Versatile XQuery Processing in MapReduce

TL;DR: An existing single-node query processor is extended, Brackit, adding MR as a distributed coordination layer, and heavy reuse of the standard query processor not only provides performance, but also allows for a more elegant design which transparently integrates MR processing into a generic query engine.

Abstract: The MapReduce MR framework has become a standard tool for performing large batch computations--usually of aggregative nature--in parallel over a cluster of commodity machines. A significant share of typical MR jobs involves standard database-style queries, where it becomes cumbersome to specify map and reduce functions from scratch. To overcome this burden, higher-level languages such as HiveQL, PigLatin, and JAQL have been proposed to allow the automatic generation of MR jobs from declarative queries. We identify two major problems of these existing solutions: i they introduce new query languages and implement systems from scratch for the sole purpose of expressing MR jobs; and ii despite solving some of the major limitations of SQL, they still lack the flexibility required by big data applications. We propose BrackitMR, an approach based on the XQuery language with extended JSON support. XQuery not only is an established query language, but also has a more expressive data model and more powerful language constructs, enabling a much greater degree of flexibility. From a system design perspective, we extend an existing single-node query processor, Brackit, adding MR as a distributed coordination layer. Such heavy reuse of the standard query processor not only provides performance, but also allows for a more elegant design which transparently integrates MR processing into a generic query engine.