The problem arises when the database crashes for a period of time. If the worker has already pulled a message from the queue, it disappears unless handled properly.
Proposed Solution: 1. Use ACK Mechanism Properly • If the database is available, insert the data and send an ACK to remove the message from the queue. • If the database is down, send a NO_ACK so that RabbitMQ requeues the message for another attempt. 2. Prevent Infinite Retry Loops • If we simply send NO_ACK, the message will immediately return to the queue, leading to a tight retry loop that overloads the system. • To solve this, implement a progressive retry delay: • The worker should sleep for a few seconds before retrying the same task: • 1s → 3s → 5s → 30s → 60s (capped at a max delay). 3. Limit Retry Attempts • Introduce a retry counter (e.g., 5 attempts). • After 5 failures, move the message to a Dead Letter Queue (DLQ) instead of retrying indefinitely.
Alternative Approach:
Instead of relying on RabbitMQ’s NO_ACK retry cycle, an alternative would be to keep the message in memory and attempt 5 internal retries within the worker itself: 1. The worker tries to insert the data into the DB. 2. If the DB is down, it retries up to 5 times, with a sleep interval between attempts. 3. If all retries fail, move the message to the DLQ.
Questions: • Which approach is preferable? Should I rely on RabbitMQ to handle retries, or should I manage them within the worker itself? • Are there better practices for handling failures in a high-scale distributed system with RabbitMQ and a database backend?