Tenzing a SQL implementation on the MapReduce framework
Authors:
Open Access
- 1 August 2011
- journal article
- Published by Association for Computing Machinery (ACM) in Proceedings of the VLDB Endowment
- Vol. 4 (12) , 1318-1327
Abstract
Tenzing is a query engine built on top of MapReduce [9] for ad hoc analysis of Google data. Tenzing supports a mostly complete SQL implementation (with several extensions) combined with several key characteristics such as heterogeneity, high performance, scalability, reliability, metadata awareness, low latency, support for columnar storage and structured data, and easy extensibility. Tenzing is currently used internally at Google by 1000+ employees and serves 10000+ queries per day over 1.5 petabytes of compressed data. In this paper, we describe the architecture and implementation of Tenzing, and present benchmarks of typical analytical queries.Keywords
This publication has 18 references indexed in Scilit:
- MapReduceCommunications of the ACM, 2010
- MapReduce and parallel DBMSsCommunications of the ACM, 2010
- HadoopDBProceedings of the VLDB Endowment, 2009
- HiveProceedings of the VLDB Endowment, 2009
- SQL/MapReduceProceedings of the VLDB Endowment, 2009
- SCOPEProceedings of the VLDB Endowment, 2008
- BigtableACM Transactions on Computer Systems, 2008
- MapReduceCommunications of the ACM, 2008
- DryadACM SIGOPS Operating Systems Review, 2007
- The Google file systemACM SIGOPS Operating Systems Review, 2003