1. Queue-based Video Processing Workflows on Azure


    Queue-based video processing is a common pattern for handling video manipulation tasks in a highly scalable way. In Azure, you can leverage various services to build such a workflow. The process typically involves:

    1. Media Services: For encoding, storing, and streaming video content.
    2. Azure Functions: For event-driven processing tasks, such as triggering a process when a new video is uploaded.
    3. Storage Account (Blob Storage): To store raw and processed video files.
    4. Service Bus or Storage Queue: To pass messages that control the flow of video processing between services.

    The following Pulumi program outlines a basic queue-based video processing architecture on Azure. In this scenario, we use Azure Storage Queue for simplicity. When a new video is uploaded to a blob container, a message is placed in a queue. An Azure Function could be triggered by this queue message to start the video processing job using Media Services.

    Let's break it down:

    • We create an Azure Blob Container to store the uploaded videos.
    • A Queue in the same Storage Account will hold the messages for video processing tasks.
    • An Azure Media Service is used to handle the encoding and processing of the videos.
    • Azure Function is not explicitly defined here, but you would typically deploy an Azure Function and configure it to respond to new messages in the queue. This part is omitted for simplicity and because it often involves a more complex setup including the deployment of function code.

    Here's a Pulumi program written in Python that sets up the Azure infrastructure for such a workflow:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('resource-group') # Create an Azure Storage Account storage_account = azure_native.storage.StorageAccount('storageaccount', resource_group_name=resource_group.name, kind="StorageV2", sku=azure_native.storage.SkuArgs( name="Standard_LRS", ) ) # Create an Azure Storage Container for uploading videos video_blob_container = azure_native.storage.BlobContainer('videocontainer', account_name=storage_account.name, resource_group_name=resource_group.name, public_access="None", ) # Create an Azure Storage Queue for video processing tasks video_queue = azure_native.storage.Queue('videoqueue', account_name=storage_account.name, resource_group_name=resource_group.name, ) # Create an Azure Media Services Account to process videos media_service_account = azure_native.media.MediaService('mediaserviceaccount', location=resource_group.location, resource_group_name=resource_group.name, storage_accounts=[ azure_native.media.StorageAccountArgs( id=storage_account.id, type="Primary" ) ] ) # Export the Storage Account name, Blob Container name, and Queue name pulumi.export('storage_account_name', storage_account.name) pulumi.export('video_blob_container_name', video_blob_container.name) pulumi.export('video_queue_name', video_queue.name)

    This program sets up the primary infrastructure components required for a queue-based video processing system on Azure.

    To complete the workflow, you would need to upload your Azure Function code that listens to the queue and starts the video processing jobs using the Media Service APIs. The exact implementation details can vary based on the video processing requirements, such as resizing, format conversion, or other transformations.

    Remember, this code is just the infrastructure setup. The application code that processes videos would typically be deployed as Azure Functions or Container Instances, and this application code would be triggered by the messages from the queue. The Pulumi program does not include the application code or the Azure Function deployment code, which would need to be deployed separately.