Distributed data store: Difference between revisions

Revision as of 15:31, 25 July 2019

Revision as of 17:40, 26 July 2019

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| quote = Although GFS provides Google with reliable, scalable distributed file storage, it does not provide any facility for structuring the data contained in the files beyond a hierarchical directory structure and meaningful file names. It’s well known that more expressive solutions are required for large data sets. Google’s terabytes upon terabytes of data that they retrieve from web crawlers, amongst many other sources, need organising, so that client applications can quickly perform lookups and updates at a finer granularity than the file level. [...] The very first thing you need to know about Bigtable is that it isn’t a relational database. This should come as no surprise: one persistent theme through all of these large scale distributed data store papers is that RDBMSs are hard to do with good performance. There is no hard, fixed schema in a Bigtable, no referential integrity between tables (so no foreign keys) and therefore little support for optimised joins.

| url = http://the-paper-trail.org/blog/?p=86

| archive-url = https://web.archive.org/web/20170716092550/http://the-paper-trail.org:80/blog/bigtable-googles-distributed-data-store

| archive-date = 2017-07-16
| dead-url = yes
}}</ref> [[Amazon.com|Amazon]]'s [[Dynamo (storage system)|Dynamo]]<ref>{{cite web

| accessdate = 2011-04-05

| author = Sarah Pidcock