The Apache Flink PMC is proud to announce the release of Apache Flink 2.2.0. Flink 2.2.0 further enriches AI capabilities, enhances materialized tables and the Connector framework, and improves batch processing and PyFlink support. This release brings together 73 global contributors, implements 9 FLIPs (Flink Improvement Proposals), and resolves over 220 issues.
Apache Flink 2.2 ushers in the AI era by seamlessly integrating real-time data processing with artificial intelligence. It introduces ML_PREDICT for large language model inference and VECTOR_SEARCH for real-time vector similarity search, empowering streaming AI applications.

Flink is an open-source stream processing framework for building real-time data applications, offering powerful features for data processing, analytics, and event-driven architectures. Developers can learn about stream processing concepts, event-driven architectures, and real-time analytics techniques to build scalable and fault-tolerant data pipelines for processing large volumes of data in real-time.

Flink

Apache Flink 2.2.0 introduces AI-focused features including ML_PREDICT for LLM inference and VECTOR_SEARCH for real-time vector similarity search. The release enhances materialized tables with optional freshness clauses and bucketing support, improves delta joins with CDC source support and caching, and adds balanced task scheduling for better load distribution. New connector capabilities include rate limiting for scan sources and skew-aware split assignment. PyFlink gains async function support for external service queries with retry mechanisms. The release includes 73 contributors, 9 FLIPs, and resolves over 220 issues.

Apache Flink 2.2.0: Advancing Real-Time Data + AI and Empowering Stream Processing for the AI Era