Streaming SQL

Streaming SQL refers to the use of SQL (Structured Query Language) or SQL-like languages to define and execute continuous queries on unbounded streams of data. It extends the familiar declarative power of SQL, traditionally used for batch processing on stored datasets in relational databases, to the domain of real-time data processing.

Instead of querying static tables that return a fixed result set, Streaming SQL queries operate continuously on incoming data events, producing results that are incrementally updated or emitted as new data arrives.

Key Concepts and Differences from Traditional SQL

Unbounded Data: Traditional SQL operates on bounded datasets (tables with a finite number of rows). Streaming SQL is designed for unbounded datasets (data streams that are continuous and potentially infinite).
Continuous Queries: Queries are long-running and continuously process new data as it arrives, rather than executing once and terminating.
Time Semantics: Time is a fundamental concept. Streaming SQL often incorporates notions of:
- Event Time: The time an event actually occurred at its source.
- Processing Time: The time an event is processed by the streaming system.
- Ingestion Time: The time an event is ingested into the streaming system.
Windowing: To perform aggregations and joins on unbounded streams, data is often grouped into finite windows (e.g., tumbling windows, sliding windows, session windows) based on time or event counts.
State Management: Stateful operations (like aggregations, joins over time, pattern matching) require the system to maintain intermediate state. Streaming SQL engines manage this state implicitly.
Incremental Results (Materialized Views): Many streaming SQL systems, like RisingWave, continuously update and maintain the results of queries in materialized views. This allows for low-latency access to fresh, processed data without recomputing everything from scratch.
Sinks and Sources: Streaming SQL queries read data from sources (e.g., Kafka, Pulsar, Kinesis, CDC streams) and write results to sinks (e.g., databases, message queues, data lakes, other streams).

Advantages of Using Streaming SQL

Declarative Power: Allows users to specify what they want to compute, rather than how to compute it, simplifying the development of complex stream processing logic.
Familiarity: Leverages the widespread knowledge of SQL, making stream processing accessible to a broader audience of data analysts, data engineers, and developers without requiring them to learn specialized programming paradigms.
Productivity: Accelerates development cycles for real-time applications.
Optimization: Streaming SQL engines can incorporate sophisticated query optimization techniques tailored for continuous data flows.
Portability (to some extent): While syntax can vary slightly between different streaming SQL implementations, the core concepts are often similar, offering some degree of conceptual portability.

Common Streaming SQL Operations

Filtering (WHERE clause): Selecting events based on specific criteria.
Projections (SELECT clause): Choosing and transforming fields from input streams.
Transformations: Applying functions (scalar, user-defined) to data fields.
Joins: Combining data from multiple streams or joining streams with tables (stream-table joins).
Aggregations (GROUP BY, COUNT, SUM, AVG, etc.): Computing summary statistics, often within time-based windows.
Windowing Operations: Defining how data is grouped for aggregations and joins over time.
Pattern Matching (MATCH_RECOGNIZE): Identifying sequences of events that match a defined pattern.

RisingWave and Streaming SQL

RisingWave is a distributed streaming database that uses PostgreSQL-compatible Streaming SQL as its primary interface. This means users can:

Define sources and sinks using SQL DDL.
Create materialized views that represent continuous queries on streaming data.
Perform complex transformations, joins (stream-stream, stream-table), and aggregations.
Leverage time windowing and event-time processing.

RisingWave's SQL implementation is designed to be highly compatible with PostgreSQL syntax, making it easy for users familiar with PostgreSQL to get started with stream processing.

Challenges and Considerations

Standardization: While SQL itself is standardized (ANSI SQL), extensions for streaming are less so, leading to variations between different platforms (e.g., Flink SQL, ksqlDB, Spark Structured Streaming SQL, RisingWave SQL).
Complexity of Time: Handling different time semantics (event time vs. processing time) and dealing with late or out-of-order data can be complex.
State Management at Scale: Efficiently managing large amounts of state for complex queries in a fault-tolerant manner is a significant technical challenge for streaming engines.