Types and persistence in database programming languages
ACM Computing Surveys (CSUR)
An Introduction to Database Systems
An Introduction to Database Systems
Map-reduce-merge: simplified relational data processing on large clusters
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Building a high-level dataflow system on top of Map-Reduce: the Pig experience
Proceedings of the VLDB Endowment
Hive: a warehousing solution over a map-reduce framework
Proceedings of the VLDB Endowment
Hadoop: The Definitive Guide
Improving the diagnosis of mild hypertrophic cardiomyopathy with MapReduce
Proceedings of third international workshop on MapReduce and its Applications Date
Run-time performance optimization of a BigData query language
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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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.