The case for PIQL: a performance insightful query language

  • Authors:

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

  • Affiliations:

  • UC Berkeley, Berkeley, CA, USA;UC Berkeley, Berkeley, CA, USA;UC Berkeley, Berkeley, CA, USA;UC Berkeley, Berkeley, CA, USA;UC Berkeley, Berkeley, CA, USA;UC Berkeley, Berkeley, CA, USA

  • Venue:
  • Proceedings of the 1st ACM symposium on Cloud computing
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Large-scale, user-facing applications 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. For complex queries, this approach often requires onerous explicit index management and imperative data lookup by the developer. We propose PIQL, a Performance Insightful Query Language that allows developers to express many queries found on these websites while still providing strict bounds on the number of I/O operations that will be performed.