Stateless Stream Processing

Stateless Stream Processing refers to computations on data streams where each incoming event is processed independently, without reference to or reliance on any information from previous events (no historical state). The output for a given input event is solely determined by the content of that event itself and the defined processing logic.

This is the simplest form of stream processing, contrasting with stateful stream processing which requires maintaining context across events.

Core Idea

In a stateless operation:

No Memory of Past Events: The processing logic for one event does not know anything about events that came before or will come after.
Deterministic Output (for a given event): The same input event will always produce the same output, regardless of when it is processed or what other events are in the stream.
No Accumulated State: The operation does not build up any internal state or context over time based on the stream's history.

Common Examples of Stateless Operations

Filtering: Selectively keeping or discarding events based on a condition applied to the event's fields (e.g., SELECT * FROM stream WHERE amount > 100). Each event is evaluated against the condition independently.
Projection/Transformation: Selecting specific fields from an event, renaming fields, or applying simple mathematical or string operations to fields within a single event (e.g., SELECT user_id, price * quantity AS total_cost FROM stream).
Parsing/Validation: Converting an event from one format to another (e.g., from a raw byte string to JSON) or validating its structure, where each event is parsed/validated on its own.
Simple Routing: Directing an event to one of several downstream paths based solely on its content.

Advantages of Stateless Processing

Simplicity: Easier to implement, understand, and reason about compared to stateful operations.
Scalability: Highly scalable because each event can be processed in parallel by any available processing instance without needing to coordinate access to shared state.
Fault Tolerance: Simpler to make fault-tolerant. If a processing instance fails, the event can often be re-routed to another instance and reprocessed without concern for lost state (assuming at-least-once delivery from the source).
Low Latency: Typically introduces minimal processing latency as there's no overhead for state access or management.

Limitations

While simple and efficient, purely stateless processing is limited in the types of insights it can derive. Many real-world stream processing use cases require state to:

Calculate aggregates over time (e.g., sums, averages).
Join data from multiple streams.
Detect complex patterns or sequences of events.
Maintain context for user sessions or application behavior.

Stateless Operations in RisingWave

RisingWave supports stateless operations as fundamental building blocks within its SQL-based stream processing:

WHERE clauses for filtering are typically stateless.
Simple SELECT transformations (e.g., arithmetic operations on columns, string manipulations within a single row) are stateless.
User-Defined Functions (UDFs) can be stateless if they operate only on the current input row without accessing external or historical data.

Even though RisingWave is a powerful stateful stream processing engine (excelling at materialized views, joins, and aggregations), these basic stateless operations are essential components of more complex streaming queries. They are often the first steps in a dataflow, preparing or cleaning data before it enters stateful operators.