Crescando

  • Authors:
  • Georgios Giannikis;Philipp Unterbrunner;Jeremy Meyer;Gustavo Alonso;Dietmar Fauser;Donald Kossmann

  • Affiliations:
  • ETH Zurich, Zurich, Switzerland;ETH Zurich, Zurich, Switzerland;Amadeus IT Group, Sophia Antipolis, France;ETH Zurich, Zurich, Switzerland;Amadeus IT Group, Sophia Antipolis, France;ETH Zurich, Zurich, Switzerland

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

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Abstract

This demonstration presents Crescando, an implementation of a distributed relational table that guarantees predictable response time on unpredictable workloads. In Crescando, data is stored in main memory and accessed via full-table scans. By using scans instead of index lookups, Crescando overcomes the read-write contention in index structures and eliminates the scalability issues that exist in traditional index-based systems. Crescando is specifically designed to process a large number of queries in parallel, allowing high query rates. The goal of this demonstration is to show the ability of Crescando to a) quickly answer arbitrary user-generated queries, and b) execute a large number of queries and updates in parallel, while providing strict response time and data freshness guarantees.