DSE Graph Queries

The DSE driver executes graph queries over the Cassandra native protocol. Use Session.execute_graph() or Session.execute_graph_async() for executing gremlin queries in DSE Graph.

The DSE driver defines three Execution Profiles suitable for graph execution:

• EXEC_PROFILE_GRAPH_DEFAULT

• EXEC_PROFILE_GRAPH_SYSTEM_DEFAULT

• EXEC_PROFILE_GRAPH_ANALYTICS_DEFAULT

See Getting Started and Execution Profiles for more detail on working with profiles.

Getting Started

First, we need to create a graph in the system. To access the system API, we use the system execution profile

from dse.cluster import Cluster, EXEC_PROFILE_GRAPH_SYSTEM_DEFAULT

cluster = Cluster()
session = cluster.connect()

graph_name = 'movies'
session.execute_graph("system.graph(name).ifNotExists().create()", {'name': graph_name},
                      execution_profile=EXEC_PROFILE_GRAPH_SYSTEM_DEFAULT)

To execute requests on our newly created graph, we need to setup an execution profile. Additionally, we also need to set the schema_mode to development for the schema creation:

from dse.cluster import Cluster, GraphExecutionProfile, EXEC_PROFILE_GRAPH_DEFAULT
from dse.graph import GraphOptions

graph_name = 'movies'
ep = GraphExecutionProfile(graph_options=GraphOptions(graph_name=graph_name))

cluster = Cluster(execution_profiles={EXEC_PROFILE_GRAPH_DEFAULT: ep})
session = cluster.connect()

session.execute_graph("schema.config().option('graph.schema_mode').set('development')")

We are ready to configure our graph schema. We will create a simple one for movies:

# properties are used to define a vertex
properties = """
schema.propertyKey("genreId").Text().create();
schema.propertyKey("personId").Text().create();
schema.propertyKey("movieId").Text().create();
schema.propertyKey("name").Text().create();
schema.propertyKey("title").Text().create();
schema.propertyKey("year").Int().create();
schema.propertyKey("country").Text().create();
"""

session.execute_graph(properties)  # we can execute multiple statements in a single request

# A Vertex represents a "thing" in the world.
vertices = """
schema.vertexLabel("genre").properties("genreId","name").create();
schema.vertexLabel("person").properties("personId","name").create();
schema.vertexLabel("movie").properties("movieId","title","year","country").create();
"""

session.execute_graph(vertices)

# An edge represents a relationship between two vertices
edges = """
schema.edgeLabel("belongsTo").single().connection("movie","genre").create();
schema.edgeLabel("actor").connection("movie","person").create();
"""

session.execute_graph(edges)

# Indexes to execute graph requests efficiently
indexes = """
schema.vertexLabel("genre").index("genresById").materialized().by("genreId").add();
schema.vertexLabel("genre").index("genresByName").materialized().by("name").add();
schema.vertexLabel("person").index("personsById").materialized().by("personId").add();
schema.vertexLabel("person").index("personsByName").materialized().by("name").add();
schema.vertexLabel("movie").index("moviesById").materialized().by("movieId").add();
schema.vertexLabel("movie").index("moviesByTitle").materialized().by("title").add();
schema.vertexLabel("movie").index("moviesByYear").secondary().by("year").add();
"""

Next, we’ll add some data:

session.execute_graph("""
g.addV('genre').property('genreId', 1).property('name', 'Action').next();
g.addV('genre').property('genreId', 2).property('name', 'Drama').next();
g.addV('genre').property('genreId', 3).property('name', 'Comedy').next();
g.addV('genre').property('genreId', 4).property('name', 'Horror').next();
""")

session.execute_graph("""
g.addV('person').property('personId', 1).property('name', 'Mark Wahlberg').next();
g.addV('person').property('personId', 2).property('name', 'Leonardo DiCaprio').next();
g.addV('person').property('personId', 3).property('name', 'Iggy Pop').next();
""")

session.execute_graph("""
g.addV('movie').property('movieId', 1).property('title', 'The Happening').
    property('year', 2008).property('country', 'United States').next();
g.addV('movie').property('movieId', 2).property('title', 'The Italian Job').
    property('year', 2003).property('country', 'United States').next();

g.addV('movie').property('movieId', 3).property('title', 'Revolutionary Road').
    property('year', 2008).property('country', 'United States').next();
g.addV('movie').property('movieId', 4).property('title', 'The Man in the Iron Mask').
    property('year', 1998).property('country', 'United States').next();

g.addV('movie').property('movieId', 5).property('title', 'Dead Man').
    property('year', 1995).property('country', 'United States').next();
""")

Now that our genre, actor and movie vertices are added, we’ll create the relationships (edges) between them:

session.execute_graph("""
genre_horror = g.V().hasLabel('genre').has('name', 'Horror').next();
genre_drama = g.V().hasLabel('genre').has('name', 'Drama').next();
genre_action = g.V().hasLabel('genre').has('name', 'Action').next();

leo  = g.V().hasLabel('person').has('name', 'Leonardo DiCaprio').next();
mark = g.V().hasLabel('person').has('name', 'Mark Wahlberg').next();
iggy = g.V().hasLabel('person').has('name', 'Iggy Pop').next();

the_happening = g.V().hasLabel('movie').has('title', 'The Happening').next();
the_italian_job = g.V().hasLabel('movie').has('title', 'The Italian Job').next();
rev_road = g.V().hasLabel('movie').has('title', 'Revolutionary Road').next();
man_mask = g.V().hasLabel('movie').has('title', 'The Man in the Iron Mask').next();
dead_man = g.V().hasLabel('movie').has('title', 'Dead Man').next();

the_happening.addEdge('belongsTo', genre_horror);
the_italian_job.addEdge('belongsTo', genre_action);
rev_road.addEdge('belongsTo', genre_drama);
man_mask.addEdge('belongsTo', genre_drama);
man_mask.addEdge('belongsTo', genre_action);
dead_man.addEdge('belongsTo', genre_drama);

the_happening.addEdge('actor', mark);
the_italian_job.addEdge('actor', mark);
rev_road.addEdge('actor', leo);
man_mask.addEdge('actor', leo);
dead_man.addEdge('actor', iggy);
""")

We are all set. You can now query your graph. Here are some examples:

# Find all movies of the genre Drama
for r in session.execute_graph("""
  g.V().has('genre', 'name', 'Drama').in('belongsTo').valueMap();"""):
    print(r)

// Find all movies of the same genre than the movie 'Dead Man'
for r in session.execute_graph("""
  g.V().has('movie', 'title', 'Dead Man').out('belongsTo').in('belongsTo').valueMap();"""):
    print(r)

// Find all movies of Mark Wahlberg
for r in session.execute_graph("""
  g.V().has('person', 'name', 'Mark Wahlberg').in('actor').valueMap();"""):
    print(r)

To see a more graph examples, see DataStax Graph Examples.

Graph Types

Here are the supported graph types with their python representations:

Graph Row Factory

By default (with GraphExecutionProfile.row_factory set to graph.graph_object_row_factory()), known graph result types are unpacked and returned as specialized types (Vertex, Edge). If the result is not one of these types, a graph.Result is returned, containing the graph result parsed from JSON and removed from its outer dict. The class has some accessor convenience methods for accessing top-level properties by name (type, properties above), or lists by index:

# dicts with `__getattr__` or `__getitem__`
result = session.execute_graph("[[key_str: 'value', key_int: 3]]", execution_profile=EXEC_PROFILE_GRAPH_SYSTEM_DEFAULT)[0]  # Using system exec just because there is no graph defined
result  # dse.graph.Result({u'key_str': u'value', u'key_int': 3})
result.value  # {u'key_int': 3, u'key_str': u'value'} (dict)
result.key_str  # u'value'
result.key_int  # 3
result['key_str']  # u'value'
result['key_int']  # 3

# lists with `__getitem__`
result = session.execute_graph('[[0, 1, 2]]', execution_profile=EXEC_PROFILE_GRAPH_SYSTEM_DEFAULT)[0]
result  # dse.graph.Result([0, 1, 2])
result.value  # [0, 1, 2] (list)
result[1]  # 1 (list[1])

You can use a different row factory by setting Session.default_graph_row_factory or passing it to Session.execute_graph(). For example, graph.single_object_row_factory() returns the JSON result string`, unparsed. graph.graph_result_row_factory() returns parsed, but unmodified results (such that all metadata is retained, unlike graph.graph_object_row_factory(), which sheds some as attributes and properties are unpacked). These results also provide convenience methods for converting to known types (as_vertex(), as_edge(), as_path()).

Vertex and Edge properties are never unpacked since their types are unknown. If you know your graph schema and want to deserialize properties, use the GraphSON1Deserializer. It provides convenient methods to deserialize by types (e.g. deserialize_date, deserialize_uuid, deserialize_polygon etc.) Example:

# ...
from dse.graph import GraphSON1Deserializer

row = session.execute_graph("g.V().toList()")[0]
value = row.properties['my_property_key'][0].value  # accessing the VertexProperty value
value = GraphSON1Deserializer.deserialize_timestamp(value)

print value  # 2017-06-26 08:27:05
print type(value)  # <type 'datetime.datetime'>

Named Parameters

Named parameters are passed in a dict to cluster.Session.execute_graph():

result_set = session.execute_graph('[a, b]', {'a': 1, 'b': 2}, execution_profile=EXEC_PROFILE_GRAPH_SYSTEM_DEFAULT)
[r.value for r in result_set]  # [1, 2]

All python types listed in Graph Types can be passed as named parameters and will be serialized automatically to their graph representation:

Example:

s.execute_graph("""
  g.addV('person').
  property('name', text_value).
  property('age', integer_value).
  property('birthday', timestamp_value).
  property('house_yard', polygon_value).toList()
""", {
  'text_value': 'Mike Smith',
  'integer_value': 34,
  'timestamp_value': datetime.datetime(1967, 12, 30),
  'polygon_value': Polygon(((30, 10), (40, 40), (20, 40), (10, 20), (30, 10)))
})

As with all Execution Profile parameters, graph options can be set in the cluster default (as shown in the first example) or specified per execution:

ep = session.execution_profile_clone_update(EXEC_PROFILE_GRAPH_DEFAULT,
                                            graph_options=GraphOptions(graph_name='something-else'))
session.execute_graph(statement, execution_profile=ep)

Using GraphSON2 Protocol

The default graph protocol used is GraphSON1. However GraphSON1 may cause problems of type conversion happening during the serialization of the query to the DSE Graph server, or the deserialization of the responses back from a string Gremlin query. GraphSON2 offers better support for the complex data types handled by DSE Graph.

DSE >=5.0.4 now offers the possibility to use the GraphSON2 protocol for graph queries. Enabling GraphSON2 can be done by changing the graph protocol of the execution profile and setting the graphson2 row factory:

from dse.cluster import Cluster, GraphExecutionProfile, EXEC_PROFILE_GRAPH_DEFAULT
from dse.graph import GraphOptions, GraphProtocol, graph_graphson2_row_factory

# Create a GraphSON2 execution profile
ep = GraphExecutionProfile(graph_options=GraphOptions(graph_name='types',
                                                      graph_protocol=GraphProtocol.GRAPHSON_2_0),
                           row_factory=graph_graphson2_row_factory)

cluster = Cluster(execution_profiles={EXEC_PROFILE_GRAPH_DEFAULT: ep})
s = cluster.connect()
s.execute_graph(...)

Using GraphSON2, all properties will be automatically deserialized to its Python representation. Note that it may bring significant behavioral change at runtime.

It is generally recommended to switch to GraphSON2 as it brings more consistent support for complex data types in the Graph driver and will be activated by default in the next major version (Python dse-driver driver 3.0).