For an API task that takes a very long time, like processing a large video file, how can the API respond quickly to the client while the task continues in the background?

When an API receives a request for a task that will take a very long time, such as processing a large video file, it must avoid performing that work directly within the immediate request-response cycle to prevent the client from timing out. This is achieved through asynchronous processing. First, the API quickly validates the incoming request. If valid, it generates a unique identifier for the task, often called a Job ID. This Job ID serves as a specific reference for the client to track the progress of its request. The API then places the details of this long-running task, including all necessary input data, into a task queue. A task queue, also known as a message queue, is a temporary storage mechanism that acts as a buffer. It allows one application component, like the API's initial endpoint, to send a message or task without waiting for another component to process it immediately. This decoupling ensures the API remains responsive. Immediately after successfully placing the task in the queue, the API sends an HTTP response back to the client, typically with an HTTP status code like 202 Accepted, which means the request has been accepted for processing but is not yet complete. This immediate response prevents the client from experiencing a long wait. Crucially, this response includes the generated Job ID, which the client will use for subsequent inquiries. In the background, separate, independent processes known as workers (or consumers) continuously monitor the task queue. A worker is an application or server component specifically designed to retrieve tasks from the queue and execute them. When a worker picks up a task, it then performs the actual long-running operation, such as the video file processing. As the task progresses, the worker updates the status of the task, referencing its Job ID, in a persistent storage system, such as a database or a caching layer. Once the worker completes the task, it stores the final result or a reference to it, along with the final status (e.g., "Completed" or "Failed"), associated with the Job ID in the persistent storage. To allow the client to retrieve the result or check the status, several methods are available. One common method is polling, where the client periodically sends new requests to a specific API endpoint, providing the Job ID. The API, upon receiving such a polling request, queries the persistent storage for the task's status and responds with the current state (e.g., "Pending", "Processing", "Completed", "Failed"). If the task is completed, the API also provides the result or a link to it. Polling involves the client repeatedly asking the server for updates. Another method is webhooks, also known as callbacks. In this approach, the client includes a URL in its initial request to the API. Once the worker finishes the long-running task, the API server initiates an outgoing HTTP POST request, which is the webhook, to the client's provided URL. This notification informs the client that the task is complete and can include the result or a link to access it. A webhook is an automated message sent from an application when a specific event occurs, pushing information to a client. For more real-time status updates, technologies like Server-Sent Events (SSE) or WebSockets can be used. With SSE, the client establishes a long-lived HTTP connection with the API. The API can then push a stream of status updates or the final result directly to the client over this single connection as the task progresses, without the client needing to send new requests. WebSockets provide a full-duplex, two-way communication channel between the client and server, allowing both sides to send messages asynchronously and enabling even more interactive real-time updates throughout the task's lifecycle.