Dgraph == Graph Database

_:hamdi

Hamdi

name

23

age

_:ahmadi

_:muzakkiy

friend

friend

_:pet

pet

kitty

Vs

Dgraph

- Good for Analyze

SQL

- Good for Transactional

Vs

Dgraph

- Good when we want to know data relation

Elastic

- Good when we want to know a very specific data ( fast )

Concept

Node

Value

 ( Attribute 1 )

Node

( Relationship )

Value

 ( Attribute 2 )

Node

• As Identifier ( object )
• Have _uid_ ( universal identifier )
• Have Attribute
• Have Relationship

Value

• Value Of Attribute

Attribute

• 1 node have 1 attribute name ( ex : node X have name ( 1 ) ) for this example, attribute == name. The name value is the Value

Relationship

• 1 node have 1/many relationship 
• Relationship only happen betwen Node - Node

Basic

Mutation

mutation {
  schema {
    name: string @index(exact, term) .
    age: int @index(int) .
    friend: uid @count .
  }
}

mutation {
  set {
    _:naruto <name> "uzumaki naruto" .
    _:naruto <child> _:boruto .
    _:naruto <child> _:himawari .
    _:naruto <job> "hokage" .
    _:naruto <pet> _:gamabunta .
    _:naruto <friend> _:sasuke .
    _:naruto <friend> _:sakura .
    _:naruto <teacher> _:kakashi .

    _:boruto <father> _:naruto .
    _:boruto <sister> _:himawari .
    _:boruto <name> "uzumaki boruto" .


    _:himawari <name> "himawari hyuga" .
  }
}

Query

{
  everyone(func: anyofterms(name, "naruto")) {
    name
    child {
      name
      friend { expand(_all_) { expand(_all_) } }
    }
  }
}