ParaLite: Supporting Collective Queries in Database System to Parallelize User-Defined Executable

This paper proposes extensions to parallel database systems called collective queries and User-Defined eXecutables (UDX). A collective query is an SQL query whose results are distributed to multiple clients and then processed by them in parallel, using arbitrary external programs (user-defined executables). The intended applications are data intensive work-flows, typically built out of various independently developed executables and scripts. Collective queries facilitate description of such workflows by making data parallel execution of external programs on big data easy and streamlined. It also provides the workflow developers with a familiar and powerful language SQL, for flexible data filtering and stereotypical data processing tasks. We implement this concept in a system "ParaLite", a parallel database system based on a popular lightweight database SQ Lite. It equips with data transfer optimization algorithms that distribute query results to multiple clients, taking both communication cost and compute loads into account. We verified the correctness and performance of Para Lite and the experimental results show that Para Lite has good performance on SQL processing and achieves good scalability for the parallelization of UDX.