the Internet. It will ingest the video streams, package them, and encrypt them if necessary, and then deliver the streams to Amazon S3 for storage.

Below is a Pulumi program written in Python that demonstrates how to set up the infrastructure for a serverless video streaming archival system on AWS:

```python
import pulumi
import pulumi_aws as aws
import pulumi_aws_native as aws_native

# Create an S3 bucket for storing the archived video streams
archival_bucket = aws_native.s3.Bucket("ArchiveBucket",
    bucket_name="video-stream-archive-for-ai-training")

# Create a MediaPackage Channel
# This channel will ingest your live video content for processing and packaging.
media_channel = aws_native.mediapackage.Channel("MediaPackageChannel",
    description="Channel for AI Training Video Stream")

# Define an OriginEndpoint for the channel
# The Origin Endpoint specifies how the packaged content is delivered.
origin_endpoint = aws_native.mediapackage.OriginEndpoint("OriginEndpoint",
    channel_id=media_channel.channel_id,
    description="Endpoint for AI Training Video Stream",
    manifest_name="index",
    startover_window_seconds=86400,  # Allows viewers to start the stream from up to 24 hours ago
    time_delay_seconds=60,  # Specify time delay for live streaming
    mss_package=aws_native.mediapackage.OriginEndpointMssPackageArgs(
        manifest_window_seconds=60,
    ),
    hls_package=aws_native.mediapackage.OriginEndpointHlsPackageArgs(
        segment_duration_seconds=6,
    ))

# Assume an IAM role that grants MediaPackage permissions to write to the S3 bucket
archival_role = aws.iam.Role("MediaPackageArchivalRole",
    name="MediaPackageArchivalRole",
    assume_role_policy=json.dumps({
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "mediapackage.amazonaws.com"
            },
        }]
    }))

# Attach S3 policy to the role allowing it to put objects in the bucket
s3_policy = aws.iam.Policy("MediaPackageS3Policy",
    name="MediaPackageS3Policy",
    policy=pulumi.Output.all(archival_bucket.bucket_name).apply(lambda bucket_name: json.dumps({
        "Version": "2012-10-17",
        "Statement": [{
            "Effect": "Allow",
            "Action": "s3:PutObject",
            "Resource": f"arn:aws:s3:::{bucket_name}/*"
        }]
    })))

# Attach the S3 policy to the IAM role
s3_role_attachment = aws.iam.RolePolicyAttachment("MediaPackageS3RoleAttachment",
    role=archival_role.name,
    policy_arn=s3_policy.arn)

# Export relevant resources
pulumi.export("archival_bucket_name", archival_bucket.bucket_name)
pulumi.export("media_package_channel_id", media_channel.channel_id)
pulumi.export("origin_endpoint_url", origin_endpoint.url)
```

In this program, we start by creating an S3 bucket which will store the video streams. Then we define a MediaPackage Channel to ingest your live content. After that, we configure an OriginEndpoint that specifies how this content gets packaged and delivered.

We create an IAM role and a policy that allows AWS MediaPackage to write the packaged content to the specified S3 bucket. We attach this policy to the role, which we will then associate with our MediaPackage channel.

By exporting the names and URLs at the end of the program, we can use these identifiers to manage our infrastructure or integrate with other systems.

Once you run this Pulumi program, these resources will be deployed in your AWS account, and you'll be ready to start streaming and archiving video content for your AI training purposes.