1. Asynchronous Task Queue for AI Data Processing with RabbitMQ


    To build an asynchronous task queue for AI data processing with RabbitMQ, you would typically use the following Pulumi resources:

    1. rabbitmq.Exchange: Defines an exchange that receives messages and pushes them to queues. The exchange type determines how it routes messages to queues based on a routing key.
    2. rabbitmq.Queue: Defines a queue that holds messages until they are processed.
    3. rabbitmq.Binding: Connects exchanges to queues, which allows messages to flow from producers to the messaging queues.

    Here is how to create each of those resources in Pulumi using Python:

    • rabbitmq.Exchange - This resource creates a new message exchange, which can be configured to handle messages in various ways (direct, topic, headers, fanout, etc.), depending on the type you specify.
    • rabbitmq.Queue - Represents a queue where you can send messages that workers can process asynchronously.
    • rabbitmq.Binding - Represents the relationship between an exchange and a queue, with an optional routing key or pattern that selects certain messages for the queue.

    Below, I'll provide a Pulumi program that sets up a simple RabbitMQ task queue for AI data processing.

    import pulumi import pulumi_rabbitmq as rabbitmq # Create a RabbitMQ virtual host vhost = rabbitmq.VHost("ai-processing-vhost") # Create a RabbitMQ user for your application user = rabbitmq.User("ai-processing-user", tags=["management"], password="password") # In a real scenario, use Pulumi config to securely manage passwords # Set permissions for the RabbitMQ user permissions = rabbitmq.Permissions("ai-processing-permissions", user=user.name, vhost=vhost.name, permissions={ "configure": ".*", "write": ".*", "read": ".*" }) # Create an exchange where producers will publish messages exchange = rabbitmq.Exchange("ai-data-exchange", name="ai-data-exchange", vhost=vhost.name, settings={ "type": "direct", # Direct type sends messages to the queue with the same routing key as message "durable": True }) # Create a queue for AI data processing tasks queue = rabbitmq.Queue("ai-data-queue", name="ai-data-queue", vhost=vhost.name, settings={ "durable": True }) # Bind the queue to the exchange binding = rabbitmq.Binding("ai-data-binding", source=exchange.name, destination=queue.name, vhost=vhost.name, destination_type="queue", routing_key="ai-data-key") # Export the exchange and queue name pulumi.export('exchange_name', exchange.name) pulumi.export('queue_name', queue.name)

    In this Pulumi program, we set up one exchange and one queue. The exchange is of type "direct", meaning it will route messages with a specific routing key directly to queues bound with the same routing key. The queue and exchange are bound with a routing key "ai-data-key".

    The pulumi.export lines at the end output the names of the created resources, which you can use to interact with the queue in your AI data processing application.

    The resources above are created on an existing RabbitMQ server. If you need to set up a RabbitMQ server as well, you would need additional resources such as cloud instances or containers, along with RabbitMQ installation and configuration steps, which are beyond the scope of this Pulumi example.

    Make sure to replace placeholder values like "password" with secure values, possibly using Pulumi secrets management, before actually deploying this code.