Flink-learning is a comprehensive educational repository and learning resource for Apache Flink stream processing.
The repository addresses the challenge of learning Flink by providing a structured collection of practical examples, tutorials, and real-world case studies. It covers foundational concepts through advanced topics including DataStream API, Table API and SQL, connectors, metrics, and performance tuning. The approach works by combining explanatory content with runnable code examples that demonstrate how to build streaming applications and integrate Flink with external systems like Kafka, Elasticsearch, HBase, Redis, and various other data stores and message queues.
This resource suits developers new to Flink who want hands-on examples alongside conceptual learning, as well as those implementing production systems who need patterns for common integration scenarios. The repository includes large-scale application examples covering use cases such as real-time deduplication at scale, monitoring and alerting, and log storage. Developers should note that the codebase is maintained across multiple branches corresponding to different Flink versions, allowing reference to examples compatible with their specific version requirements.
The project maintains active version alignment with Flink releases, having upgraded its codebase multiple times to track major framework updates. Code examples are tested to run successfully on their target versions. The repository includes supplementary educational materials such as academic papers on stream processing engines and references to foundational Flink literature. Documentation is provided in both English and Chinese, with associated blog posts and a published column on Flink implementation and performance optimization.
