Tenzing a sql implementation on the mapreduce framework
B Chattopadhyay, L Lin, W Liu, S Mittal… - Proceedings of the …, 2011 - dl.acm.org
B Chattopadhyay, L Lin, W Liu, S Mittal, P Aragonda, V Lychagina, Y Kwon, M Wong
Proceedings of the VLDB Endowment, 2011•dl.acm.orgTenzing 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 …
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 …
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.
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