Exactly-Once

Exactly-Once Stream Processing

Guarantee every event is processed exactly once — no duplicates, no data loss. RisingWave provides built-in exactly-once semantics for streaming SQL, materialized views, and sink delivery without manual configuration.

Start Free vs Flink

Zero-Config

Built-in by Default

Exactly-once is the default, not an advanced configuration option. No RocksDB tuning or state backend setup.

End-to-End

Source to Sink

Coordinated checkpoints cover ingestion, processing, state, and sink delivery — no gaps in the guarantee.

Async

Barrier Checkpoints

Lightweight barriers flow through the dataflow graph without stopping processing, preserving throughput.

Auto

Failure Recovery

Automatic checkpoint restore and source replay on failure. No manual intervention or data reprocessing.

Guarantees

What does exactly-once semantics mean in stream processing?

Exactly-once semantics guarantees that each event in a data stream affects the final result precisely one time, even during failures, restarts, or network partitions. Unlike at-least-once or at-most-once approaches, exactly-once eliminates both data loss and duplicate processing without requiring application-level deduplication.

Guarantee	Duplicates?	Data Loss?	Use Case Fit
At-Most-Once	No	Yes	Logging, metrics
At-Least-Once	Yes	No	Notifications, alerts
Exactly-Once	No	No	Financial, analytics, ML

How It Works

How does RisingWave achieve exactly-once without manual configuration?

RisingWave builds exactly-once semantics into its core architecture. Barrier-based checkpointing, transactional state updates, and coordinated sink commits work together automatically. Engineers write SQL and get exactly-once guarantees without configuring state backends, tuning checkpoint intervals, or writing deduplication logic.

Barrier-Based Checkpointing

Lightweight checkpoint barriers flow through the dataflow graph, snapshotting operator state without stopping processing.

Transactional State Updates

All state changes within a checkpoint epoch are committed atomically — partial updates never become visible.

Coordinated Sink Commits

Sink writes are committed in lockstep with internal checkpoints, preventing duplicate output to downstream systems.

Automatic Source Offset Tracking

Kafka and other source offsets are persisted with each checkpoint, enabling precise replay on recovery.

Comparison

Why do other streaming systems struggle with exactly-once guarantees?

Most streaming frameworks treat exactly-once as an opt-in feature requiring significant configuration. Teams must choose state backends, tune checkpoint intervals, implement idempotent sinks, and handle transactional producers manually. This complexity leads to subtle bugs and incorrect results that surface only under failure conditions.

→Flink requires RocksDB configuration, checkpoint storage setup, and transactional sink implementations
→Kafka Streams depends on transactional producers, isolation level settings, and idempotent consumer patterns
→Spark Structured Streaming needs idempotent sink logic and WAL configuration for exactly-once output
→Most systems leave end-to-end exactly-once to the application developer, creating hidden correctness gaps
→RisingWave eliminates this burden by making exactly-once the default, not an advanced configuration option

Frequently Asked Questions

Does exactly-once processing affect performance?

How does RisingWave handle failures and recovery?

Is exactly-once guaranteed end-to-end including sinks?

How does RisingWave's approach differ from Kafka's exactly-once?

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