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DLS 2018
Sun 4 - Fri 9 November 2018 Boston, Massachusetts, United States
co-located with SPLASH 2018
Tue 6 Nov 2018 14:00 - 14:30 at The Loft - Optimization Chair(s): Stefan Marr

Relational database management systems (RDBMS) are operationally similar to a dynamic language processor.
They take SQL queries as input, dynamically generate an optimized execution plan, and then execute it. In recent decades, the emergence of in-memory databases with columnar storage, which use array-like storage structures, has shifted the focus on optimizations from the traditional I/O bottleneck to CPU and memory. However, database research so far has primarily focused on CPU cache optimizations. The similarity in the computational characteristics of such database workloads and array programming language optimizations are largely unexplored. We believe that these database implementations can benefit from merging database optimizations with dynamic array-based programming language approaches. Therefore, in this paper, we propose a novel approach to optimize database query execution using a new array-based intermediate representation, HorseIR, that resides between database queries and compiled code.
Furthermore, we provide a translator to generate HorseIR from database execution plans and a compiler that optimizes HorseIR and generates efficient code. We compare HorseIR with the MonetDB RDBMS, by testing standard SQL queries, and show how our approach and compiler optimizations improve the runtime of complex queries.

Tue 6 Nov

13:30 - 15:00: DLS 2018 - Optimization at The Loft
Chair(s): Stefan MarrUniversity of Kent
dls-201813:30 - 14:00
Mark MarronMicrosoft Research
dls-201814:00 - 14:30
Hanfeng ChenMcGill University, Canada, Joseph Vinish D'SilvaMcGill University, Canada, Hongji ChenMcGill University, Canada, Bettina KemmeMcGill University, Canada, Laurie HendrenMcGill University, Canada
dls-201814:30 - 15:00
Manuel SerranoInria, France