NoSQL

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NoSQL refers to a number of non-relational distributed database architectures. NoSQL architectures usually store data as key-value pairs, rather than supporting relations. Some systems eliminate the guarantee of consistency (instead promising eventual consistency) in order to increase scalability. The distributed nature of NoSQL architectures makes such data stores highly scalable and fault-tolerant.

NoSQL vs. RDBMS

In many cases, NoSQL databases can process data more quickly than traditional relational database management systems. One reason for this is that data representation in NoSQL databases is much simpler than in relational systems. For example, a table in a relational database might have many columns, but data in a key-value store will always have only two parts: the key and the value. In addition, many NoSQL databases do not fully support ACID transactions. While this allows faster performance, it can also be risky in applications where precision is needed, such as in banking applications.

Types of NoSQL Databases

Key-value Store

A key-value store maintains data as a pair consisting of an indexed key and a value. In general, key-value stores provide a single operation: fetching a single value using its key. Some key-value store implementations include mechanisms for performing a join on two distinct tables. Examples of key-value stores include Oracle's BerkeleyDB and Amazon's Dynamo.

BerkeleyDB

BerkeleyDB is an open source, transactional, embedded database engine. It is available as a library that can be included in any application. Data are represented as key/value pairs. The keys and values in BerkeleyDB can be any objects supported by the programming language. Data are stored in files on disk, as a single file for each key-value store. BerkeleyDB also provides the option to maintain data stores in memory only, if the store is small enough to fit in main memory.

BerkeleyDB provides a number of features competitive with relational databases, including support for transactions, two-phase locking, joins, and write ahead logging. These features make BerkeleyDB very reliable. The BerkeleyDB engine is used in a number of applications. The MySQL database management system offers BerkeleyDB as an option for the storage engine.

BerkeleyDB in itself does not provide a method for distributing data, but using a distributed hash table, it is possible to distribute data across multiple BerkeleyDB instances.

Column-oriented Databases

The column oriented database stores entries by column as opposed to row-oriented databases. This optimizes the SQL databases by making data aggregation easier and maximizing disk performance. Examples of open-source and commercial column oriented databases include: Cassandra(Facebook), Big Table(Google), Hypertable(Open-source implementation of Big Table), Hbase(Open-source implementation of Big Table), etc.

Big Table

Bigtable is a distributed storage system for managing structured data, which is designed to scale to petabytes of data reliably. It has been developed by Google since 2005 and used for more than 60 Google products.

Bigtable is a multi-dimensional sorted map, which can be indexed by a row key, column key, and a timestamp. For example, if we want to store a large collection of web pages, we would use URLs as row keys, various aspects of web pages as column names and the contents of the web pages can be stored in column under the timestamps when they were fetched.Every read or write of data under a single row key is atomic. The columns can be dynamically added. The timestamps represent different versions of data which are assigned by client application. The older versions are garbage-collected. The rows are sorted lexicographically. Consecutive keys are grouped together as "tablets". Column keys are grouped into sets called "column families". Column key is named using syntax: family: qualifier. Access control and disk/memory accounting are at column family level. The data design includes creating and deleting tables and column families, changing cluster, table and column family metadata like access control rights. The client interactions include writing and deleting values, read values, scan row ranges, single-row transactions, map, reduce integration.