Gremlin DSLs in Python with DSE Graph

killr.movies() # equivalent to g.V().hasLabel('movie') killr.users() # equivalent to g.V().hasLabel('user') killr.persons() # equivalent to g.V().hasLabel('person') killr.genres() # equivalent to g.V().hasLabel('genre')

killr.movies('Young Guns') # equivalent to g.V().has('movie', 'title', 'Young Guns') killr.users('u460') # equivalent to g.V().has('user', 'userId', 'u460')

killr.movies('Young Guns', 'Hot Shots!') # equivalent to g.V().has('movie', 'title', within('Young Guns', 'Hot Shots!')) killr.users('u460', 'u461', 'u462') # equivalent to g.V().has('user', 'userId', within('u460', 'u461', 'u462'))

class KillrVideoTraversalSource(GraphTraversalSource):

      return traversal

   def users(self, *args): traversal = self.get_graph_traversal().V().hasLabel("user")

   return traversal

killr.movies('Young Guns').out('actor') # equivalent to g.V().has('movie','title','Young Guns').out('actor') killr.movies('Young Guns').inE('rated') # equivalent to g.V().has('movie','title','Young Guns').inE('rated')

killr.movies('Young Guns').actors() # equivalent to g.V().has('movie','title','Young Guns').out('actor') killr.movies('Young Guns').ratings() # equivalent to g.V().has('movie','title','Young Guns').inE('rated')

        return self.out("actor").hasLabel('person') # extra verification that a 'person' is obtained

    return self.inE('rated')

killr.users('u460').recommend()

   def ratings(self): return self.inE('rated')

   def recommend(self, recommendations, minimum_rating, include):

killr.users('u460').recommend(5, 7, has('country','USA').has('duration',gt(90))) # recommend movies from 'USA' longer than 90 minutes

killr.users('u460').recommend(5, 7, out('belongsTo').has('name', 'Comedy')) # recommend movies that are comedies

killr.users('u460').recommend(5, 7, genre('Comedy')) # recommend movies that are comedies

   def recommend(self, recommendations, minimum_rating, include):

class KillrVideoTraversal(GraphTraversal):

# ... omitted other DSL steps to focus on recommend() step

def recommend(self, recommendations, minimum_rating, include, recommender):

class Recommender(Enum):

      return switcher.get(self)

class KillrVideoTraversal(GraphTraversal):

# ... omitted other DSL steps to focus on recommend() step

def recommend(self, recommendations, minimum_rating, include, recommender):

class KillrVideoTraversal(GraphTraversal):

# ... omitted other DSL steps to focus on recommend() step

killr.users('u460').recommend(5, 7) killr.users('u460').recommend(5, 7, has('country','USA')) killr.users('u460').recommend(5, 7, has('country','USA'), Recommender.LARGE_SAMPLE)

from aenum import Enum

class KillrVideoTraversal(GraphTraversal):

# ... omitting other DSL methods to focus on genre()

killr.users('u460').recommend(5, 7, genre(Genre.COMEDY, Genre.ANIMATION)) # recommend movies that are comedies or animations