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In-Browser Interactive SQL Analytics with Afterburner

Published: 09 May 2017 Publication History

Abstract

This demonstration explores the novel and unconventional idea of implementing an analytical RDBMS in pure JavaScript so that it runs completely inside a browser with no external dependencies. Our prototype, called Afterburner, generates compiled query plans that exploit two JavaScript features: typed arrays and asm.js. On the TPC-H benchmark, we show that Afterburner achieves comparable performance to MonetDB running natively on the same machine. This is an interesting finding in that it shows how far JavaScript has come as an efficient execution platform. Beyond a mere technical curiosity, we demonstrate how our techniques can support interactive data exploration by automatically generating materialized views from a backend that is then shipped to the browser to facilitate subsequent interactions seamlessly and efficiently.

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Cited By

View all
  • (2023)DynQ: a dynamic query engine with query-reuse capabilities embedded in a polyglot runtimeThe VLDB Journal10.1007/s00778-023-00784-232:5(1111-1135)Online publication date: 13-Mar-2023
  • (2022)BrowVis: Visualizing Large Graphs in the BrowserIEEE Access10.1109/ACCESS.2022.321888410(115776-115786)Online publication date: 2022
  • (2021)Language-agnostic integrated queries in a managed polyglot runtimeProceedings of the VLDB Endowment10.14778/3457390.345740514:8(1414-1426)Online publication date: 21-Oct-2021
  • Show More Cited By

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Published In

cover image ACM Conferences
SIGMOD '17: Proceedings of the 2017 ACM International Conference on Management of Data
May 2017
1810 pages
ISBN:9781450341974
DOI:10.1145/3035918
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 May 2017

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Author Tags

  1. data exploration
  2. interactive analytics
  3. query compilation
  4. split execution

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SIGMOD/PODS'17
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Cited By

View all
  • (2023)DynQ: a dynamic query engine with query-reuse capabilities embedded in a polyglot runtimeThe VLDB Journal10.1007/s00778-023-00784-232:5(1111-1135)Online publication date: 13-Mar-2023
  • (2022)BrowVis: Visualizing Large Graphs in the BrowserIEEE Access10.1109/ACCESS.2022.321888410(115776-115786)Online publication date: 2022
  • (2021)Language-agnostic integrated queries in a managed polyglot runtimeProceedings of the VLDB Endowment10.14778/3457390.345740514:8(1414-1426)Online publication date: 21-Oct-2021
  • (2020)Visualization Systems for Linked Datasets2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00171(1790-1793)Online publication date: Apr-2020
  • (2019)Understanding SPARQL Endpoints through Targeted Exploration and Visualization2019 First International Conference on Graph Computing (GC)10.1109/GC46384.2019.00012(21-28)Online publication date: Sep-2019

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