How to Resume API Calls in Python After Token Expiration

Discover a reliable way to handle token expiration when iterating over JSON objects in Python, ensuring seamless API calls that can resume where they left off. --- This video is based on the question https://stackoverflow.com/q/78005523/ asked by the user 'Saksham Srivastava' ( https://stackoverflow.com/u/13127635/ ) and on the answer https://stackoverflow.com/a/78005926/ provided by the user 'Hai Vu' ( https://stackoverflow.com/u/459745/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, comments, revision history etc. For example, the original title of the Question was: Resume the loop while Iterating Json object from the specific row where it left off after token expired Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/licensing The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Handling Token Expiration in API Calls with Python When working with APIs, especially in scenarios involving multiple clients, developers often face the frustrating issue of token expiration. The question at hand is: How can we resume iterating over a JSON object from the precise point where the iteration stopped due to a token expiration? This is a common problem that can lead to increased processing time and potentially missed data. But don't worry; there is a systematic approach to tackle this challenge. Understanding the Problem In this case, you're iterating over a JSON object containing client information, calling an API with each client ID. The problem arises when the token used for authentication expires during the iteration, which forces you to start over from the beginning. This inefficiency can be mitigated by keeping track of where the previous iteration stopped, allowing you to resume from the last successful API call. Let’s break down a structured solution to effectively manage this problem. Proposed Solution Steps to Implement a Resume Functionality Load the JSON Data: Read the client JSON data from the file. Check for a Last-Failed File: Determine if a record exists that indicates where the last session stopped. If it doesn't exist, proceed from the start. Resume from Last Known Client ID: If a last-failed file exists, identify the last processed client ID and continue the iteration from that point. Make API Calls: Iterate through the list of clients and perform API calls. Handle Token Expiration: If a token expiration occurs, save the current client ID to the last-failed file and break the loop. Delete Last-Failed Record Upon Success: If the iteration completes successfully without re-enabling token expiration, remove the last-failed file to reset the state. Implementation Example Here’s a streamlined version of the code that incorporates the above logic: [[See Video to Reveal this Text or Code Snippet]] Key Components of the Code Yielding Clients: The get_clients() function efficiently generates a list of clients to iterate over, enhancing performance by using generators. Client Resumption: The get_clients_starting_from() function ensures that the program only processes clients starting from the last failed one. Simulated Token Expiration: The call_api() function represents API interaction with a randomized token expiration to simulate real-world interactions. Clean Up: The last-failed file is removed once the process completes without issues, helping maintain a clean state for future runs. Conclusion By implementing this strategy, you can efficiently manage token expirations during API calls, allowing your scripts to resume processing from where they left off rather than restarting. This not only saves time but also ensures that you do not miss out on valuable data. With the example above, you'll be better equipped to tackle similar challenges in your future projects. Remember, handling failures gracefully is key to improving the robustness of your applications.