RHEEMix in the data jungle: a cost-based optimizer for cross-platform systems

Sebastian Kruse, Zoi Kaoudi, Bertty Contreras-Rojas, Sanjay Chawla, Felix Naumann, Jorge Arnulfo Quiané-Ruiz

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Data analytics are moving beyond the limits of a single platform. In this paper, we present the cost-based optimizer of Rheem, an open-source cross-platform system that copes with these new requirements. The optimizer allocates the subtasks of data analytic tasks to the most suitable platforms. Our main contributions are: (i) a mechanism based on graph transformations to explore alternative execution strategies; (ii) a novel graph-based approach to determine efficient data movement plans among subtasks and platforms; and (iii) an efficient plan enumeration algorithm, based on a novel enumeration algebra. We extensively evaluate our optimizer under diverse real tasks. We show that our optimizer can perform tasks more than one order of magnitude faster when using multiple platforms than when using a single platform.

Original languageEnglish
Pages (from-to)1287-1310
Number of pages24
JournalVLDB Journal
Volume29
Issue number6
DOIs
StatePublished - Nov 2020
Externally publishedYes

Keywords

  • Cross-platform
  • Data processing
  • Polystore
  • Query optimization

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