A distributed data store is a blurred concept and means either a distributed database where users store their information on a number of nodes, or a network in which a user stores their information on a number of peer network nodes.
Contents |
Distributed data store are non-relational databases that make a quick access to data over a large number of nodes possible. Examples for this kind of data stores are Google's BigTable, which is much more than a distributed file system or a peer-to-peer network[1], Amazon's Dynamo[2] and Windows Azure Storage[3].
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, availability, and partition tolerance of the network, as it has been proven by the CAP theorem.
In peer network data stores, the user can usually reciprocate and allow other users to use their computer as a storage node as well. Information may or may not be accessible to other users depending on the design of the network.
Most of the peer-to-peer networks do not have distributed data stores in that the user's data is only available when their node is on the network. However, this distinction is somewhat blurred in a system such as BitTorrent, where it is possible for the originating node to go offline but the content to continue to be served. Still, this is only the case for individual files requested by the redistributors, as contrasted with a network such as Freenet where all computers are made available to serve all files.
Distributed data stores typically use an error detection and correction technique. Some distributed data stores (such as Parchive over NNTP) use forward error correction techniques to recover the original file when parts of that file are damaged or unavailable. Others try again to download that file from a different mirror.