PIQL: a performance insightful query language

  • Authors:

  • Michael Armbrust;Stephen Tu;Armando Fox;Michael J. Franklin;David A. Patterson;Nick Lanham;Beth Trushkowsky;Jesse Trutna

  • Affiliations:

  • University of California at Berkeley, Berkeley, CA, USA;University of California at Berkeley, Berkeley, CA, USA;University of California at Berkeley, Berkeley, CA, USA;University of California at Berkeley, Berkeley, CA, USA;University of California at Berkeley, Berkeley, CA, USA;University of California at Berkeley, Berkeley, CA, USA;University of California at Berkeley, Berkeley, CA, USA;University of California at Berkeley, Berkeley, CA, USA

  • Venue:
  • Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
  • Year:
  • 2010

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Abstract

Large-scale websites are increasingly moving from relational databases to distributed key-value stores for high request rate, low latency workloads. Often this move is motivated not only by key-value stores' ability to scale simply by adding more hardware, but also by the easy to understand predictable performance they provide for all operations. While this data model works well, lookups are only done by primary key. More complex queries require onerous, explicit index management and imperative data lookups by the developer. We demonstrate PIQL, a Performance Insightful Query Language that allows developers to express many of the queries found on these websites, while still providing strict bounds on the number of I/O operations for any query.