What database does Google use

Question

Is it Oracle or MySQL or something they have built themselves

User · Answer

Although Google uses BigTable for all their main applications  they also use MySQL for other  perhaps minor  apps

User · Answer

It s something they ve built themselves - it s called Bigtable   http   en wikipedia org wiki BigTable  There is a paper by Google on the database   http   research google com archive bigtable html

User · Answer

It s something they ve built themselves - it s called Bigtable   http   en wikipedia org wiki BigTable  There is a paper by Google on the database   http   research google com archive bigtable html

User · Answer

Google services have a polyglot persistence architecture  BigTable is leveraged by most of its services like YouTube  Google Search  Google Analytics etc  The search service initially used MapReduce for its indexing infrastructure but later transitioned to BigTable during the Caffeine release    Google Cloud datastore has over 100 applications in production at Google both facing internal and external users  Applications like Gmail  Picasa  Google Calendar  Android Market  amp  AppEngine use Cloud Datastore  amp  Megastore   Google Trends use MillWheel for stream processing  Google Ads initially used MySQL later migrated to F1 DB - a custom written distributed relational database  Youtube uses MySQL with Vitess  Google stores exabytes of data across the commodity servers with the help of the Google File System    Source  Google Databases  How Do Google Services Store Petabyte-Exabyte Scale Data   YouTube Database     How Does It Store So Many Videos Without Running Out Of Storage Space

User · Answer

And it s maybe also handy to know that BigTable is not a relational database  like MySQL  but a huge  distributed  hash table which has very different characteristics  You can play around with  a limited version  of BigTable yourself on the Google AppEngine platform   Next to Hadoop mentioned above there are many other implementations that try to solve the same problems as BigTable  scalability  availability   I saw a nice blog post yesterday listing most of them here

User · Answer

As others have mentioned  Google uses a homegrown solution called BigTable and they ve released a few papers describing it out into the real world    The Apache folks have an implementation of the ideas presented in these papers called HBase  HBase is part of the larger Hadoop project which according to their site  is a software platform that lets one easily write and run applications that process vast amounts of data   Some of the benchmarks are quite impressive  Their site is at http   hadoop apache org

User · Answer

Google services have a polyglot persistence architecture  BigTable is leveraged by most of its services like YouTube  Google Search  Google Analytics etc  The search service initially used MapReduce for its indexing infrastructure but later transitioned to BigTable during the Caffeine release    Google Cloud datastore has over 100 applications in production at Google both facing internal and external users  Applications like Gmail  Picasa  Google Calendar  Android Market  amp  AppEngine use Cloud Datastore  amp  Megastore   Google Trends use MillWheel for stream processing  Google Ads initially used MySQL later migrated to F1 DB - a custom written distributed relational database  Youtube uses MySQL with Vitess  Google stores exabytes of data across the commodity servers with the help of the Google File System    Source  Google Databases  How Do Google Services Store Petabyte-Exabyte Scale Data   YouTube Database     How Does It Store So Many Videos Without Running Out Of Storage Space

User · Answer

Google primarily uses Bigtable   Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size   For more information  download the document from here   Google also uses Oracle and MySQL databases for some of their applications    Any more information you can add is highly appreciated

User · Answer

And it s maybe also handy to know that BigTable is not a relational database  like MySQL  but a huge  distributed  hash table which has very different characteristics  You can play around with  a limited version  of BigTable yourself on the Google AppEngine platform   Next to Hadoop mentioned above there are many other implementations that try to solve the same problems as BigTable  scalability  availability   I saw a nice blog post yesterday listing most of them here

User · Answer

As others have mentioned  Google uses a homegrown solution called BigTable and they ve released a few papers describing it out into the real world    The Apache folks have an implementation of the ideas presented in these papers called HBase  HBase is part of the larger Hadoop project which according to their site  is a software platform that lets one easily write and run applications that process vast amounts of data   Some of the benchmarks are quite impressive  Their site is at http   hadoop apache org

User · Answer

As others have mentioned  Google uses a homegrown solution called BigTable and they ve released a few papers describing it out into the real world    The Apache folks have an implementation of the ideas presented in these papers called HBase  HBase is part of the larger Hadoop project which according to their site  is a software platform that lets one easily write and run applications that process vast amounts of data   Some of the benchmarks are quite impressive  Their site is at http   hadoop apache org

User · Answer

Bigtable  A Distributed Storage System for Structured Data     Bigtable is a distributed storage   system  built by Google  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    Some features   fast and extremely large-scale DBMS a sparse  distributed multi-dimensional sorted map  sharing characteristics of both row-oriented and column-oriented databases  designed to scale into the petabyte range it works across hundreds or thousands of machines it is 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 versioning  tables are optimized for GFS  Google File System  by being split into multiple tablets - segments of the table as split along a row chosen such that the tablet will be  200 megabytes in size    Architecture  BigTable is not a relational database  It does not support joins nor does it support rich SQL-like queries  Each table is a multidimensional sparse map  Tables consist of rows and columns  and each cell has a time stamp  There can be multiple versions of a cell with different time stamps  The time stamp allows for operations such as  select  n  versions of this Web page  or  delete cells that are older than a specific date time     In order to manage the huge tables  Bigtable splits tables at row boundaries and saves them as tablets  A tablet is around 200 MB  and each machine saves about 100 tablets  This setup allows tablets from a single table to be spread among many servers  It also allows for fine-grained load balancing  If one table is receiving many queries  it can shed other tablets or move the busy table to another machine that is not so busy  Also  if a machine goes down  a tablet may be spread across many other servers so that the performance impact on any given machine is minimal   Tables are stored as immutable SSTables and a tail of logs  one log per machine   When a machine runs out of system memory  it compresses some tablets using Google proprietary compression techniques  BMDiff and Zippy   Minor compactions involve only a few tablets  while major compactions involve the whole table system and recover hard-disk space   The locations of Bigtable tablets are stored in cells  The lookup of any particular tablet is handled by a three-tiered system  The clients get a point to a META0 table  of which there is only one  The META0 table keeps track of many META1 tablets that contain the locations of the tablets being looked up  Both META0 and META1 make heavy use of pre-fetching and caching to minimize bottlenecks in the system   Implementation  BigTable is built on Google File System  GFS   which is used as a backing store for log and data files  GFS provides reliable storage for SSTables  a Google-proprietary file format used to persist table data   Another service that BigTable makes heavy use of is Chubby  a highly-available  reliable distributed lock service  Chubby allows clients to take a lock  possibly associating it with some metadata  which it can renew by sending keep alive messages back to Chubby  The locks are stored in a filesystem-like hierarchical naming structure   There are three primary server types of interest in the Bigtable system    Master servers  assign tablets to tablet servers  keeps track of where tablets are located and redistributes tasks as needed   Tablet servers  handle read write requests for tablets and split tablets when they exceed size limits  usually 100MB - 200MB   If a tablet server fails  then a 100 tablet servers each pickup 1 new tablet and the system recovers   Lock servers  instances of the Chubby distributed lock service  Lots of actions within BigTable require acquisition of locks including opening tablets for writing  ensuring that there is no more than one active Master at a time  and access control checking    Example from Google s research paper        A slice of an example table that   stores Web pages  The row name is a   reversed URL  The contents column   family contains the page contents  and   the anchor column family contains the   text of any anchors that reference the   page  CNN s home page is referenced by   both the Sports Illustrated and the   MY-look home pages  so the row   contains columns named   anchor cnnsi com and   anchor my look ca  Each anchor cell   has one version  the contents column   has three versions  at timestamps   t3  t5  and t6    API  Typical operations to BigTable are creation and deletion of tables and column families  writing data and deleting columns from a row  BigTable provides this functions to application developers in an API  Transactions are supported at the row level  but not across several row keys     Here is the link to the PDF of the research paper    And here you can find a video showing Google s Jeff Dean in a lecture at the University of Washington  discussing the Bigtable content storage system used in Google s backend

User · Answer

Spanner is Google s globally distributed relational database management system  RDBMS   the successor to BigTable  Google claims it is not a pure relational system because each table must have a primary key   Here is the link of the paper      Spanner is Google s scalable  multi-version  globally-distributed  and   synchronously-replicated database  It is the first system to   distribute data at global scale and support externally-consistent   distributed transactions  This paper describes how Spanner is   structured  its feature set  the rationale underlying various design   decisions  and a novel time API that exposes clock uncertainty  This   API and its implementation are critical to supporting external   consistency and a variety of powerful features  non-blocking reads in   the past  lock-free read-only transactions  and atomic schema changes    across all of Spanner    Another database invented by Google is Megastore  Here is the abstract      Megastore is a storage system developed to meet the requirements of   today s interactive online services  Megastore blends the scalability   of a NoSQL datastore with the convenience of a traditional RDBMS in a   novel way  and provides both strong consistency guarantees and high   availability  We provide fully serializable ACID semantics within   fine-grained partitions of data  This partitioning allows us to   synchronously replicate each write across a wide area network with   reasonable latency and support seamless failover between datacenters    This paper describes Megastore s semantics and replication algorithm    It also describes our experience supporting a wide range of Google   production services built with Megastore

User · Answer

And it s maybe also handy to know that BigTable is not a relational database  like MySQL  but a huge  distributed  hash table which has very different characteristics  You can play around with  a limited version  of BigTable yourself on the Google AppEngine platform   Next to Hadoop mentioned above there are many other implementations that try to solve the same problems as BigTable  scalability  availability   I saw a nice blog post yesterday listing most of them here

User · Answer

Although Google uses BigTable for all their main applications  they also use MySQL for other  perhaps minor  apps

User · Answer

It s something they ve built themselves - it s called Bigtable   http   en wikipedia org wiki BigTable  There is a paper by Google on the database   http   research google com archive bigtable html

User · Answer

Bigtable  A Distributed Storage System for Structured Data     Bigtable is a distributed storage   system  built by Google  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    Some features   fast and extremely large-scale DBMS a sparse  distributed multi-dimensional sorted map  sharing characteristics of both row-oriented and column-oriented databases  designed to scale into the petabyte range it works across hundreds or thousands of machines it is 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 versioning  tables are optimized for GFS  Google File System  by being split into multiple tablets - segments of the table as split along a row chosen such that the tablet will be  200 megabytes in size    Architecture  BigTable is not a relational database  It does not support joins nor does it support rich SQL-like queries  Each table is a multidimensional sparse map  Tables consist of rows and columns  and each cell has a time stamp  There can be multiple versions of a cell with different time stamps  The time stamp allows for operations such as  select  n  versions of this Web page  or  delete cells that are older than a specific date time     In order to manage the huge tables  Bigtable splits tables at row boundaries and saves them as tablets  A tablet is around 200 MB  and each machine saves about 100 tablets  This setup allows tablets from a single table to be spread among many servers  It also allows for fine-grained load balancing  If one table is receiving many queries  it can shed other tablets or move the busy table to another machine that is not so busy  Also  if a machine goes down  a tablet may be spread across many other servers so that the performance impact on any given machine is minimal   Tables are stored as immutable SSTables and a tail of logs  one log per machine   When a machine runs out of system memory  it compresses some tablets using Google proprietary compression techniques  BMDiff and Zippy   Minor compactions involve only a few tablets  while major compactions involve the whole table system and recover hard-disk space   The locations of Bigtable tablets are stored in cells  The lookup of any particular tablet is handled by a three-tiered system  The clients get a point to a META0 table  of which there is only one  The META0 table keeps track of many META1 tablets that contain the locations of the tablets being looked up  Both META0 and META1 make heavy use of pre-fetching and caching to minimize bottlenecks in the system   Implementation  BigTable is built on Google File System  GFS   which is used as a backing store for log and data files  GFS provides reliable storage for SSTables  a Google-proprietary file format used to persist table data   Another service that BigTable makes heavy use of is Chubby  a highly-available  reliable distributed lock service  Chubby allows clients to take a lock  possibly associating it with some metadata  which it can renew by sending keep alive messages back to Chubby  The locks are stored in a filesystem-like hierarchical naming structure   There are three primary server types of interest in the Bigtable system    Master servers  assign tablets to tablet servers  keeps track of where tablets are located and redistributes tasks as needed   Tablet servers  handle read write requests for tablets and split tablets when they exceed size limits  usually 100MB - 200MB   If a tablet server fails  then a 100 tablet servers each pickup 1 new tablet and the system recovers   Lock servers  instances of the Chubby distributed lock service  Lots of actions within BigTable require acquisition of locks including opening tablets for writing  ensuring that there is no more than one active Master at a time  and access control checking    Example from Google s research paper        A slice of an example table that   stores Web pages  The row name is a   reversed URL  The contents column   family contains the page contents  and   the anchor column family contains the   text of any anchors that reference the   page  CNN s home page is referenced by   both the Sports Illustrated and the   MY-look home pages  so the row   contains columns named   anchor cnnsi com and   anchor my look ca  Each anchor cell   has one version  the contents column   has three versions  at timestamps   t3  t5  and t6    API  Typical operations to BigTable are creation and deletion of tables and column families  writing data and deleting columns from a row  BigTable provides this functions to application developers in an API  Transactions are supported at the row level  but not across several row keys     Here is the link to the PDF of the research paper    And here you can find a video showing Google s Jeff Dean in a lecture at the University of Washington  discussing the Bigtable content storage system used in Google s backend

User · Answer

As others have mentioned  Google uses a homegrown solution called BigTable and they ve released a few papers describing it out into the real world    The Apache folks have an implementation of the ideas presented in these papers called HBase  HBase is part of the larger Hadoop project which according to their site  is a software platform that lets one easily write and run applications that process vast amounts of data   Some of the benchmarks are quite impressive  Their site is at http   hadoop apache org

User · Answer

Bigtable  A Distributed Storage System for Structured Data     Bigtable is a distributed storage   system  built by Google  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    Some features   fast and extremely large-scale DBMS a sparse  distributed multi-dimensional sorted map  sharing characteristics of both row-oriented and column-oriented databases  designed to scale into the petabyte range it works across hundreds or thousands of machines it is 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 versioning  tables are optimized for GFS  Google File System  by being split into multiple tablets - segments of the table as split along a row chosen such that the tablet will be  200 megabytes in size    Architecture  BigTable is not a relational database  It does not support joins nor does it support rich SQL-like queries  Each table is a multidimensional sparse map  Tables consist of rows and columns  and each cell has a time stamp  There can be multiple versions of a cell with different time stamps  The time stamp allows for operations such as  select  n  versions of this Web page  or  delete cells that are older than a specific date time     In order to manage the huge tables  Bigtable splits tables at row boundaries and saves them as tablets  A tablet is around 200 MB  and each machine saves about 100 tablets  This setup allows tablets from a single table to be spread among many servers  It also allows for fine-grained load balancing  If one table is receiving many queries  it can shed other tablets or move the busy table to another machine that is not so busy  Also  if a machine goes down  a tablet may be spread across many other servers so that the performance impact on any given machine is minimal   Tables are stored as immutable SSTables and a tail of logs  one log per machine   When a machine runs out of system memory  it compresses some tablets using Google proprietary compression techniques  BMDiff and Zippy   Minor compactions involve only a few tablets  while major compactions involve the whole table system and recover hard-disk space   The locations of Bigtable tablets are stored in cells  The lookup of any particular tablet is handled by a three-tiered system  The clients get a point to a META0 table  of which there is only one  The META0 table keeps track of many META1 tablets that contain the locations of the tablets being looked up  Both META0 and META1 make heavy use of pre-fetching and caching to minimize bottlenecks in the system   Implementation  BigTable is built on Google File System  GFS   which is used as a backing store for log and data files  GFS provides reliable storage for SSTables  a Google-proprietary file format used to persist table data   Another service that BigTable makes heavy use of is Chubby  a highly-available  reliable distributed lock service  Chubby allows clients to take a lock  possibly associating it with some metadata  which it can renew by sending keep alive messages back to Chubby  The locks are stored in a filesystem-like hierarchical naming structure   There are three primary server types of interest in the Bigtable system    Master servers  assign tablets to tablet servers  keeps track of where tablets are located and redistributes tasks as needed   Tablet servers  handle read write requests for tablets and split tablets when they exceed size limits  usually 100MB - 200MB   If a tablet server fails  then a 100 tablet servers each pickup 1 new tablet and the system recovers   Lock servers  instances of the Chubby distributed lock service  Lots of actions within BigTable require acquisition of locks including opening tablets for writing  ensuring that there is no more than one active Master at a time  and access control checking    Example from Google s research paper        A slice of an example table that   stores Web pages  The row name is a   reversed URL  The contents column   family contains the page contents  and   the anchor column family contains the   text of any anchors that reference the   page  CNN s home page is referenced by   both the Sports Illustrated and the   MY-look home pages  so the row   contains columns named   anchor cnnsi com and   anchor my look ca  Each anchor cell   has one version  the contents column   has three versions  at timestamps   t3  t5  and t6    API  Typical operations to BigTable are creation and deletion of tables and column families  writing data and deleting columns from a row  BigTable provides this functions to application developers in an API  Transactions are supported at the row level  but not across several row keys     Here is the link to the PDF of the research paper    And here you can find a video showing Google s Jeff Dean in a lecture at the University of Washington  discussing the Bigtable content storage system used in Google s backend

User · Answer

Although Google uses BigTable for all their main applications  they also use MySQL for other  perhaps minor  apps

User · Answer

And it s maybe also handy to know that BigTable is not a relational database  like MySQL  but a huge  distributed  hash table which has very different characteristics  You can play around with  a limited version  of BigTable yourself on the Google AppEngine platform   Next to Hadoop mentioned above there are many other implementations that try to solve the same problems as BigTable  scalability  availability   I saw a nice blog post yesterday listing most of them here

User · Answer

Google primarily uses Bigtable   Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size   For more information  download the document from here   Google also uses Oracle and MySQL databases for some of their applications    Any more information you can add is highly appreciated

User · Answer

It s something they ve built themselves - it s called Bigtable   http   en wikipedia org wiki BigTable  There is a paper by Google on the database   http   research google com archive bigtable html

User · Answer

Spanner is Google s globally distributed relational database management system  RDBMS   the successor to BigTable  Google claims it is not a pure relational system because each table must have a primary key   Here is the link of the paper      Spanner is Google s scalable  multi-version  globally-distributed  and   synchronously-replicated database  It is the first system to   distribute data at global scale and support externally-consistent   distributed transactions  This paper describes how Spanner is   structured  its feature set  the rationale underlying various design   decisions  and a novel time API that exposes clock uncertainty  This   API and its implementation are critical to supporting external   consistency and a variety of powerful features  non-blocking reads in   the past  lock-free read-only transactions  and atomic schema changes    across all of Spanner    Another database invented by Google is Megastore  Here is the abstract      Megastore is a storage system developed to meet the requirements of   today s interactive online services  Megastore blends the scalability   of a NoSQL datastore with the convenience of a traditional RDBMS in a   novel way  and provides both strong consistency guarantees and high   availability  We provide fully serializable ACID semantics within   fine-grained partitions of data  This partitioning allows us to   synchronously replicate each write across a wide area network with   reasonable latency and support seamless failover between datacenters    This paper describes Megastore s semantics and replication algorithm    It also describes our experience supporting a wide range of Google   production services built with Megastore

User · Answer

Although Google uses BigTable for all their main applications  they also use MySQL for other  perhaps minor  apps

User · Answer

Bigtable  A Distributed Storage System for Structured Data     Bigtable is a distributed storage   system  built by Google  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    Some features   fast and extremely large-scale DBMS a sparse  distributed multi-dimensional sorted map  sharing characteristics of both row-oriented and column-oriented databases  designed to scale into the petabyte range it works across hundreds or thousands of machines it is 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 versioning  tables are optimized for GFS  Google File System  by being split into multiple tablets - segments of the table as split along a row chosen such that the tablet will be  200 megabytes in size    Architecture  BigTable is not a relational database  It does not support joins nor does it support rich SQL-like queries  Each table is a multidimensional sparse map  Tables consist of rows and columns  and each cell has a time stamp  There can be multiple versions of a cell with different time stamps  The time stamp allows for operations such as  select  n  versions of this Web page  or  delete cells that are older than a specific date time     In order to manage the huge tables  Bigtable splits tables at row boundaries and saves them as tablets  A tablet is around 200 MB  and each machine saves about 100 tablets  This setup allows tablets from a single table to be spread among many servers  It also allows for fine-grained load balancing  If one table is receiving many queries  it can shed other tablets or move the busy table to another machine that is not so busy  Also  if a machine goes down  a tablet may be spread across many other servers so that the performance impact on any given machine is minimal   Tables are stored as immutable SSTables and a tail of logs  one log per machine   When a machine runs out of system memory  it compresses some tablets using Google proprietary compression techniques  BMDiff and Zippy   Minor compactions involve only a few tablets  while major compactions involve the whole table system and recover hard-disk space   The locations of Bigtable tablets are stored in cells  The lookup of any particular tablet is handled by a three-tiered system  The clients get a point to a META0 table  of which there is only one  The META0 table keeps track of many META1 tablets that contain the locations of the tablets being looked up  Both META0 and META1 make heavy use of pre-fetching and caching to minimize bottlenecks in the system   Implementation  BigTable is built on Google File System  GFS   which is used as a backing store for log and data files  GFS provides reliable storage for SSTables  a Google-proprietary file format used to persist table data   Another service that BigTable makes heavy use of is Chubby  a highly-available  reliable distributed lock service  Chubby allows clients to take a lock  possibly associating it with some metadata  which it can renew by sending keep alive messages back to Chubby  The locks are stored in a filesystem-like hierarchical naming structure   There are three primary server types of interest in the Bigtable system    Master servers  assign tablets to tablet servers  keeps track of where tablets are located and redistributes tasks as needed   Tablet servers  handle read write requests for tablets and split tablets when they exceed size limits  usually 100MB - 200MB   If a tablet server fails  then a 100 tablet servers each pickup 1 new tablet and the system recovers   Lock servers  instances of the Chubby distributed lock service  Lots of actions within BigTable require acquisition of locks including opening tablets for writing  ensuring that there is no more than one active Master at a time  and access control checking    Example from Google s research paper        A slice of an example table that   stores Web pages  The row name is a   reversed URL  The contents column   family contains the page contents  and   the anchor column family contains the   text of any anchors that reference the   page  CNN s home page is referenced by   both the Sports Illustrated and the   MY-look home pages  so the row   contains columns named   anchor cnnsi com and   anchor my look ca  Each anchor cell   has one version  the contents column   has three versions  at timestamps   t3  t5  and t6    API  Typical operations to BigTable are creation and deletion of tables and column families  writing data and deleting columns from a row  BigTable provides this functions to application developers in an API  Transactions are supported at the row level  but not across several row keys     Here is the link to the PDF of the research paper    And here you can find a video showing Google s Jeff Dean in a lecture at the University of Washington  discussing the Bigtable content storage system used in Google s backend

[database] What database does Google use?

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