Simultaneously running a data parser and a Telegram bot: How can I make them work together?

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1

I’m trying to figure out how to run my data parser and the Telegram bot at the same time. The idea is for the parser to constantly update a shared database while the bot responds to user commands by accessing the same data.

I have a working bot that communicates well with the database, and separately a parser that fills the database with data. My goal is for the parser to start when the bot starts, yet both should run independently, only interacting through the database.

Below is a new, simplified code sample that reflects my current approach:

# Bot code
import asyncio
from aiogram import Bot, Dispatcher

async def run_bot():
    # Setup bot and command handlers
    pass

async def start_parser():
    # Initialize the parser logic
    pass

async def main():
    await asyncio.gather(run_bot(), start_parser())

if __name__ == '__main__':
    asyncio.run(main())

# Parser code
async def parse_data():
    # Process data and update database
    pass

def run_parser():
    asyncio.run(parse_data())

I’ve experimented with several methods, but I can’t seem to run both processes simultaneously and independently. Any suggestions on how to achieve this?

2

Considering your setup, I’d recommend leveraging Python’s threading module. It’s well-suited for I/O-bound tasks like your bot and parser. Here’s a straightforward approach:

import threading

def bot_thread():
    asyncio.run(run_bot())

def parser_thread():
    asyncio.run(parse_data())

if __name__ == '__main__':
    bot = threading.Thread(target=bot_thread)
    parser = threading.Thread(target=parser_thread)
    bot.start()
    parser.start()
    bot.join()
    parser.join()

This method allows both processes to run concurrently while sharing the same Python interpreter, which is more resource-efficient than multiprocessing for your use case. It also maintains the asynchronous nature of your existing code. Remember to implement proper synchronization mechanisms for database access to prevent race conditions.

3

I’ve tackled a similar challenge in one of my projects. The key is to use asyncio effectively. Here’s what worked for me:

Instead of trying to run the parser in a separate function, integrate it directly into your main coroutine. Use asyncio.create_task() to spawn the parser as a background task.

In your main() function, you could do something like this:

async def main():
    parser_task = asyncio.create_task(parse_data())
    bot_task = asyncio.create_task(run_bot())
    await asyncio.gather(parser_task, bot_task)

This approach ensures both the parser and bot run concurrently. The parser will continuously update the database in the background while the bot handles user interactions.

Remember to implement proper error handling and consider using a connection pool for database operations to manage concurrent access efficiently. It took me some trial and error, but this setup has been running smoothly for months now.

4

hav u tried using multiprocessing? it lets u run stuff in parallel. basically u create separate processes for ur bot and parser:

from multiprocessing import Process

def run_bot():
    # bot code here

def run_parser():
    # parser code here

if __name__ == '__main__':
    bot_process = Process(target=run_bot)
    parser_process = Process(target=run_parser)
    bot_process.start()
    parser_process.start()

this way they run independently but can still share data thru the db