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Comment by chuckhend | Hacker News Reader

If the message never reaches the queue (network error, database is down, app is down, etc), then yes that is a 0 delivery scenario. Once the message reaches the queue though, it is guaranteed that only a single consumer can read the message for the duration of the visibility timeout. FOR UPDATE guarantees only a single consumer can read the record, and the visibility timeout means we don't have to hold a lock. After that visibility timeout expires, it is an at-least-once scenario. Any suggestion for how we could change the verbiage on the readme to make that more clear?

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You are talking about distributed systems, nobody expects to read "exactly once" delivery. If I read that on the docs, I consider that a huge red flag.

And the fact is that what you describe is a performance optimization, I still have to write my code so that it is idempotent, so that optimization does not affect me in any other way, because exactly once is not a thing.

All of this to say, I'm not even sure it's worth mentioning?

You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features.

[0] https://flink.apache.org/

[1] https://flink.apache.org/what-is-flink/flink-applications/#b...

A common mistake:

Exactly once delivery is not the same as exactly once processing.

Exactly once delivery is an impossibility; see the two generals problem. Exactly once processing is usually at-least-once delivery coupled with deduplication or an otherwise idempotent action

They are not the first or the last.

You CAN have "at most once" processing, since there is always the option of processing zero messages due to systems being down, but it's a joint effort, nobody is able to offer it "for free" (as in, no effort to implement it)

> Once the message reaches the queue though, it is guaranteed that only a single consumer can read the message for the duration of the visibility timeout.

But does the message get persisted and replicated before any consumer gets the message (and after the submission of the message is acked)? If it's a single node, the answer is simply "no": the hard drive can melt before anyone reads the message and the data is lost. It's not "exactly once" if nobody gets the message.

And if the message is persisted and replicated, but there's subsequently a network partition, do multiple consumers get to read the message? What happens if writing the confirmation from the consumer fails, does the visibility timeout still expire?

> After that visibility timeout expires, it is an at-least-once scenario.

That's not really what "at least once" refers to. That's normal operation, and sets the normal expectations of how the system should work under normal conditions. What matters is what happens under abnormal conditions.

> FOR UPDATE guarantees only a single consumer can read the record, and the visibility timeout means we don't have to hold a lock

This is not always true!

Postgres does snapshot isolation within transactions that begins on the first statement within a transaction. This means that some isolation levels like REPEATABLE READ can feel misleadingly “safer” but actually break your guarantee. From the docs:

> Note also that if one is relying on explicit locking to prevent concurrent changes, one should either use Read Committed mode, or in Repeatable Read mode be careful to obtain locks before performing queries. A lock obtained by a repeatable read transaction guarantees that no other transactions modifying the table are still running, but if the snapshot seen by the transaction predates obtaining the lock, it might predate some now-committed changes in the table. A repeatable read transaction's snapshot is actually frozen at the start of its first query or data-modification command (SELECT, INSERT, UPDATE, DELETE, or MERGE), so it is possible to obtain locks explicitly before the snapshot is frozen.

https://www.postgresql.org/docs/current/applevel-consistency...

Well that only is a problem with locks if you actually need locks to satisfy your guarantee and you acquire the lock after your first read. However repeatable reads are never a good guarantee without locking

As u/Fire-Dragon-DoL says, you can have an exactly-once guarantee within this small system, but you can't extend it beyond that. And even then, I don't think you can have it. If the DB is full, you can't get new messages and they might get dropped, and if the threads picking up events get stuck or die then messages might go unprocessed and once again the guarantee is violated. And if processing an event requires having external side-effects that cannot be rolled back then you're violating the at-most-once part of the exactly-once guarantee.

> you can have an exactly-once guarantee within this small system

It's actually harder to do this in a small system. I submit a message to the queue, and it's saved and acknowledged. Then the hard drive fails before the message is requested by a consumer. It's gone. Zero deliveries, or "at most once".

> If the DB is full, you can't get new messages and they might get dropped

In this case, I'd expect the producer to receive a failure, so technically there's nothing to deliver.

> if the threads picking up events get stuck or die

While this obviously affects delivery, this is a concurrency bug and not a fundamental design choice. The failures folks usually refer to in this context are ones that are outside of your control, like hardware failures or power outages.

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