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At most once, at least once, exactly once
In modern architecture, systems are broken up into small and independent building blocks with well-defined interfaces between them. Message queues provide communication and coordination for those building blocks. Today, let’s discuss different delivery semantics: at-most once, at-least once, and exactly once.
As the name suggests, at-most once means a message will be delivered not more than once. Messages may be lost but are not redelivered. This is how at-most once delivery works at the high level.
Use cases: It is suitable for use cases like monitoring metrics, where a small amount of data loss is acceptable.
With this data delivery semantic, it’s acceptable to deliver a message more than once, but no message should be lost.
Use cases: With at-least once, messages won’t be lost but the same message might be delivered multiple times. While not ideal from a user perspective, at-least once delivery semantics are usually good enough for use cases where data duplication is not a big issue or deduplication is possible on the consumer side. For example, with a unique key in each message, a message can be rejected when writing duplicate data to the database.
Exactly once is the most difficult delivery semantic to implement. It is friendly to users, but it has a high cost for the system’s performance and complexity.
Use cases: Financial-related use cases (payment, trading, accounting, etc.). Exactly once is especially important when duplication is not acceptable and the downstream service or third party doesn’t support idempotency.
Question: what is the difference between message queues vs event streaming platforms such as Kafka, Apache Pulsar, etc?
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