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This article presents Alibaba Cloud's open‑source big‑data team's enhancements to Flink CEP, covering its core concepts, typical use cases, dynamic multi‑rule loading via FLIP‑200, extended CEP SQL syntax, performance optimizations, and real‑world risk‑control scenarios.
Bilibili’s FlinkSQL team has enhanced the Flink engine—now based on 1.11 with back‑ported 1.15 features—by adding Delay‑Join, table‑valued functions, projection‑push‑down, UDF and object reuse, automatic mini‑batch/two‑phase aggregation, key‑group skew fixes, connector slot‑groups, real‑time projection with Hudi, and RocksDB state‑performance tweaks, while planning remote state backends and deeper stream‑batch integration.
The article details how to add field‑lineage tracking to Spark SQL by creating a custom SparkSessionExtension that injects a check‑analysis rule and a parser, which capture INSERT statements, analyze the physical plan, and generate a JSON mapping of source‑to‑target fields for data governance.