Exactly-Once Semantics
What the phrase really means, where it holds, and the idempotent sink underneath it.
5 to work through
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A payments team requires exactly-once processing of a transaction stream. What is actually achievable, and what should be built?
2 min answer -
advanced
A team requires exactly-once processing across a message platform and its external providers. What is achievable, and what should be built instead?
2 min answer -
advanced
A team wants exactly-once processing in a streaming pipeline. Where do transactional producers, idempotent consumers and output-side deduplication each fit, and which failure modes still require reconciliation?
3 min answer -
advanced
A vendor claims their streaming platform provides exactly-once processing. How do you evaluate the claim?
1 min answer -
advanced
Your streaming job has exactly-once semantics enabled. Customers report receiving the same email twice. Explain.
2 min answer
3 terms in this topic
Exactly-Once Semantics
The guarantee that each input record affects the output state precisely once, achieved through atomic state-and-offset commits rather than through de…
patternIdempotent Consumer
A message consumer whose effect is the same whether a message is processed once or many times, which is what makes at-least-once delivery safe.
patternIdempotent Sink
A destination where writing the same record twice has the same effect as writing it once, which is what makes end-to-end exactly-once achievable at all.
Neighbouring topics
Streaming & Real-Time Data
General material on continuous processing of unbounded data.
Streaming vs Batch
The freshness requirement that actually justifies streaming, and the cost of assuming one.
Stream Processing Frameworks
Flink, Kafka Streams, Spark Structured Streaming — state, checkpointing and recovery.
Windowing
Tumbling, sliding and session windows, and the aggregation each one answers.
Watermarks & Late Data
Deciding a window is complete when events can still arrive, and what to do when they do.
Stateful Stream Processing
Keyed state, state backends, checkpoint size, and the restore time that follows.
Stream-Table Duality
A changelog and a table as two views of the same thing, and materialising between them.
Kappa vs Lambda
One pipeline replayed versus two pipelines reconciled, and the maintenance each carries.
Streaming Schema Evolution
Changing an event's shape while a retained log still holds every older version of it.
Streaming Joins
Joining two unbounded streams, the buffering it needs, and the enrichment alternative.
Backfill & Reprocessing
Replaying history through changed logic without double-counting the live output.
CDC to Stream
Turning database changes into an event log, and how that differs from a domain event.
Real-Time Serving Layer
Where a low-latency read of a streaming aggregate actually lands.
Feature Freshness
How stale a feature can be before the model degrades, and the pipeline that follows.
Streaming SLOs
End-to-end latency, consumer lag and completeness as commitments rather than dashboards.
Partition Keys & Ordering
Ordering guaranteed only within a partition, and choosing the key that makes that enough.
Dead Letter Handling
The poison message that blocks a partition, and the queue nobody reads.
Streaming Cost
Always-on compute, retention and cross-zone traffic as the three bills that surprise.
Real-Time Analytical Stores
Druid, Pinot and ClickHouse — ingest-and-query engines for sub-second aggregation.