How can I elegantly manage concurrent user queries for my Python Telegram bot without omitting pending responses, given that sequential update codes might mark previous requests as handled?
I have worked on a similar project and found that leveraging Python’s asynchronous capabilities really helped me manage concurrent queries. I ran into issues where tasks would be dropped when multiple updates occurred simultaneously. The breakthrough came when I started queuing each request and then processing them asynchronously with tasks. This ensured that no update was overlooked while allowing the bot to be responsive. It took some trial and error to configure the queue and task handling appropriately but eventually, the structure provided both reliability and efficiency in handling multiple overlapping queries.
I encountered similar challenges when building a Python Telegram bot where overlapping queries posed a significant challenge. My solution was to separate concerns by designing a dedicated processing module that handled each incoming request independently using async functions. This approach ensured that every update was tracked until its respective response was finalized. I also introduced a persistent state mechanism that stored pending and active queries to prevent any loss of data. This method, although requiring some initial setup and testing, ultimately improved the bot’s reliability and responsiveness.
i solved it by creating a small dispatcher using asyncio that holds tasks until they’re processed - a bit hacky but works well in preventing dropped updates despite overlapping queries
Working on a similar project, I found that establishing an intermediary layer between the incoming queries and their processing function is critical. I implemented a dedicated runner that cycles continuously to poll for new requests, enabling each to have a distinct handling routine. This approach allowed me to manage overlaps more gracefully, as it distributed the processing load evenly over time while keeping state details intact. Tweaking the timing settings was the tricky part, but in the end, it significantly improved how the bot responded under heavy load.