A document-oriented database is a designed for storing, retrieving, and managing document-oriented, or semi structured data. Document-oriented databases are one of the main categories of NoSQL databases. The central concept of a document-oriented database is the notion of a Document. While each document-oriented database implementation differs on the details of this definition, in general, they all assume documents encapsulate and encode data (or information) in some standard format(s) (or encoding(s)). Encodings in use include XML, YAML, JSON and BSON, as well as binary forms like PDF and Microsoft Office documents (MS Word, Excel, and so on).
MongoDB: MongoDB is a collection-oriented, schema-free document database. Data is grouped into sets that are called ‘collections’. Each collection has a unique name in the database, and can contain an unlimited number of documents. Collections are analogous to tables in a RDBMS, except that they don’t have any defined schema.
It store data (which is in BASON – “Binary Serialized dOcument Notation” format) that is a structured collection of key-value pairs, where keys are strings, and values are any of a rich set of data types, including arrays and documents.
- Terrastore: Terrastore is a modern document store which provides advanced scalability and elasticity features without sacrificing consistency. It is based on Terracotta, so it relies on an industry-proven, fast clustering technology.
- RavenDB: Raven is a .NET Linq enabled Document Database, focused on providing high performance, schema-less, flexible and scalable NoSQL data store for the .NET and Windows platforms.
Raven store any JSON document inside the database. It is schema-less database where you can define indexes using C#’s Linq syntax.
- OrientDB: OrientDB is an open source NoSQL database management system written in Java. Even if it is a document-based database, the relationships are managed as in graph databases with direct connections between records. It supports schema-less, schema-full and schema-mixed modes. It has a strong security profiling system based on users and roles and supports SQL as a query languages.
- ThruDB: Thrudb is a set of simple services built on top of the Apache Thrift framework that provides indexing and document storage services for building and scaling websites. Its purpose is to offer web developers flexible, fast and easy-to-use services that can enhance or replace traditional data storage and access layers.
It supports multiple storage backends such as BerkeleyDB, Disk, MySQL and also having Memcache and Spread integration.
- SisoDB: SisoDb is a document-oriented db-provider for Sql-Server written in C#. It lets you store object graphs of POCOs (plain old clr objects) without having to configure any mappings. Each entity is treated as an aggregate root and will get separate tables created on the fly.
Quick Start: http://www.sisodb.com/Wiki
- RaptorDB: RaptorDB is a extremely small size and fast embedded, noSql, persisted dictionary database using b+tree or MurMur hash indexing. It was primarily designed to store JSON data (see my fastJSON implementation), but can store any type of data that you give it.
- CloudKit: CloudKit provides schema-free, auto-versioned, RESTful JSON storage with optional OpenID and OAuth support, including OAuth Discovery.
- Jackrabbit: The Apache Jackrabbit™ content repository is a fully conforming implementation of the Content Repository for Java Technology API (JCR, specified in JSR 170 and 283). A content repository is a hierarchical content store with support for structured and unstructured content, full text search, versioning, transactions, observation, and more.
Document databases store and retrieve documents and basic atomic stored unit is a document. As always your requirement leads into the decision. You need to think about your data-access patterns / use-cases to create a smart document-model. When your domain model can be split and partitioned across some documents, a document-database will be a suitable one for you. For example for a blog-software, a CMS or a wiki-software a document-db works extremely well. But at the same time a non-relational database is not better than a relational one in some cases where your database have a lot of relations and normalization.
Just check the following link from stackoverflow also to cover the pros/cons of Relational Vs Document based databases.