At-Least-Once Semantics

At-Least-Once Semantics is a processing guarantee commonly found in distributed data systems, particularly messaging queues and stream processing frameworks. It ensures that each message or event sent from a source or processed by an operator will be delivered or processed at least one time, but possibly more than once.

This guarantee prioritizes avoiding data loss over preventing duplicate processing. In scenarios involving failures and subsequent retries, a message might be successfully processed, but the acknowledgment might fail or be delayed, leading the system to re-send or re-process the same message upon recovery.

Context: Processing Guarantees in Distributed Systems

Distributed systems face inherent challenges like network partitions, node failures, and communication delays. When processing data streams across such systems, different levels of guarantees can be offered regarding message delivery and processing:

1. At-Most-Once: Each message is processed zero or one time. Data loss is possible if a failure occurs after message receipt but before processing completion and acknowledgment.
2. At-Least-Once: Each message is processed one or more times. Data loss is prevented, but duplicates are possible.
3. Exactly-Once Semantics (EOS): Each message is processed exactly one time, affecting the final state precisely once. This is the strongest guarantee, preventing both data loss and duplicates, but often requires more complex mechanisms like transactional commits or idempotent operations.

At-Least-Once is often considered a practical middle ground, ensuring no data is lost, while shifting the burden of handling potential duplicates to the downstream consumer or application logic.

How At-Least-Once Works (Example Scenario)

Consider a simple pipeline: Producer -> Message Queue -> Consumer

1. Producer sends Message M1: The queue receives M1.
2. Queue delivers M1 to Consumer: The consumer receives M1.
3. Consumer processes M1: The consumer performs its logic (e.g., updates a database).
4. Consumer attempts to Acknowledge M1: The consumer tells the queue it has finished with M1.
5. Failure! The acknowledgment message is lost due to a network issue, or the consumer crashes after processing but before acknowledging.
6. Recovery: The queue, not having received an acknowledgment for M1 within a timeout period, assumes M1 was not processed successfully.
7. Queue redelivers M1 to Consumer: The consumer (or another instance if the first crashed) receives M1 again.
8. Consumer processes M1 again: The consumer potentially performs the database update again.

In this scenario, Message M1 was processed twice, demonstrating the possibility of duplicates under At-Least-Once semantics.

Implications and Trade-offs

• Pro: No Data Loss: Guarantees that messages are eventually processed, which is critical for many applications.
• Con: Potential Duplicates: Downstream systems must be prepared to handle duplicate messages or events. This often requires designing downstream operations to be Idempotent (meaning executing the operation multiple times with the same input has the same effect as executing it once). Examples include using unique IDs for database inserts with checks, or performing upserts instead of simple inserts.
• Simpler Implementation (Relatively): Compared to Exactly-Once, achieving At-Least-Once is often less complex for system implementers.

At-Least-Once in Stream Processing (RisingWave Context)

While RisingWave aims for Exactly-Once Semantics (EOS) for its internal state management (meaning aggregations, joins, and materialized view updates reflect each input event exactly once within RisingWave itself), the end-to-end guarantee of a pipeline involving external sources and sinks depends on the capabilities of those external systems and their connectors.

• Sources: If a source connector (e.g., reading from Kafka) operates with At-Least-Once semantics (which is common if offsets aren't committed transactionally with RisingWave's internal state updates), RisingWave might receive the same source event multiple times upon recovery. However, RisingWave's stateful processing is designed to handle this correctly internally to maintain its own state consistency (often by using internal deduplication based on primary keys or unique identifiers within the state).
• Sinks: If a sink connector (e.g., writing to an external database or Kafka topic) only provides At-Least-Once guarantees, RisingWave might attempt to write the same resulting change multiple times after a failure and recovery. To achieve effective end-to-end Exactly-Once, the sink operation itself often needs to be Idempotent, or participate in a two-phase commit protocol if supported.

Therefore, while RisingWave provides strong internal guarantees, understanding the semantics of the connected sources and sinks is crucial for determining the overall end-to-end processing guarantee of the pipeline.

Related Glossary Terms

• Exactly-Once Semantics (EOS)
• At-Most-Once Semantics
• Idempotency
• Fault Tolerance
• Checkpointing
• Recovery Point Objective (RPO)
• Message Queue (MQ)
• Event Streaming Platform (ESP)
• RisingWave Source / Sink