I'm building a multi-service app and need reliable task delivery

For reliable task delivery in a multi-service app, the safest pattern is usually durable queue + worker + idempotent handlers.

Recommended approach

1. Put tasks on a persistent queue

   • Examples: RabbitMQ, Kafka, SQS, Redis Streams, NATS JetStream.
   • Avoid in-memory queues if you need reliability.

2. Use at-least-once delivery

   • Assume a task may run more than once.
   • This is the most practical reliability model.

3. Make workers idempotent

   • Store a unique task/job ID.
   • Before applying changes, check whether that task was already processed.
   • This prevents duplicate side effects.

4. Ack only after successful processing

   • If the worker crashes before ack, the broker can redeliver.
   • Combine this with idempotency to stay safe.

5. Add retries with backoff

   • Retry transient failures automatically.
   • Use exponential backoff with a max retry count.

6. Use a dead-letter queue

   • Failed tasks that exceed retries go to a DLQ for inspection/replay.

7. Persist task state

   • Track states like: queued -> processing -> succeeded/failed.
   • Helps with visibility, debugging, and recovery.

8. Handle timeouts and leases

   • If a worker disappears, the task should become available again.
   • Prevent tasks from being lost forever.

If you need stronger guarantees

• Exactly-once is very hard in distributed systems.
• Usually you get there by combining:
  • durable queue
  • idempotent consumers
  • deduplication keys
  • transactional outbox/inbox patterns

Common pattern: Outbox

If one service updates its database and then needs to send a task/event:

• write the business data and an “outbox” record in the same DB transaction
• a relay publishes from the outbox to the queue
• prevents “DB updated but message not sent” failures

Good default architecture

• Service DB
• Transactional outbox
• Message broker
• Workers
• Retry + DLQ
• Idempotent processing

If you want, I can recommend a specific stack and architecture based on your language/runtime, expected throughput, and whether tasks are short-lived jobs or event-driven messages.