  Query languages in general and SQL in particular are arguably one of the most
successful programming interfaces. Yet, in the domain of high-energy physics
(HEP), they have found limited acceptance. This is surprising since data
analysis in HEP matches the SQL model well: it is fully structured data queried
using combinations of selections, projections, joins, and reductions. To gain
insights on why this is the case, in this paper we perform an exhaustive
performance and functionality analysis of several data processing platforms
(Amazon Athena, Google Big Query, Presto, Rumble) and compare them to the new
RDataFrame interface of the ROOT framework, the most commonly used system by
particle physicists today. The goal of the analysis is to identify the
potential advantages and shortcomings of each system considering not only
performance but also cost for cloud deployments, suitability of the query
dialect, and resulting query complexity. The analysis is done using a HEP
workload: the Analysis Description Languages (ADL) benchmark, created by
physicists to capture representative aspects of their data processing tasks.
The evaluation of these systems results in an interesting and rather complex
picture of existing solutions: those offering the best possibilities in terms
of expressiveness, conciseness, and usability turn out to be the slowest and
most expensive; the fastest ones are not the most cost-efficient and involve
complex queries; RDataFrame, the baseline we use as a reference, is often
faster and cheaper but is currently facing scalability issues with large
multi-core machines. In the paper, we analyze all the aspects that lead to such
results and discuss how systems should evolve to better support HEP workloads.
In the process, we identify several weaknesses of existing systems that should
be relevant to a wide range of use cases beyond particle physics.
