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title: Bigtable - Wikipedia
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> **Original source:** https://en.wikipedia.org/wiki/Bigtable?utm_source=openai

From Wikipedia, the free encyclopedia

Google Bigtable

[Developer](https://en.wikipedia.org/wiki/Programmer "Programmer")

[Google](https://en.wikipedia.org/wiki/Google "Google")

Release

February 2005; 21 years ago

Written in

[C++](https://en.wikipedia.org/wiki/C++ "C++") (core), [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [Go](https://en.wikipedia.org/wiki/Go_\(programming_language\) "Go (programming language)"), [Ruby](https://en.wikipedia.org/wiki/Ruby_\(programming_language\) "Ruby (programming language)")

[Platform](https://en.wikipedia.org/wiki/Computing_platform "Computing platform")

[Google Cloud Platform](https://en.wikipedia.org/wiki/Google_Cloud_Platform "Google Cloud Platform")

[Type](https://en.wikipedia.org/wiki/Software_categories#Categorization_approaches "Software categories")

[NoSQL](https://en.wikipedia.org/wiki/NoSQL "NoSQL") [Database](https://en.wikipedia.org/wiki/Database "Database"), [data store](https://en.wikipedia.org/wiki/Data_store "Data store")

[License](https://en.wikipedia.org/wiki/Software_license "Software license")

[Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software")

Website

[cloud.google.com/bigtable/](https://cloud.google.com/bigtable/)

**Bigtable** is a fully managed [wide-column](https://en.wikipedia.org/wiki/Wide-column_store "Wide-column store") and [key-value](https://en.wikipedia.org/wiki/Key-value "Key-value") [NoSQL](https://en.wikipedia.org/wiki/NoSQL "NoSQL") database service for large analytical and operational workloads as part of the [Google Cloud](https://en.wikipedia.org/wiki/Google_Cloud "Google Cloud") portfolio.

Bigtable development began in 2004.[\[1\]](#cite_note-1) It is now used by a number of Google applications, such as [Google Analytics](https://en.wikipedia.org/wiki/Google_Analytics "Google Analytics"),[\[2\]](#cite_note-:0-2) web indexing,[\[3\]](#cite_note-FOOTNOTEChangDeanGhemawatHsieh2006-3) [MapReduce](https://en.wikipedia.org/wiki/MapReduce "MapReduce"), which is often used for generating and modifying data stored in Bigtable,[\[4\]](#cite_note-FOOTNOTEChangDeanGhemawatHsieh20063-4) [Google Maps](https://en.wikipedia.org/wiki/Google_Maps "Google Maps"),[\[5\]](#cite_note-5) [Google Books](https://en.wikipedia.org/wiki/Google_Books "Google Books") search, "My Search History", [Google Earth](https://en.wikipedia.org/wiki/Google_Earth "Google Earth"), [Blogger.com](https://en.wikipedia.org/wiki/Blogger.com "Blogger.com"), [Google Code](https://en.wikipedia.org/wiki/Google_Code "Google Code") hosting, [YouTube](https://en.wikipedia.org/wiki/YouTube "YouTube"),[\[6\]](#cite_note-6) and [Gmail](https://en.wikipedia.org/wiki/Gmail "Gmail").[\[7\]](#cite_note-7) Google's reasons for developing its own database include scalability and better control of performance characteristics.[\[8\]](#cite_note-FOOTNOTEChangDeanGhemawatHsieh2006Conclusion-8)

[Apache HBase](https://en.wikipedia.org/wiki/Apache_HBase "Apache HBase") and [Cassandra](https://en.wikipedia.org/wiki/Apache_Cassandra "Apache Cassandra") are some of the best known open source projects that were modeled after Bigtable. Bigtable offers [HBase](https://cloud.google.com/bigtable/docs/hbase-bigtable) and [Cassandra compatible APIs](https://cloud.google.com/bigtable/docs/migrate-from-cassandra).

On 6 May 2015, a public version of Bigtable was made available as a part of [Google Cloud](https://en.wikipedia.org/wiki/Google_Cloud_Platform "Google Cloud Platform") under the name Cloud Bigtable.**[\[2\]](#cite_note-:0-2)**

As of April 2024, Bigtable manages over 10 Exabytes of data and serves more than 7 billion requests per second.[\[9\]](#cite_note-9) Since its launch, Google announced a number of updates to Bigtable, including [SQL support](https://cloud.google.com/bigtable/docs/googlesql-overview), [incremental materialized views](https://cloud.google.com/bigtable/docs/continuous-materialized-views), [global secondary indexes](https://cloud.google.com/bigtable/docs/global-secondary-index) and automated scalability.[\[10\]](#cite_note-10)

Bigtable is one of the prototypical examples of a [wide-column store](https://en.wikipedia.org/wiki/Wide-column_store "Wide-column store"). It 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.[\[3\]](#cite_note-FOOTNOTEChangDeanGhemawatHsieh2006-3): 1  It is built on Colossus ([Google File System](https://en.wikipedia.org/wiki/Google_File_System "Google File System")), [Chubby Lock Service](https://en.wikipedia.org/wiki/Distributed_lock_manager#Other_implementations "Distributed lock manager"), SSTable (log-structured storage like [LevelDB](https://en.wikipedia.org/wiki/LevelDB "LevelDB")) and a few other [Google](https://en.wikipedia.org/wiki/Google "Google") technologies. Bigtable is designed to scale into the [petabyte](https://en.wikipedia.org/wiki/Petabyte "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".[\[11\]](#cite_note-o'reilly-11) For example, Google's copy of the web can be stored in a bigtable where the row key is a [domain-reversed URL](https://en.wikipedia.org/wiki/Reverse_domain_name_notation "Reverse domain name notation"), and columns describe various properties of a web page, with one particular column holding the page itself. The page column can have several timestamped versions describing different copies of the web page timestamped by when they were fetched. Each cell of a bigtable can have zero or more timestamped versions of the data. Another function of the timestamp is to allow for both [versioning](https://en.wikipedia.org/wiki/Version_control "Version control") and [garbage collection](https://en.wikipedia.org/wiki/Garbage_collection_\(computer_science\) "Garbage collection (computer science)") of expired data.

Tables are split into multiple _tablets_ – segments of the table are split at certain row keys so that each tablet is a few hundred megabytes or a few gigabytes in size. A bigtable is somewhat like a mapreduce worker pool in that thousands to hundreds of thousands of tablet shards may be served by hundreds to thousands of BigTable servers. When Table size threaten to grow beyond a specified limit, the tablets may be compressed using the algorithm BMDiff[\[12\]](#cite_note-algorithms1-12)[\[13\]](#cite_note-13) and the Zippy compression algorithm[\[14\]](#cite_note-algorithms2-14) publicly known and open-sourced as [Snappy](https://en.wikipedia.org/wiki/Snappy_\(compression\) "Snappy (compression)"),[\[15\]](#cite_note-15) which is a less space-optimal variation of [LZ77](https://en.wikipedia.org/wiki/LZ77 "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](https://en.wikipedia.org/wiki/Bottleneck_\(software\) "Bottleneck (software)") since the processor time and bandwidth necessary to discover and transmit META1 locations is minimal and clients aggressively cache locations to minimize queries.

1.  [↑](#cite_ref-1) Hitchcock, Andrew (2005). ["Google's Bigtable"](https://andrewhitchcock.org/2005/bigtable.html). Retrieved 29 July 2007. First an overview. Bigtable has been in development since early 2004 and has been in active use for about eight months (about February 2005).
2.  [1](#cite_ref-:0_2-0) [2](#cite_ref-:0_2-1) ["Announcing Google Cloud Bigtable: The same database that powers Google Search, Gmail and Analytics is now available on Google Cloud Platform"](https://googlecloudplatform.blogspot.com/2015/05/introducing-Google-Cloud-Bigtable.html). _Google Blog_. 6 May 2015. Retrieved 21 September 2016.
3.  [1](#cite_ref-FOOTNOTEChangDeanGhemawatHsieh2006_3-0) [2](#cite_ref-FOOTNOTEChangDeanGhemawatHsieh2006_3-1) [Chang et al. 2006](#CITEREFChangDeanGhemawatHsieh2006).
4.  [↑](#cite_ref-FOOTNOTEChangDeanGhemawatHsieh20063_4-0) [Chang et al. 2006](#CITEREFChangDeanGhemawatHsieh2006), p. 3: 'Bigtable can be used with MapReduce, a framework for running large-scale parallel computations developed at Google. We have written a set of wrappers that allow a Bigtable to be used both as an input source and as an output target for MapReduce jobs'
5.  [↑](#cite_ref-5) Hitchcock, Andrew (2005). ["Google's Bigtable"](https://andrewhitchcock.org/2005/bigtable.html). Retrieved 29 July 2007. There are currently around 100 cells for services such as Print, Search History, Maps, and Orkut.
6.  [↑](#cite_ref-6) Cordes, Kyle (12 July 2007). ["YouTube Scalability Talk"](https://kylecordes.com/2007/youtube-scalability). Their new solution for thumbnails is to use Google's Bigtable, which provides high performance for a large number of rows, fault tolerance, caching, etc. This is a nice (and rare?) example of actual synergy in an acquisition.
7.  [↑](#cite_ref-7) "How Entities and Indexes are Stored", [_Google App Engine_](https://web.archive.org/web/20120107215117/http://code.google.com/intl/pl/appengine/articles/storage_breakdown.html#anc-background), Google Code, archived from [the original](https://code.google.com/intl/pl/appengine/articles/storage_breakdown.html#anc-background) on 7 January 2012, retrieved 17 April 2014.
8.  [↑](#cite_ref-FOOTNOTEChangDeanGhemawatHsieh2006Conclusion_8-0) [Chang et al. 2006](#CITEREFChangDeanGhemawatHsieh2006), Conclusion: 'We have described Bigtable, a distributed system for storing structured data at Google... Our users like the performance and high availability provided by the Bigtable implementation, and that they can scale the capacity of their clusters by simply adding more machines to the system as their resource demands change over time... Finally, we have found that there are significant advantages to building our own storage solution at Google. We have gotten a substantial amount of flexibility from designing our own data model for Bigtable.'
9.  [↑](#cite_ref-9) ["Celebrating 20 years of Bigtable with exciting announcements at Next"](https://cloud.google.com/blog/products/databases/bigtable-enhancements-at-next24). _Google Cloud Blog_. 11 April 2024.
10.  [↑](#cite_ref-10) Kerner, Sean Michael (27 January 2022). ["Google scales up Cloud Bigtable NoSQL database"](https://www.techtarget.com/searchdatamanagement/news/252512583/Google-scales-up-Cloud-Bigtable-NoSQL-database). _[TechTarget](https://en.wikipedia.org/wiki/TechTarget "TechTarget")_. Retrieved 10 October 2022.
11.  [↑](#cite_ref-o'reilly_11-0) "Google File System and Bigtable", [_Radar_](http://radar.oreilly.com/2006/05/database-war-stories-7-google.html) ([World Wide Web](https://en.wikipedia.org/wiki/World_Wide_Web "World Wide Web") log), Database War Stories, O'Reilly, May 2006.
12.  [↑](#cite_ref-algorithms1_12-0) ["Google Bigtable, Compression, Zippy and BMDiff"](https://web.archive.org/web/20130501020631/http://feedblog.org/2008/10/12/google-bigtable-compression-zippy-and-bmdiff/). 12 October 2008. Archived from [the original](http://feedblog.org/2008/10/12/google-bigtable-compression-zippy-and-bmdiff) on 1 May 2013. Retrieved 14 April 2015..
13.  [↑](#cite_ref-13) Bentley, Jon; McIlroy, Douglas (1999). _Data compression using long common strings_. DCC '99: Proceedings of the Conference on Data Compression. IEEE Computer Society. [CiteSeerX](https://en.wikipedia.org/wiki/CiteSeerX_\(identifier\) "CiteSeerX (identifier)") [10.1.1.11.8470](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.8470). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1109/DCC.1999.755678](https://doi.org/10.1109%2FDCC.1999.755678).
14.  [↑](#cite_ref-algorithms2_14-0) "Google's Bigtable", [_Outer court_](https://blogoscoped.com/archive/2005-10-23-n61.html) (Weblog), 23 October 2005.
15.  [↑](#cite_ref-15) ["snappy"](https://google.github.io/snappy/). _google.github.io_.

-   Chang, Fay; Dean, Jeffrey; Ghemawat, Sanjay; Hsieh, Wilson C.; Wallach, Deborah A.; Burrows, Mike; Chandra, Tushar; Fikes, Andrew; Gruber, Robert E. (2006). [_Bigtable: A Distributed Storage System for Structured Data_](https://static.googleusercontent.com/media/research.google.com/en//archive/bigtable-osdi06.pdf) (PDF). [7th USENIX Symposium on Operating Systems Design and Implementation](https://en.wikipedia.org/wiki/Operating_Systems_Design_and_Implementation "Operating Systems Design and Implementation") (OSDI'06). Seattle, WA.

-   [_Bigtable: A Distributed Structured Storage System_](http://www.cs.washington.edu/htbin-post/mvis/mvis?ID=437), Washington. [_Video_](https://web.archive.org/web/20110418072707/http://video.google.com/videoplay?docid=7278544055668715642), archived from [the original](http://video.google.com/videoplay?docid=7278544055668715642) on 18 April 2011, retrieved 11 July 2006.
    -   [_UWTV_](https://web.archive.org/web/20060615164406/http://www.uwtv.org/programs/displayevent.asp?rid=2787), archived from [the original](http://www.uwtv.org/programs/displayevent.asp?rid=2787) (video) on 15 June 2006.
    -   Hitchcock, Andrew, [_Google's Bigtable_](https://andrewhitchcock.org/2005/bigtable.html) (notes on the official presentation), retrieved 29 July 2007.
-   Carr, David F (6 July 2006), ["How Google Works"](https://www.baselinemag.com/c/a/Infrastructure/How-Google-Works-1), _Baseline_\[_[dead link](https://en.wikipedia.org/wiki/Wikipedia:Link_rot "Wikipedia:Link rot")_\].
-   "Is the Relational Database Doomed?", [_Read-write web_](https://web.archive.org/web/20121110100338/http://readwrite.com/2009/02/12/is-the-relational-database-doomed), 12 February 2009, archived from the original on 10 November 2012.