Differences between NoSQL databases

问题:

NoSQL term has 4 categories.

1. Key\value stores
2. Document oriented
3. Graph
4. Column oriented.

From my point of view all these data modeling has same definition, What are differences?

Key\value database maintains data in structure like object in OOP. having access to data is base on unique key.

Column oriented is an approach like key\value! But in key\value, you cant access to value by query. I mean, queries are key-based.

Compare 1st & 2nd picture from 2 different categories.

Document oriented stores data in collections, something like rows. Having access to data is base on unique key. The collections store data like key\value. However, you can access data by value.

As you can see, In these 3 categories, we define a unique key for specify a unique object & some pairs of key\value for more information

Graph db is a little different.

So, what are differences in definition & in real-world?

回答1:

Watch this: http://www.youtube.com/watch?v=qI_g07C_Q5I

Best introduction to NoSQL I've ever seen.

回答2:

You question needs a lengthy answer, so adding link below. Please refer those for clarification:

http://rebelic.nl/2011/05/28/the-four-categories-of-nosql-databases/

http://www.modelmetrics.com/technology-viewpoint/the-four-horsemen-of-nosql/

http://nosql-database.org/

Hope,these link will guide you.

回答3:

Document databases pair each key with a complex data structure known as a document. Documents can contain many different key-value pairs, or key-array pairs, or even nested documents.
Graph stores are used to store information about networks of data, such as social connections. Graph stores include Neo4J and Giraph.
Key-value stores are the simplest NoSQL databases. Every single item in the database is stored as an attribute name (or 'key'), together with its value. Examples of key-value stores are Riak and Berkeley DB. Some key-value stores, such as Redis, allow each value to have a type, such as 'integer', which adds functionality.
Wide-column stores such as Cassandra and HBase are optimized for queries over large datasets, and store columns of data together, instead of rows.

for more, follow this link on MongoDB