StreamingSQLStatements

Streaming SQL statements are SQL-based queries designed to process continuous, unbounded data streams in real time. They are executed by stream processing engines and produce updated results as new events arrive, enabling real-time analytics and event-driven applications. Unlike traditional batch SQL, streaming SQL operates on an ongoing input feed and continuously refreshes outputs rather than producing a single static result.

Key features include continuous queries, time-aware semantics, and stateful processing. Streaming SQL supports windowed aggregations over unbounded streams, using tumbling, hopping, or session windows to create bounded summaries. It also enables joins between streams and between streams and tables, allowing complex event-pattern processing. Queries maintain state to remember past events and manage late data, often employing mechanisms to handle out-of-order events and late arrivals.

Time concepts are central, with event-time processing based on timestamps in the data and processing-time processing based on system time. Watermarks or similar mechanisms help bound late data and determine when results can be emitted. Processing guarantees range from at-least-once to exactly-once, depending on the engine and configuration, and fault tolerance is typically achieved through checkpointing or durable logs.

In practice, streaming SQL is implemented in platforms such as Apache Flink SQL, ksqlDB, Spark Structured Streaming, and Google Cloud Dataflow. While there is ongoing effort to standardize streaming extensions to SQL, engines often provide a mix of standard and vendor-specific syntax and semantics. Use cases include real-time dashboards, fraud detection, anomaly monitoring, and live clickstream analysis.