[Demo] low-latency spark queries on updatable data

Alexandru Uta, Bogdan Ghit, Ankur Dave, Peter Boncz

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

As data science gets deployed more and more into operational applications, it becomes important for data science frameworks to be able to perform computations in interactive, sub-second time. Indexing and caching are two key techniques that can make interactive query processing on large datasets possible. In this demo, we show the design, implementation and performance of a new indexing abstraction in Apache Spark, called the Indexed DataFrame. This is a cached DataFrame that incorporates an index to support fast lookup and join operations, and supports updates with multi-version concurrency. We demonstrate the Indexed Dataframe on a social network dataset using microbench-marks and real-world graph processing queries, in datasets that are continuously growing.

Original languageEnglish
Title of host publicationSIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages2009-2012
Number of pages4
ISBN (Electronic)9781450356435
DOIs
Publication statusPublished - 25 Jun 2019
Event2019 International Conference on Management of Data, SIGMOD 2019 - Amsterdam, Netherlands
Duration: 30 Jun 20195 Jul 2019

Publication series

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

Conference

Conference2019 International Conference on Management of Data, SIGMOD 2019
CountryNetherlands
CityAmsterdam
Period30/06/195/07/19

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