Chapter 2: Data Models and Query Languages

Document DB

Json representation.

Example:

• MongoDB

• CouchDB

• RethinkDB, Espresso etc.

SQL vs NoSQL Database

SQL	NoSQL
Better join support	Limited join support
normalized structure	denormalized structure with locality
Many-to-many Many-to-one relationship	one-to-many relationship
XML schema with validation	Document-like structure Json representation
Schema on write	Schema on read

Glossary

Normalization: process of breaking down data into separate tables to minimize redundancy. This approach is effective for many-to-many relationship.

Graph DB

If many-to-many relationship are very common and complex in your application, it's natural to start modeling data as graph.

Think of a graph store as two relational tables, one for vertices and one for edges.

Examples

Property graph model: Neo4j, Titan, InfiniteGraph

Triple-store model: Datomic, AllegroGraph

Imperative graph: Gremlin, Pregel.

Query languages:

Relational DB: SQL

Document DB: MapReduce, MongoDB's aggregation pipeline

Graph DB: Cypher, SPARQL, Datalog