A distributed data store is a computer network where data is stored on more than one single node, very much in a replicated manner. It is explicitly referred to as either a distributed database (wherein users can store information on a number of nodes) or a computer network (wherein users store information on a number of peer network nodes).
- Distributed Database: It is a database in which storage devices are not at all connected to a CPU and are controlled by a distributed database management system (collectively at times known as distributed database system). It is either stored in different computers placed in the same physical location or disseminated over a network of unified computers. Contrasting to parallel systems, wherein the processors are closely attached to create a single database system. Distributed database system comprises of slackly-tied sites that share any physical components.
- Peer Network Node Data Stores: In it, the end-user can share and permit other users to use their computer as a storage node. Data may or may not be openly accessible to other users contingent on the network design.
Distributed data stores basically use an error detection and correction method. But, there are some distributed data stores which use forward error correction provisions to pull through the original file when any portion of the file is either damaged or inaccessible.
How does distributed data work?
In order to understand how distributed data stores work, let us understand the key concepts related to distributed data-partitioning, query routing and replication.
Partitioning: More often than not, our data sets are too large to be stored on a single machine. So, in order to manage it, we partition the data into subsets that are stored and processed on individual machines. Partitioning can be done in several ways:
1) Vertical Partitioning: Here, the data is split up by common fields. Common fields are the ones that are generally accessed together.
2) Horizontal Partitioning: Also called sharding, horizontal partitioning involves the splitting of the data into subsets with the same schema. This kind of partitioning can be chiefly classified into two categories: algorithmic and dynamic.
3) Query Routing: In addition to partitioning, you need to route queries from the client to the right machine. This can occur at different levels of the software stack.
4) Replication: Replication involves creating multiple copies of the same data. Replication can be either synchronous or asynchronous. In synchronous replication, the data gets copied to all the replicas before it responds to the request. On the other hand, in asynchronous replication, the data is stored on just one replica before it responds to the request.