Specialised stream processing technology inspired by the Google Data Flow model. Based on a single record (not micro batch) model, with exactly once processing semantics (for supported sources and sinks) via light weight checkpointing, and focusing on high throughput, low latency use cases. Supports both a Java and Scala API, with a fluent DataStream API for working with continuous data flows (including a flexible windowing API that supports both event time and processing time windows and support for out of order or late data), and a DataSet API for working with batch data sets (that uses the same streaming execution engine). Also supports a number of connectors and extra libraries, including experimental support for SQL expressions, a CEP library (FlinkCEP) that can be used to detect complex event patterns, a beta package for running Storm apps on Flink, a graph processing library (Gelly) and a machine learning library (FlinkML). Clustered, with support for YARN, Mesos and Kubernetes as well as standalone clusters. Open sourced by Data Artisans in April 2013, donated to the Apache Foundation in April 2014 before graduating in August 2014. Under active development with a large number of contributors and a range of user case studies. Sold as a hosted managed service (dA Platform) by Data Artisans who also supply training.
Other Names Flink Vendors The Apache Software Foundation, Data Artisans Type Commercial Open Source Last Updated April 2019 - 1.8
Is packaged by Apache Bigtop, Amazon EMR
version release date release links release comment 1.3 2017-06-01 summary 1.4 2017-12-12 summary 1.5 2018-06-05 summary Datanami view 1.6 2018-08-09 summary; Data Artisans view 1.7 2018-11-30 summary 1.8 2019-04-09 summary