Stateful Stream Processing

Stateful Stream Processing refers to computations on data streams that require maintaining and accessing information (state) derived from previously seen events. Unlike stateless operations that process each event in isolation, stateful operations depend on historical context to produce their results.

This is a fundamental concept in stream processing, as many meaningful real-time analyses require understanding trends, correlations, or accumulated values over time.

Core Idea

The "state" in stateful stream processing can take many forms:

• Aggregations: Counts, sums, averages, min/max values calculated over a window of time or for specific keys (e.g., total sales per product in the last hour).
• Joins: Information from one stream that needs to be temporarily stored to be correlated with matching information from another stream (e.g., holding onto order events to join them with shipment events when they arrive).
• Pattern Detection: Sequences of events or conditions that are being tracked (e.g., detecting a specific series of user actions that might indicate fraud).
• Machine Learning Models: Parameters of a model that are continuously updated as new data arrives.
• Materialized Views: The pre-computed results of a continuous query, which represent a snapshot of the processed stream data, are a form of managed state.

Why is State Necessary?

Many valuable insights from streaming data cannot be derived by looking at events one by one in isolation. Stateful processing allows:

• Contextual Analysis: Understanding an event in the context of what happened before it.
• Temporal Analysis: Analyzing data over periods, identifying trends, and calculating windowed metrics.
• Correlating Data: Combining information from multiple streams or enriching events with reference data.
• Complex Event Processing (CEP): Identifying patterns and relationships among multiple events.

Challenges of Stateful Stream Processing

• State Management: Efficiently storing, accessing, and updating potentially large volumes of state with low latency.
• Fault Tolerance: Ensuring that the state is not lost and remains consistent if system components fail. This usually involves checkpointing state to durable storage.
• Scalability: Distributing and managing state across multiple processing nodes as the data volume or complexity grows.
• Consistency: Guaranteeing that state updates are applied correctly and that queries see a consistent view of the state, especially in distributed environments and during recovery from failures (related to exactly-once semantics).
• Resource Consumption: State can consume significant memory and disk resources.

Stateful Stream Processing in RisingWave

RisingWave is specifically designed as a stateful stream processing system. Its core architecture revolves around efficiently managing state for continuous queries and materialized views:

• Materialized Views as State: The primary way RisingWave handles state is by materializing the results of SQL queries. These materialized views are essentially stored state, incrementally updated as new data flows in.
• State Store (Hummock): RisingWave uses its Hummock state store, built on cloud object storage, to durably and scalably manage the state underlying its materialized views and other streaming operators.
• Incremental Computation: RisingWave's engine performs incremental computation, meaning it only processes changes to the input streams and updates the relevant parts of the state, rather than recomputing everything from scratch. This is highly efficient for stateful operations.
• SQL Interface: Users define stateful computations (like windowed aggregations, joins) using standard SQL, and RisingWave manages the underlying state automatically.

By addressing the challenges of state management, RisingWave enables users to perform complex, stateful analytics on streaming data with ease and reliability.