Distributed data store: Difference between revisions

Revision as of 14:29, 30 July 2020

Revision as of 06:38, 25 January 2021

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==Distributed databases==

[[Distributed database]]s are usually [[non-relational database]]s that enable a quick access to data over a large number of nodes. Some distributed databases expose rich query abilities while others are limited to a [[key-value store]] semantics. Examples of limited distributed databases are [[Google]]'s [[Bigtable]], which is much more than a [[distributed file system]] or a [[peer-to-peer network]],<ref>{{cite web

| accessdateaccess-date = 2011-04-05

| ___location = http://the-paper-trail.org/

| publisher = Paper Trail

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| url-status = dead

}}</ref> [[Amazon.com|Amazon]]'s [[Dynamo (storage system)|Dynamo]]<ref>{{cite web

| accessdateaccess-date = 2011-04-05

| author = Sarah Pidcock

| date = 2011-01-31

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| quote = Dynamo: a highly available and scalable distributed data store

| url = http://www.cs.uwaterloo.ca/~kdaudjee/courses/cs848/slides/sarah1.pdf}}</ref>

and [[Azure Services Platform|Microsoft Azure Storage]].<ref>{{cite web|url=http://www.microsoft.com/windowsazure/features/storage/|title=Windows Azure Storage|date=2011-09-16|accessdateaccess-date=6 November 2011|url-status=dead|archiveurlarchive-url=https://web.archive.org/web/20111109002826/http://www.microsoft.com/windowsazure/features/storage/|archivedatearchive-date=9 November 2011}}</ref>

As the ability of arbitrary querying is not as important as the [[availability]], designers of distributed data stores have increased the latter at an expense of consistency. But the high-speed read/write access results in reduced consistency, as it is not possible to have both [[Consistency (database systems)|consistency]], availability, and partition tolerance of the network, as it has been proven by the [[CAP theorem]].