Widespread distributed processing of big datasets has been around for more than a decade now thanks to Hadoop, but only recently higher-level abstractions have been proposed for programmers to easily operate on those datasets, e.g. Spark. ROOT has joined that trend with its RDataFrame tool for declarative analysis, which currently supports local multi-threaded parallelisation. However, RDataFrame’s programming model is general enough to accommodate multiple implementations or backends: users could write their code once and execute it as is locally or distributedly, just by selecting the corresponding backend.
This abstract introduces PyRDF, a new python library developed on top of RDataFrame to seamlessly switch from local to distributed environments in a transparent way for users. Programmers are provided with ergonomic interfaces, integrated with web-based services, which allow to dynamically plug in new resources, as well as to write, execute, monitor and debug distributed applications in the most intuitive way possible.
|Consider for promotion||Yes|