Download
Apache Flink is a distributed engine for stateful computations over data streams and batches, designed for low-latency processing at scale. Its core runtime executes dataflow graphs with fine-grained backpressure and checkpointing, allowing applications to recover consistently from failures. Flink’s event-time model and watermarks enable accurate out-of-order processing, windowing, and complex time semantics that typical real-time systems struggle with. Developers program against high-level APIs—DataStream and Table/SQL—to express transformations, joins, and stateful patterns, while specialized libraries support CEP, machine learning workflows, and connectors. A rich connector ecosystem integrates with systems like Kafka, Kinesis, filesystems, JDBC sources/sinks, and object stores. Deployments span Kubernetes, YARN, Mesos, and standalone clusters, and operational features such as savepoints, state backends, and metrics make long-running jobs manageable in production.
Features
- Streaming-first engine that natively supports both real-time stream processing and batch processing workloads
- Elegant, fluent APIs in Java (including DataStream API), Java/Scala/Python/SQL support via Table and SQL APIs
- Advanced event-time semantics with out-of-order processing and flexible windowing (time, count, session, custom triggers)
- Fault-tolerance through exactly-once state consistency via periodic checkpoints, savepoints, and natural back-pressure mechanisms
- High performance with low latency, high throughput, and a custom memory manager for efficient switching between in-memory and out-of-core processing
- Rich ecosystem including libraries for graph processing, machine learning, and complex event processing, plus connectors for Hadoop, Kafka, HDFS, and more
Project Samples
