Google’s Bigtable
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. This describes the simple data model provided by
Bigtable, which gives clients dynamic control over data layout and format, and
also describe the design and implementation of Bigtable.
Over the last two and a half years Google designed, implemented, and
deployed a distributed storage system for managing structured data called
Bigtable. Bigtable is designed to reliably scale to petabytes of data and
thousands of machines. Bigtable has achieved several goals: wide applicability,
scalability, high performance, and high availability. Bigtable is used by more
than sixty Google products and projects, including Google Analytics, Google
Finance, Orkut, Personalized Search and Google Earth. These products use
Bigtable for a variety of demanding workloads, which range from
throughput-oriented batch-processing jobs to latency-sensitive serving of data
to end users. The Bigtable clusters used by these products span a wide range of
configurations, from a handful to thousands of servers, and store up to several
hundred terabytes of data.
You can download Google’s Bigtable seminar abstract from here.
9 Sep 2013
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