While using a Python Telegram bot with message queuing, rapid replies trigger flood control errors. My custom code below simulates handling delays. How can I prevent bans?
import time
def dispatch_text(message):
try:
print(message) # simulate sending message
except Exception as exc:
if 'Flood control' in str(exc):
delay = 5
time.sleep(delay)
Over the years I have experimented with several approaches and found that a simple exponential backoff strategy can work wonders when dealing with flood control issues. In my setup, I combine this backoff with real-time traffic monitoring, so that whenever a flood error is encountered, the delay grows progressively until message delivery stabilizes. This approach has the advantage of automatically adjusting during periods of high activity without the need for fixed sleep intervals or complex queuing logic. Testing revealed that this method allowed my bot to recover quickly from throttle events and maintain smooth communication overall.
I encountered a similar challenge with my bot where messages came in too quickly and the bot ended up getting throttled. My solution was not to rely solely on exception catching and sleep delays but rather to implement a more controlled scheduling mechanism. I integrated an asynchronous queue to manage outgoing messages and ensured that each message was sent after an appropriate interval. The key was to pre-calculate safe intervals between messages and update them dynamically based on feedback from the server, which significantly reduced the incidence of flood control errors.
In my experience, relying purely on exception handling to manage flood control issues can be insufficient. Instead, I opted for a rate-limiting solution that employs an adaptive delay based on real-time feedback from Telegram. By tracking the time of each sent message and using a dynamic sleep interval, the bot adjusted its sending frequency appropriately. This method allowed for a smoother handling of bursts without triggering flood control bans. Additionally, integrating this mechanism with an asynchronous messaging queue helped in managing the flow without accumulating excessive latency during slower periods.
hey, i fixed a similar issue by using a token bucket algorim to control msg flow, rather than relying on sleep delays. it kinda smooths out the sending rate and prevents bursts. might be worth a try for your bot.