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Massive Technical Interviews Tips: Google Bigtable Architecture
src: 2.bp.blogspot.com

Bigtable is a compressed, high performance, and proprietary data storage system built on Google File System, Chubby Lock Service, SSTable (log-structured storage like LevelDB) and a few other Google technologies. On May 6, 2015, a public version of Bigtable was made available as a service. Bigtable also underlies Google Cloud Datastore, which is available as a part of the Google Cloud Platform.


Video Bigtable



History

Bigtable development began in 2004 and is now used by a number of Google applications, such as web indexing, MapReduce, which is often used for generating and modifying data stored in Bigtable, Google Maps, Google Book Search, "My Search History", Google Earth, Blogger.com, Google Code hosting, YouTube, and Gmail. Google's reasons for developing its own database include scalability and better control of performance characteristics.

Google's Spanner RDBMS is layered on an implementation of Bigtable with a Paxos group for two-phase commits to each table. Google F1 was built using Spanner to replace an implementation based on MySQL.


Maps Bigtable



Design

Bigtable maps two arbitrary string values (row key and column key) and timestamp (hence three-dimensional mapping) into an associated arbitrary byte array. It is not a relational database and can be better defined as a sparse, distributed multi-dimensional sorted map. Bigtable is designed to scale into the petabyte range across "hundreds or thousands of machines, and to make it easy to add more machines [to] the system and automatically start taking advantage of those resources without any reconfiguration".

Each table has multiple dimensions (one of which is a field for time, allowing for versioning and garbage collection). Tables are optimized for Google File System (GFS) by being split into multiple tablets - segments of the table are split along a row chosen such that the tablet will be ~200 megabytes in size. When sizes threaten to grow beyond a specified limit, the tablets are compressed using the algorithm BMDiff and the Zippy compression algorithm publicly known and open-sourced as Snappy, which is a less space-optimal variation of LZ77 but more efficient in terms of computing time. The locations in the GFS of tablets are recorded as database entries in multiple special tablets, which are called "META1" tablets. META1 tablets are found by querying the single "META0" tablet, which typically resides on a server of its own since it is often queried by clients as to the location of the "META1" tablet which itself has the answer to the question of where the actual data is located. Like GFS's master server, the META0 server is not generally a bottleneck since the processor time and bandwidth necessary to discover and transmit META1 locations is minimal and clients aggressively cache locations to minimize queries.


Massive Technical Interviews Tips: Google Bigtable Architecture
src: 2.bp.blogspot.com


Other similar software

  • Apache Accumulo -- built on top of Hadoop, ZooKeeper, and Thrift. Has cell-level access labels and a server-side programming mechanism. Written in Java.
  • Apache Cassandra -- brings together Dynamo's fully distributed design and Bigtable's data model. Written in Java.
  • Apache HBase -- Provides Bigtable-like support on the Hadoop Core. Has cell-level access labels and a server-side programming mechanism too. Written in Java.
  • Hypertable -- Hypertable is designed to manage the storage and processing of information on a large cluster of commodity servers. Written in C++.
  • "KDI", Bluefish, GitHub  -- Kosmix attempt to make a Bigtable clone. Written in C++.

Area Domus - Big Table
src: www.areadomus.gr


See also

  • Amazon SimpleDB
  • Big data
  • Distributed data stores, an overview
  • Dynamo (storage system)
  • Column-oriented DBMS
  • Hadoop
  • CouchDB

An introduction to Google Big Table - YouTube
src: i.ytimg.com


References


Google BigTable: Architecture fs metadata,
src: image.slidesharecdn.com


Bibliography

  • Chang, Fay; Dean, Jeffrey; Ghemawat, Sanjay; Hsieh, Wilson C; Wallach, Deborah A; Burrows, Michael 'Mike'; Chandra, Tushar; Fikes, Andrew; Gruber, Robert E (2006), "Bigtable: A Distributed Storage System for Structured Data", (download ebook) (PDF), Google .

Automatic Big Table Caching - YouTube
src: i.ytimg.com


External links

  • Bigtable: A Distributed Structured Storage System, Washington . Video, Google .
    • UWTV, archived from the original (video) on 15 June 2006 .
    • Witchcock, Andrew, Google's Bigtable (notes on the official presentation) .
  • Carr, David F (2006-07-06), "How Google Works", Baseline .
  • "Is the Relational Database Doomed?", Read-write web .

Source of the article : Wikipedia

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