This article explains the concept of retract streams in Apache Flink, detailing how non‑retract Kafka sources and Group‑By aggregations generate delete/insert messages, provides code examples for aggregation and sink operators, and compares retract mechanisms across aggregation and CDC sink scenarios.
This article, presented by Tencent senior engineer Du Li, details the current state of Flink SQL, compares Jar, Canvas, and SQL modes, introduces window‑function extensions, retract‑stream optimizations, and outlines future roadmap plans for cost‑based optimization and new features in the real‑time computing platform.
This article introduces Apache Flink's Streaming SQL, explains its theoretical foundations such as the table‑stream relationship and watermark semantics, describes the platform's practical enhancements—including source/sink wrappers, built‑in functions, and native Retract Stream support—and showcases several real‑time computation examples.
