The Data Activated 流 (Liu) Graph Engine (DALiuGE) is a workflow graph execution framework, specifically designed to support very large scale processing graphs for the reduction of interferometric radio astronomy data sets. DALiuGE aims to provide a distributed data management platform and a scalable pipeline execution environment to support continuous, soft real-time, data-intensive processing for producing radio astronomy data products.

DALiuGE originated from a prototyping activity as part of the SKA SDP Consortium called Data Flow Management System (DFMS).

The development of DALiuGE is largely based on radio astronomy processing requirements. However, DALiuGE has adopted a generic, data-driven framework architecture potentially applicable to many other data-intensive applications.

DALiuGE stands on shoulders of many previous studies on dataflow, data management, distributed systems (databases), graph theory, and HPC scheduling. DALiuGE has also borrowed useful ideas from existing dataflow-related open sources (mostly Python!) such as Luigi, TensorFlow, Airflow, Snakemake, etc. Nevertheless, we believe DALiuGE has some unique features well suited for data-intensive applications:

  • Completely data-activated, by promoting data Drops to become graph “nodes” (no longer just edges) that have persistent states and can consume and raise events

  • Integration of data-lifecycle management within the data processing framework

  • Separation of concerns between logical graphs (high level workflows) and physical graphs (execution recipes)

  • Flexible pipeline component interface, including Docker containers.

  • Native multi-core execution out of the box

In Architecture and Design we give a glimpse to the main concepts present in DALiuGE. Later sections of the documentation describe more in detail how DALiuGE works. Enjoy!