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I chose to perform the work before recording a message as complete. That order can repeat an effect if the consumer crashes at the wrong moment, but reversing it can mark unfinished work complete and lose it. The choice favors at-least-once processing: possible duplicates rather than silently skipping work.
Where the crash window comes from
A message consumer often has two separate jobs: perform an effect, such as writing a database record, and record that it has finished the message. Unless both changes can be committed atomically, there is a gap between them.
Effect first: replay is possible
- The consumer receives a message.
- It performs the side effect.
- It crashes before recording progress or acknowledging the message.
- After recovery, the message is delivered again, so the effect may happen twice.
Progress first: work can be skipped
- The consumer receives a message.
- It records progress or acknowledges the message.
- It crashes before the side effect is durable.
- The message is not delivered again, even though its work may not have happened.
These are different failure preferences. The first risks duplicate work; the second risks losing unperformed work. Apache Kafka describes at-most-once as “Messages may be lost but are never redelivered,” and at-least-once as “Messages are never lost but may be redelivered.” Those definitions assume the configured system’s relevant failure and durability conditions; neither label means protection from every possible failure. Apache Kafka: Message Delivery Semantics.
What at-least-once and at-most-once mean in practice
| Choice | Crash consequence | What to plan for |
|---|---|---|
| At-most-once | Progress may be recorded before work is durable; the message may be lost rather than redelivered. | Use only when losing an occasional operation is preferable to duplicates, or when another recovery mechanism can reconstruct it. |
| At-least-once | Work is attempted before progress is recorded; a crash can cause redelivery and repeat the effect. | Make the operation idempotent, or deduplicate retries using a stable message or operation identifier. |
| Exactly-once | Possible only within a clearly defined scope where the relevant state changes can be coordinated. | Specify which input, output, and side effects are included; a guarantee for one system boundary does not automatically cover another. |
“Double-counting” is a useful shorthand for the risk, but a duplicate delivery does not have to produce a duplicate result. If applying the same operation twice leaves the destination in the same state as applying it once, the operation is idempotent. Otherwise, the destination needs a deduplication strategy or an atomic way to coordinate the effect with progress.
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How to reduce duplicate effects
Make the operation idempotent
Design the destination update so retries with the same operation identifier do not apply the business effect again. For example, a hypothetical payment-recording service could store a unique transaction identifier and reject a second attempt with that identifier. This requires the identifier check and the effect to be protected together; checking in one step and writing in another can recreate a crash window.
Deduplicate at the destination
Keep a durable record of processed message or operation IDs alongside the result. Enforce uniqueness at the destination where possible, so concurrent retries cannot both pass a separate “already processed?” check. Retention matters: removing deduplication records while old messages can still be replayed reopens the possibility of duplicates.
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Coordinate progress and output atomically
If the output and consumed position are stored in one transactional system, commit them together. When that is not possible, preserve at-least-once behavior and make retries safe rather than assuming separate writes will stay synchronized.
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Producer idempotence addresses producer retries
Kafka producer idempotence prevents duplicate records in the Kafka log caused by producer retries. It does not ensure that a consumer’s arbitrary database write, API call, or other external effect happens only once. Producer-side deduplication and consumer-side effect handling are different boundaries. Apache Kafka producer configuration: enable.idempotence.
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Kafka transactions cover Kafka output and offsets
Kafka transactions can atomically commit consumed offsets together with records produced to Kafka topics. This supports exactly-once processing within that Kafka-to-Kafka boundary when configured and used as documented. It does not atomically include a write to an external database or a request to a remote service. Apache Kafka: Message Delivery Semantics.
External destinations need their own coordination
When a consumer writes to a database or calls an API, Kafka cannot by itself make that external effect and its offset update one atomic operation. Use destination-side idempotency or deduplication, or a design that coordinates the destination’s durable state with progress. If the external system supports idempotency keys, use a stable key for retries and confirm how long the destination retains those keys.
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Durability depends on the failures you intend to survive
Delivery labels are not a substitute for durability settings. Replication, acknowledgements, storage behavior, and the specific failure—such as a process crash versus loss of a broker or replica—affect whether committed data remains available. Configure and test the system against the failures your service must tolerate; do not interpret “at-least-once” or “exactly-once” as a promise that no data can ever be lost. Kafka’s delivery-semantics discussion describes these guarantees in the context of its producer, broker, and consumer behavior: Apache Kafka documentation.
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The same acknowledge-after-work principle applies beyond Kafka. RabbitMQ’s reliability guide says a consumer “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 mechanics differ, but acknowledging before durable work creates the corresponding risk of skipped work. RabbitMQ Reliability Guide.
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