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Think about this for a second. Kafka offsets are a thing, consumer groups are a thing. It's trivial to ensure that only one message is delivered to only one con

Think about this for a second. Kafka offsets are a thing, consumer groups are a thing. It's trivial to ensure that only one message is delivered to only one consumer if that's what you want. Consumer groups track their offset and then commit the offset, the message stays in Kafka but it won't be read again. This IMO is better behaviour than RabbitMQ since you can always re-read messages once they have been processed, whereas generally with MQ systems the message is then marked for deletion and asynchronously deleted.

throwwgisgreat 1y ago
> better behaviour than RabbitMQ since you can always re-read messages once they have been processed I can imagine, a 1 Billion dollar transaction accidentally gets processed by ten thousand client nodes due to a client app synchronization bug, company rethinks its dumb data dumper server strategy...news at 11.
otterley 1y ago
> It's trivial to ensure that only one message is delivered to only one consumer Exactly-once delivery is one of the hardest distributed systems problems. If you’ve “trivially” solved it, please show us your solution.
- bccdee 1y ago
  > It's trivial to ensure that only one message is delivered to only one consumer if that's what you want. Consumer groups track their offset and then commit the offset, the message stays in Kafka but it won't be read again. This IMO is better behaviour than RabbitMQ The trivial solution is to use Kafka. They're clearly saying that Kafka makes it trivial, not that it's trivial to solve from scratch.
  - deleted 1y ago
    [deleted]
  - otterley 1y ago
    What the parent poster described isn’t what makes Kafka’s “exactly once” semantics work. It’s the use of an idempotency token associated with each publication, which effectively turns “at-least-once” semantics into effectively “exactly once” via deduplication.
    - saberience 1y ago
      We used Flink and S3 compatible backends (for the Flink state) to ensure exactly once processing. I didn't say Kafka magically solves these problems for you, but it was required for the scalability we needed.

throwwgisgreat 1y ago

> better behaviour than RabbitMQ since you can always re-read messages once they have been processed I can imagine, a 1 Billion dollar transaction accidentally gets processed by ten thousand client nodes due to a client app synchronization bug, company rethinks its dumb data dumper server strategy...news at 11.

otterley 1y ago

> It's trivial to ensure that only one message is delivered to only one consumer Exactly-once delivery is one of the hardest distributed systems problems. If you’ve “trivially” solved it, please show us your solution.

bccdee 1y ago
> It's trivial to ensure that only one message is delivered to only one consumer if that's what you want. Consumer groups track their offset and then commit the offset, the message stays in Kafka but it won't be read again. This IMO is better behaviour than RabbitMQ The trivial solution is to use Kafka. They're clearly saying that Kafka makes it trivial, not that it's trivial to solve from scratch.
- deleted 1y ago
  [deleted]
- otterley 1y ago
  What the parent poster described isn’t what makes Kafka’s “exactly once” semantics work. It’s the use of an idempotency token associated with each publication, which effectively turns “at-least-once” semantics into effectively “exactly once” via deduplication.
  - saberience 1y ago
    We used Flink and S3 compatible backends (for the Flink state) to ensure exactly once processing. I didn't say Kafka magically solves these problems for you, but it was required for the scalability we needed.