Key-value stores are designed for storing data in flexible and schema-less way. In these databases, data is stored as a list of keys with their corresponding values. All keys are unique and data access is achieved by means of key-value association. In addition, all keys are stored in a hash table to provide quick access for them. Values may contain actual information (raw data), or they may contain another set of keys. Key-value stores provide high concurrency, fast lookups and massive storage. These features make them suitable for application tasks like managing user profiles or sessions or retrieving product names. There are four common implementation of key-value stores which are Riak, Radis, Amazon 's Dynamo, and Voldemort.
• Column-Family Stores/Column Oriented Stores
The data model of column-family databases is similar to relational database model. Column-family database could be considered as a specific type of key-value pair model. Data is structured in columns that may be countless. Column-family databases consist of the following aspects:
• Column is represented as a pair of key and value. It is an atomic unit of information supported by the database.
• Super-column group associated columns together.
• Column family consists of several super-columns. It is similar to a table in relational databases.
There are two main differences between column-family and relational databases. First, a new column or super column can be added to the design easily whenever necessary. Second, each row in the column-family database does not need to be of the same degree. In other words, rows can have different sets of columns and super-columns, and they can change dynamically.
It important to notice that rows can be ad...
... middle of paper ...
...ties to relationship between nodes. The performance in (Of) relational databases is lower than the performance of graph databases because Join is a slow operation which decreases the performance as table size increases. In contrast, graph databases find relationships within large amounts of data quickly because they provide index free adjacency instead of processing intensive joins. Another important difference between relational and databases graph databases is schema. Relational databases uses pre-defined schema and there is no provision for dynamic and ad-hoc data while graph databases do not require a pre-defined schema which leads to easier adaptation to schema evolution and ability to capture ad-hoc relationships. Also, most of graph databases can store semi-structured information.
Examples of Graph databases are Neo4J, Infinite Graph, InfoGrid, HyperGraphDB.
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