Afterburner: The Case for In-Browser Analytics

TL;DR

This paper presents Afterburner, a JavaScript-based in-browser analytical RDBMS that achieves near-native performance, demonstrating JavaScript's potential for efficient, ubiquitous, and distributed interactive analytics within web browsers.

Contribution

Introducing Afterburner, the first in-browser RDBMS using JavaScript with compiled query plans, exploiting typed arrays and asm.js for high performance.

Findings

Afterburner achieves performance comparable to native MonetDB on simple queries.

JavaScript can serve as an efficient platform for in-browser analytics.

Potential for integrating in-browser analytics with notebooks and split-execution strategies.

Abstract

This paper 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 typed arrays and asm.js, two relatively recent advances in JavaScript. On a few simple queries, 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 discuss how our techniques could support ubiquitous in-browser interactive analytics (potentially integrating with browser-based notebooks) and also present interesting opportunities for split-execution strategies where query operators are distributed between the browser and backend servers.