Document DBs One of the NoSQL ways to handle your data 

Document DBs - Definition Document-oriented databases store documents. A document is like a hash, with a unique ID field and values that may be any of a variety of types, including more hashes. Documents can contain nested structures, and so they exhibit a high degree of flexibility, allowing for variable domains. The system imposes few restrictions on incoming data, as long as it meets the basic requirement of being expressible as a document. 

Document DBs - Major implementations 1. Mongo DB 2. Couch DB 

Mongo DB - Core design goals Document Database designed with three main goals: scalability, performance and easy data access 

Mongo DB - Structure 

Mongo DB - Structure 

Mongo DB - Create 

Mongo DB - Read 

Mongo DB - Read 

Mongo DB - Read 

Mongo DB - Read 

Mongo DB - Read 

Mongo DB - Read 

Mongo DB - Update 

Mongo DB - Update 

Mongo DB - Delete 

Mongo DB - Rails Mapping 1. MongoMapper 2. Mongoid 

Mongo DB - Pro's ● Very flexible data model (No schema and nested values) ● Handle huge amount of data and requests via horizontal and vertical scaling ● Easy to use (Mongo commands close to SQL ones) 

Mongo DB - Con's ● Encourage denormalization of schemas ● Mongo flexibility (about field names, ...) could be dangerous ● Require some effort to design and manage the data cluster 

Mongo DB - Additional Features ● Replication ● Sharding ● GeoSpatial Requests ● GridFS 

Mongo DB - Supporting Companies ● Foursquare ● Bit.ly ● CERN (for the LHC) ● Heroku ● The NY Times 

References Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement by Eric Redmond and Jim R. Wilson 

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