Everything you always wanted to know about compiled and vectorized queries but were afraid to ask

Kersten T, Leis V, Kemper A, Neumann T, Pavlo A, Boncz P (2018)


Publication Type: Conference contribution

Publication year: 2018

Publisher: VLDB Endowment

Book Volume: 11

Pages Range: 2209-2222

Conference Proceedings Title: Proceedings of the VLDB Endowment

Event location: Rio de Janeiro BR

DOI: 10.14778/3275366.3275370

Abstract

The query engines of most modern database systems are either based on vectorization or data-centric code generation. These two state-of-the-art query processing paradigms are fundamentally different in terms of system structure and query execution code. Both paradigms were used to build fast systems. However, until today it is not clear which paradigm yields faster query execution, as many implementation-specific choices obstruct a direct comparison of architectures. In this paper, we experimentally compare the two models by implementing both within the same test system. This allows us to use for both models the same query processing algorithms, the same data structures, and the same parallelization framework to ultimately create an apples-to-apples comparison. We find that both are efficient, but have different strengths and weaknesses. Vectorization is better at hiding cache miss latency, whereas data-centric compilation requires fewer CPU instructions, which benefits cacheresident workloads. Besides raw, single-threaded performance, we also investigate SIMD as well as multi-core parallelization and different hardware architectures. Finally, we analyze qualitative differences as a guide for system architects.

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How to cite

APA:

Kersten, T., Leis, V., Kemper, A., Neumann, T., Pavlo, A., & Boncz, P. (2018). Everything you always wanted to know about compiled and vectorized queries but were afraid to ask. In Proceedings of the VLDB Endowment (pp. 2209-2222). Rio de Janeiro, BR: VLDB Endowment.

MLA:

Kersten, Timo, et al. "Everything you always wanted to know about compiled and vectorized queries but were afraid to ask." Proceedings of the 44th International Conference on Very Large Data Bases, VLDB 2018, Rio de Janeiro VLDB Endowment, 2018. 2209-2222.

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