SnipSuggest: context-aware autocompletion for SQL

  • Authors:

  • Nodira Khoussainova;YongChul Kwon;Magdalena Balazinska;Dan Suciu

  • Affiliations:

  • University of Washington, Seattle, WA;University of Washington, Seattle, WA;University of Washington, Seattle, WA;University of Washington, Seattle, WA

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2010

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Abstract

In this paper, we present SnipSuggest, a system that provides on-the-go, context-aware assistance in the SQL composition process. SnipSuggest aims to help the increasing population of non-expert database users, who need to perform complex analysis on their large-scale datasets, but have difficulty writing SQL queries. As a user types a query, SnipSuggest recommends possible additions to various clauses in the query using relevant snippets collected from a log of past queries. SnipSuggest's current capabilities include suggesting tables, views, and table-valued functions in the FROM clause, columns in the SELECT clause, predicates in the WHERE clause, columns in the GROUP BY clause, aggregates, and some support for sub-queries. SnipSuggest adjusts its recommendations according to the context: as the user writes more of the query, it is able to provide more accurate suggestions. We evaluate SnipSuggest over two query logs: one from an undergraduate database class and another from the Sloan Digital Sky Survey database. We show that SnipSuggest is able to recommend useful snippets with up to 93.7% average precision, at interactive speed. We also show that SnipSuggest outperforms naïve approaches, such as recommending popular snippets.