Implementing the Polling Endpoint

Oct 13, 2025 by talynt.org 53 views

Introduction

Machine learning (ML) requests can be computationally intensive and may take hours to complete. Traditional fetch requests, however, are not designed to be open for extended periods. The default fetch timeout in Udici/node.js is 5 minutes, which can lead to unstable connections and broken requests. To overcome this limitation, we can implement a polling system to break up the fetch request and ensure that it continues from where it failed. In this article, we will explore the concept of polling and its implementation in conjunction with traditional fetch requests.

Understanding Polling

Polling is a technique used to check the status of a long-running operation at regular intervals. It involves sending a request to a server to check the status of a task, and if the task is not complete, the server returns a response indicating that the task is still in progress. The client then waits for a specified amount of time before sending another request to check the status of the task. This process continues until the task is complete.

Benefits of Polling

Polling offers several benefits, including:

Improved reliability: Polling ensures that the client continues to receive updates on the status of the task, even if the initial request fails.
Reduced latency: Polling allows the client to receive updates on the status of the task at regular intervals, reducing the latency associated with traditional fetch requests.
Increased flexibility: Polling enables the client to choose the frequency of updates, allowing for more flexibility in terms of how often the client receives updates on the status of the task.

Implementing Polling

To implement polling, we need to modify the backend to return a task ID for long-running operations. This task ID can be used to check the status of the task at regular intervals. We can implement a polling endpoint (e.g., /check-status?task_id=...) to receive updates on the status of the task.

Modifying the Backend

To modify the backend to return a task ID for long-running operations, we can follow these steps:

Create a task ID: When a long-running operation is initiated, create a unique task ID that can be used to check the status of the task.
Return the task ID: Return the task ID to the client as part of the initial response.
Create a polling endpoint: Create a polling endpoint (e.g., /check-status?task_id=...) that can be used to check the status of the task.

Implementing the Polling Endpoint

To implement the polling endpoint, we can follow these steps:

Create a route: Create a route for the polling endpoint (e.g., /check-status).
Check the task ID: Check the task ID passed in the request to determine the status of the task.
Return the status: Return the status of the task to the client.

Updating the Frontend

To update the frontend to use polling instead of long fetch requests, we can follow these steps:

Create a polling function: Create a function that sends a request to the polling endpoint at regular intervals.
Check the status: Check the status of the task returned by the polling endpoint.
Update the UI: Update the UI to reflect the status of the task.

Example Use Case

Suppose we have a machine learning model that takes several hours to train. We can use polling to check the status of the training process at regular intervals. Here's an example of how we can implement polling in this scenario:

Create a task ID: When the training process is initiated, create a unique task ID that can be used to check the status of the training process.
Return the task ID: Return the task ID to the client as part of the initial response.
Create a polling endpoint: Create a polling endpoint (e.g., /check-status?task_id=...) that can be used to check the status of the training process.
Create a polling function: Create a function that sends a request to the polling endpoint at regular intervals (e.g., every 5 minutes).
Check the status: Check the status of the training process returned by the polling endpoint.
Update the UI: Update the UI to reflect the status of the training process.

Conclusion

In conclusion, polling is a technique used to check the status of a long-running operation at regular intervals. It offers several benefits, including improved reliability, reduced latency, and increased flexibility. To implement polling, we need to modify the backend to return a task ID for long-running operations and create a polling endpoint to receive updates on the status of the task. We can update the frontend to use polling instead of long fetch requests by creating a polling function that sends a request to the polling endpoint at regular intervals. By implementing polling, we can improve the reliability and flexibility of our machine learning requests.

Ideas for Tasks

Here are some ideas for tasks that can be implemented to improve the polling system:

Modify the backend to return a task ID for long-running operations: Modify the backend to return a task ID for long-running operations, allowing the client to check the status of the task at regular intervals.
Implement a polling endpoint: Implement a polling endpoint (e.g., /check-status?task_id=...) that can be used to check the status of the task.
Update the frontend to use polling instead of long fetch requests: Update the frontend to use polling instead of long fetch requests, allowing the client to receive updates on the status of the task at regular intervals.

Future Work

In the future, we can explore other techniques for improving the reliability and flexibility of our machine learning requests, such as:

Implementing a message queue: Implementing a message queue to handle long-running operations and provide updates on the status of the task.
Using a distributed system: Using a distributed system to handle long-running operations and provide updates on the status of the task.
Implementing a caching layer: Implementing a caching layer to improve the performance of the polling system.
Frequently Asked Questions (FAQs) about Implementing a Polling System for Long-Running Machine Learning Requests ==============================================================================================

Q: What is polling, and how does it work?

A: Polling is a technique used to check the status of a long-running operation at regular intervals. It involves sending a request to a server to check the status of a task, and if the task is not complete, the server returns a response indicating that the task is still in progress. The client then waits for a specified amount of time before sending another request to check the status of the task.

Q: Why do we need polling for long-running machine learning requests?

A: Traditional fetch requests are not designed to be open for extended periods, and the default fetch timeout in Udici/node.js is 5 minutes. This can lead to unstable connections and broken requests. Polling ensures that the client continues to receive updates on the status of the task, even if the initial request fails.

Q: How do we implement polling in our machine learning requests?

A: To implement polling, we need to modify the backend to return a task ID for long-running operations. This task ID can be used to check the status of the task at regular intervals. We can implement a polling endpoint (e.g., /check-status?task_id=...) to receive updates on the status of the task.

Q: What are the benefits of using polling for long-running machine learning requests?

A: Polling offers several benefits, including:

Improved reliability: Polling ensures that the client continues to receive updates on the status of the task, even if the initial request fails.
Reduced latency: Polling allows the client to receive updates on the status of the task at regular intervals, reducing the latency associated with traditional fetch requests.
Increased flexibility: Polling enables the client to choose the frequency of updates, allowing for more flexibility in terms of how often the client receives updates on the status of the task.

Q: How do we update the frontend to use polling instead of long fetch requests?

A: To update the frontend to use polling instead of long fetch requests, we can follow these steps:

Create a polling function: Create a function that sends a request to the polling endpoint at regular intervals.
Check the status: Check the status of the task returned by the polling endpoint.
Update the UI: Update the UI to reflect the status of the task.

Q: What are some common challenges associated with implementing polling for long-running machine learning requests?

A: Some common challenges associated with implementing polling for long-running machine learning requests include:

Overloading the server: If the client sends too many requests to the polling endpoint, it can overload the server and lead to performance issues.
Inconsistent updates: If the client receives inconsistent updates on the status of the task, it can lead to confusion and errors.
Security concerns: If the client is not properly authenticated or authorized, it can lead to security concerns and potential data breaches.

Q: How do we address these challenges and ensure a successful implementation of polling for long-running machine learning requests?

A: To address these challenges and ensure a successful implementation of polling for long-running machine learning requests, we can follow these best practices:

Implement rate limiting: Implement rate limiting to prevent the client from sending too many requests to the polling endpoint.
Use caching: Use caching to improve the performance of the polling system and reduce the load on the server.
Implement authentication and authorization: Implement authentication and authorization to ensure that the client is properly authenticated and authorized before sending requests to the polling endpoint.

Q: What are some future directions for improving the polling system for long-running machine learning requests?

A: Some future directions for improving the polling system for long-running machine learning requests include:

Implementing a message queue: Implementing a message queue to handle long-running operations and provide updates on the status of the task.
Using a distributed system: Using a distributed system to handle long-running operations and provide updates on the status of the task.
Implementing a caching layer: Implementing a caching layer to improve the performance of the polling system.