Exploratory data analysis tools must respond quickly to a user's questions, so that the answer to one question (e.g. a visualized histogram or fit) can influence the next. In some SQL-based query systems used in industry, even very large (petabyte) datasets can be summarized on a human timescale (seconds), employing techniques such as columnar data representation, caching, indexing, and code generation/JIT-compilation. This article describes progress toward realizing such a system for High Energy Physics (HEP), focusing on the intermediate problems of optimizing data access and calculations for "query sized" payloads, such as a single histogram or group of histograms, rather than large reconstruction or data-skimming jobs. These techniques include direct extraction of ROOT TBranches into Numpy arrays and compilation of Python analysis functions (rather than SQL) to be executed very quickly. We will also discuss the problem of caching and actively delivering jobs to worker nodes that have the necessary input data preloaded in cache. All of these pieces of the larger solution are available as standalone GitHub repositories, and could be used in current analyses.
|Original language||English (US)|
|Journal||Journal of Physics: Conference Series|
|State||Published - Oct 18 2018|
|Event||18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017 - Seattle, United States|
Duration: Aug 21 2017 → Aug 25 2017
All Science Journal Classification (ASJC) codes
- Physics and Astronomy(all)