VerdictDB: Universalizing approximate query processing

Yongjoo Park, Barzan Mozafari, Joseph Sorenson, Junhao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases-an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a databaseagnostic approximation engine that will widen the reach of this emerging technology across various platforms. Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171× speedup (18.45× on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.

Original languageEnglish (US)
Title of host publicationSIGMOD 2018 - Proceedings of the 2018 International Conference on Management of Data
EditorsGautam Das, Christopher Jermaine, Ahmed Eldawy, Philip Bernstein
PublisherAssociation for Computing Machinery
Pages1461-1476
Number of pages16
ISBN (Electronic)9781450317436
DOIs
StatePublished - May 27 2018
Externally publishedYes
Event44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018 - Houston, United States
Duration: Jun 10 2018Jun 15 2018

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

Other44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018
CountryUnited States
CityHouston
Period6/10/186/15/18

ASJC Scopus subject areas

  • Software
  • Information Systems

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