Lachesis: Automatic partitioning for udf-centric analytics

Jia Zou, Amitabh Das, Pratik Barhate, Arun Iyengar, Binhang Yuan, Dimitrije Jankov, Chris Jermaine

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


Partitioning is effective in avoiding expensive shuffling operations. However, it remains a significant challenge to automate this process for Big Data analytics workloads that extensively use user defined functions (UDFs), where sub-computations are hard to be reused for partitionings compared to relational applications. In addition, functional dependency that is widely utilized for partitioning selection is often unavailable in the unstructured data that is ubiquitous in UDF-centric analytics. We propose the Lachesis system, which represents UDF-centric workloads as workflows of analyzable and reusable sub-computations. Lachesis further adopts a deep reinforcement learning model to infer which sub-computations should be used to partition the underlying data. This analysis is then applied to automatically optimize the storage of the data across applications to improve the performance and users’ productivity.

Original languageEnglish (US)
Pages (from-to)1262-1275
Number of pages14
JournalProceedings of the VLDB Endowment
Issue number8
StatePublished - 2021
Event47th International Conference on Very Large Data Bases, VLDB 2021 - Virtual, Online
Duration: Aug 16 2021Aug 20 2021

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Computer Science


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