Comparing High Level MapReduce Query Languages by R.J. Stewart, P.W. Trinder, and H-W. Loidl.
Abstract:
The MapReduce parallel computational model is of increasing importance. A number of High Level Query Languages (HLQLs) have been constructed on top of the Hadoop MapReduce realization, primarily Pig, Hive, and JAQL. This paper makes a systematic performance comparison of these three HLQLs, focusing on scale up, scale out and runtime metrics. We further make a language comparison of the HLQLs focusing on conciseness and computational power. The HLQL development communities are engaged in the study, which revealed technical bottlenecks and limitations described in this document, and it is impacting their development.
A starting place for watching these three HLQLs as they develop, which no doubt they will continue to do. And one expects them to be joined by other candidates so familiarity with this paper may help speed their evaluation as well.