When a message consumer must choose what to do first, performing the work and then recording progress favors avoiding lost work—even though a crash between those actions can make the work happen twice. Recording progress first avoids that replay but risks skipping work that never finished. This is the crash window behind the title’s choice: tolerate possible duplicates rather than silently advance past an unperformed action.
Where the crash window comes from
A consumer typically has two things to do: produce a side effect, such as writing a database record, and record that it has finished the input message. Those actions are not automatically one indivisible operation. If the process stops between them, the order determines what happens after recovery.
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| Order | Crash point | Possible result after restart |
|---|---|---|
| Perform the side effect, then record progress | After the side effect but before progress is recorded | The message can be delivered again, repeating the side effect. |
| Record progress, then perform the side effect | After progress is recorded but before the side effect completes | The input may not be delivered again, so the intended work can be skipped. |
The first order chooses replay as the safer failure mode: the work may be repeated, but the consumer has not marked the message complete before attempting it. It does not prevent duplicates; it makes them a possibility to handle deliberately.
At-most-once and at-least-once delivery
Apache Kafka’s documentation describes the tradeoff in terms of delivery semantics: “At most once –Messages may be lost but are never redelivered,” while “At least once –Messages are never lost but may be redelivered.” These descriptions assume the system’s relevant durability and failure conditions are met; neither phrase means protection from every possible failure.
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- At-most-once: A message is not redelivered, but a failure can mean its work is lost.
- At-least-once: Work is retried rather than silently skipped, but the same input may be processed more than once.
The choice to finish the side effect before acknowledging or committing progress follows the at-least-once approach. RabbitMQ’s Reliability Guide expresses the same practical principle: “A consuming application should not acknowledge messages until it has done whatever it needs to them: recorded them in a data store, forwarded them on, or performed any other operation.” Broker-specific acknowledgement and recovery mechanisms differ, but the ordering problem is shared.
Make repeated work safe where possible
At-least-once delivery is easier to use when repeating an operation does not create a second effect. An operation is idempotent when applying it again leaves the result equivalent to applying it once. For example, setting an order’s status to “shipped” can be idempotent if it simply writes that state; charging a payment again is not inherently idempotent.
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For a non-idempotent operation, the destination can often help detect repeats. A consumer might include a stable message or operation identifier with a database write, and the destination can enforce uniqueness for that identifier. The key is that the deduplication record and the effect it protects must be coordinated: recording “already handled” separately from the effect can create another crash window.
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What Kafka guarantees—and what it does not
Kafka producer idempotence addresses duplicate records in Kafka caused by producer retries. It does not, by itself, ensure that a consumer’s arbitrary external side effect—such as a database update or remote API call—happens only once.
Kafka transactions can atomically coordinate consumed offsets with output written to Kafka topics. When a processing flow reads from Kafka and writes its result back to Kafka, transactions can make those Kafka-side changes and offsets part of one coordinated operation. That boundary matters: a transaction over Kafka state does not automatically include a separate database or service.
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Kafka documentation uses “Exactly once –Each message is processed once and only once.” Treat “exactly once” as a guarantee with a defined scope, not a universal property of a system. For an external destination, the offset state must be coordinated with what that destination actually stored, or the destination must make retries safe through idempotency or deduplication. Otherwise, a crash can still leave uncertainty about whether the external effect occurred.
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Account for the failures your durability setup covers
Delivery semantics depend on the failure assumptions and durability configuration behind them. Replication, acknowledgements, and the failures a cluster is designed to survive affect whether committed data remains available. “At-least-once” does not mean data can never be lost under every configuration or catastrophic failure, and a transaction does not remove the need to understand what its storage system protects against.
- Identify the failure modes your broker and destination are expected to withstand.
- Verify that replication and acknowledgement settings match those assumptions.
- Check whether the side effect and progress marker share an atomic transaction boundary.
- If they do not, design for retries: use idempotent operations or destination-side deduplication where possible.
The practical decision is not “duplicates are good.” It is that, when the two actions cannot be committed together, acknowledging only after the work is done avoids deliberately creating a path that skips unfinished work. The resulting duplicate risk should be managed at the side-effect boundary.
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