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Parallel database systems: the future of high performance database systems
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The Zebra striped network file system
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IBM Systems Journal
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Acta Informatica
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OSDI '99 Proceedings of the third symposium on Operating systems design and implementation
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ACM Computing Surveys (CSUR)
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SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Mariposa: A New Architecture for Distributed Data
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Notes on Data Base Operating Systems
Operating Systems, An Advanced Course
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DCC '99 Proceedings of the Conference on Data Compression
Tapestry: An Infrastructure for Fault-tolerant Wide-area Location and
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SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
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Scientific Programming - Dynamic Grids and Worldwide Computing
Operating system support for planetary-scale network services
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
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Paxos made live: an engineering perspective
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International Journal of Metadata, Semantics and Ontologies
Engineering of Software-Intensive Systems: State of the Art and Research Challenges
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IE '08 Proceedings of the 5th Australasian Conference on Interactive Entertainment
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A distributed architecture for data mining and integration
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ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
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PREFAIL: a programmable tool for multiple-failure injection
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Depot: Cloud Storage with Minimal Trust
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TransMR: data-centric programming beyond data parallelism
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Ramos: Concurrent writing and reconfiguration for collaborative systems
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Scalable load balancing in cluster storage systems
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bLSM: a general purpose log structured merge tree
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Large-scale machine learning at twitter
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Structured comparative analysis of systems logs to diagnose performance problems
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Rya: a scalable RDF triple store for the clouds
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Hi-index | 0.06 |
Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this paper we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.