Consistency (State Management)

Consistency, in the context of stateful stream processing, refers to the guarantees provided by the system about the validity and visibility of its internal state, especially when dealing with concurrent operations, failures, and recovery. It ensures that the state accessed by operators or exposed to users reflects a meaningful and correct point in the processing history, preventing anomalies or contradictions.

Maintaining consistency for distributed state across multiple nodes and over time, despite potential failures, is a core challenge for stateful stream processing systems like RisingWave. It's closely related to the processing guarantees offered (like At-Least-Once or Exactly-Once Semantics) and fault tolerance mechanisms like Checkpointing.

The Challenge: Distributed State Validity

Imagine a simple streaming aggregation counting events per user. The 'state' is the current count for each user. In a distributed system, this state might be partitioned across multiple nodes. Challenges include:

1. Failures: If a node holding part of the state crashes, how does the system ensure the recovered state is correct and consistent with the input data processed?
2. Concurrent Updates: How does the system handle multiple updates to the same state key arriving concurrently or close together?
3. Visibility: When is a state update considered 'committed' and visible to other operations or downstream queries?
4. Recovery: After a failure, how does the system ensure the restored state across all nodes represents a single, valid point in time corresponding to the input streams?

Different systems offer varying levels of consistency guarantees to address these challenges.

Levels of Consistency (Simplified View)

• Eventual Consistency: If no new updates are made, eventually all accesses to a state item will return the last updated value. However, during periods of change or after failures, reads might return stale data temporarily. This is often simpler to achieve but can be insufficient for applications requiring strong correctness.
• Strong Consistency: Guarantees that operations appear to happen atomically and in some global order. Once a state update is committed, all subsequent reads will reflect that update (or a later one). This provides simpler reasoning for application developers but typically requires more sophisticated coordination mechanisms (like consensus protocols or careful checkpointing/transactional approaches).

Consistency and Processing Guarantees

Consistency is intrinsically linked to processing semantics:

• At-Least-Once Semantics: Can sometimes lead to inconsistent state if not handled carefully, as duplicate processing might cause incorrect state updates (e.g., double counting). Systems often need internal mechanisms (like deduplication based on keys within the state) to maintain state consistency even if duplicate inputs arrive.
• Exactly-Once Semantics (EOS): Achieving EOS for state updates means that the effect of each input record is reflected precisely once in the state. This generally implies strong consistency guarantees for the internal state itself. Failures and recovery should restore the state to a point reflecting exactly-once processing up to that recovery point.

Consistency in RisingWave

RisingWave is designed to provide strong consistency guarantees for its internal state, which underpins its ability to offer Exactly-Once Semantics for stateful operations like Materialized Views, aggregations, and joins. This is achieved through:

1. Checkpointing: The asynchronous barrier snapshotting mechanism ensures that checkpoints represent a globally consistent 'cut' of the entire pipeline's state at a specific logical point in time.
2. Hummock State Store: RisingWave's state store manages state versions and uses mechanisms derived from database techniques (like MVCC - Multi-Version Concurrency Control concepts) to handle concurrent access and updates. State changes associated with a specific checkpoint epoch become visible atomically.
3. Epoch-Based Processing: RisingWave internally uses epochs (often aligned with checkpoints) to manage state visibility and consistency. Operations within an epoch see a consistent view of the state.
4. Recovery Mechanism: Upon failure, RisingWave restores operator state from the last successful checkpoint persisted in Hummock, ensuring all restored state across the cluster is mutually consistent and corresponds to the same point in the input streams. Input sources are reset to replay data from just after that point.

This focus on strong consistency ensures that queries against RisingWave's Materialized Views reflect a correct and reliable transformation of the source streams, even in the presence of failures.

Related Glossary Terms

• Exactly-Once Semantics (EOS)
• At-Least-Once Semantics
• Checkpointing
• Fault Tolerance
• Stateful Stream Processing
• State Store (RisingWave Specific)
• Hummock (RisingWave Specific)
• Materialized View
• ACID Transactions (Related concept from databases)