Graph

Graph object

class graphscope.framework.graph.Graph(session_id, incoming_data=None)[source]

A class for representing metadata of a graph in the GraphScope.

A Graph object holds the metadata of a graph, such as key, schema, and the graph is directed or not.

It is worth noting that the graph is stored by the backend such as Analytical Engine, Vineyard. In other words, the graph object holds nothing but metadata.

The graph object should not be created directly from Graph. Instead, the graph should be created by Session.load_from

The following example demonstrates its usage:

>>> import graphscope as gs
>>> from graphscope.framework.loader import Loader
>>> sess = gs.session()
>>> g = sess.load_from(
...     edges={
...         "knows": (
...             Loader("{}/p2p-31_property_e_0".format(property_dir), header_row=True),
...             ["src_label_id", "dst_label_id", "dist"],
...             ("src_id", "person"),
...             ("dst_id", "person"),
...         ),
...     },
...     vertices={
...         "person": Loader(
...             "{}/p2p-31_property_v_0".format(property_dir), header_row=True
...         ),
...     }
... )
__init__(session_id, incoming_data=None)[source]

Construct a Graph object.

Parameters

  • session_id (str) – Session id of the session the graph is created in.

  • incoming_data –

    Graph can be initialized through various type of sources, which can be one of:

    • GraphDef

    • nx.Graph

    • Graph

    • VineyardObject

add_column(results, selector)[source]

Add the results as a column to the graph. Modification rules are given by the selector.

Parameters

  • results (Context) – A Context that created by doing a query.

  • selector (dict) – Select results to add as column. Format is similar to selectors in Context

Returns

A new Graph with new columns.

Return type

Graph

attach_interactive_instance(instance)[source]

Store the instance when a new interactive instance is started.

Parameters

instance – interactive instance

attach_learning_instance(instance)[source]

Store the instance when a new learning instance is created.

Parameters

instance – learning instance

detach()[source]

Detaching a graph makes it being left in vineyard even when the varaible for this Graph object leaves the lexical scope.

The graph can be accessed using the graph’s ObjectID or its name later.

property graph_type

The type of the graph object.

Returns

the type of the graph.

Return type

type (types_pb2.GraphType)

property key

The key of the corresponding graph in engine.

property op

The DAG op of this graph.

project_to_simple(v_label='_', e_label='_', v_prop=None, e_prop=None)[source]

Project a property graph to a simple graph, useful for analytical engine. Will translate name represented label or property to index, which is broadedly used in internal engine.

Parameters

  • v_label (str, optional) – vertex label to project. Defaults to “_”.

  • e_label (str, optional) – edge label to project. Defaults to “_”.

  • v_prop (str, optional) – vertex property of the v_label. Defaults to None.

  • e_prop (str, optional) – edge property of the e_label. Defaults to None.

Returns

A Graph instance, which graph_type is ARROW_PROJECTED

Return type

Graph

property schema

Schema of the graph.

Returns

the schema of the graph

Return type

GraphSchema

property schema_path

Path that Coordinator will write interactive schema path to.

Returns

The path contains the schema. for interactive engine.

Return type

str

property session_id

Get the currrent session_id.

Returns

Return session id that the graph belongs to.

Return type

str

to_dataframe(selector, vertex_range=None)[source]

Select some elements of the graph and output as a pandas.DataFrame

Parameters

  • selector (dict) – Select some portions of graph.

  • vertex_range (dict, optional) – Slice vertices. Defaults to None.

Returns

pandas.DataFrame

to_numpy(selector, vertex_range=None)[source]

Select some elements of the graph and output to numpy.

Parameters

  • selector (str) – Select a portion of graph as a numpy.ndarray.

  • vertex_range (dict, optional) – Slice vertices. Defaults to None.

Returns

numpy.ndarray

unload()[source]

Unload this graph from graphscope engine.

property vineyard_id

Get the vineyard object_id of this graph.

Returns

return vineyard id of this graph

Return type

str

Loader object

class graphscope.framework.loader.Loader(source, delimiter=',', header_row=True, **kwargs)[source]

Generic data source wrapper. Loader can take various data sources, and assemble necessary information into a AttrValue.

__init__(source, delimiter=',', header_row=True, **kwargs)[source]

Initialize a loader with configurable options. Note: Loader cannot be reused since it may change inner state when constructing information for loading a graph. :param source:

The data source to be load, which could be one of the followings:

  • local file: specified by URL file://...

  • oss file: specified by URL oss://...

  • hdfs file: specified by URL hdfs://...

  • s3 file: specified by URL s3://...

  • numpy ndarray, in CSR format

  • pandas dataframe

Ordinary data sources can be loaded using vineyard stream as well, a vineyard:// prefix can be used in the URL then the local file, oss object or HDFS file will be loaded into a vineyard stream first, then GraphScope’s fragment will be built upon those streams in vineyard.

Once the stream IO in vineyard reaches a stable state, it will be the default mode to load data sources and construct fragments in GraphScope.

Parameters

  • delimiter (char, optional) – Column delimiter. Defaults to ‘,’

  • header_row (bool, optional) – Whether source have a header. If true, column names will be read from the first row of source, else they are named by ‘f0’, ‘f1’, …. Defaults to True.

Notes

Data is resolved by drivers in libvineyard . See more additional info in Loading Graph section of Docs, and implementations in libvineyard.

Graph Functions

graphscope.framework.graph_utils.load_from(edges: Union[Mapping[str, Union[Sequence, graphscope.framework.loader.Loader, str, Sequence[numpy.ndarray], pandas.core.frame.DataFrame, graphscope.framework.vineyard_object.VineyardObject, Mapping]], graphscope.framework.loader.Loader, str, Sequence[numpy.ndarray], pandas.core.frame.DataFrame, graphscope.framework.vineyard_object.VineyardObject, Sequence], vertices: Optional[Union[Mapping[str, Union[Sequence, graphscope.framework.loader.Loader, str, Sequence[numpy.ndarray], pandas.core.frame.DataFrame, graphscope.framework.vineyard_object.VineyardObject, Mapping]], graphscope.framework.loader.Loader, str, Sequence[numpy.ndarray], pandas.core.frame.DataFrame, graphscope.framework.vineyard_object.VineyardObject, Sequence]] = None, directed=True, oid_type='int64_t', generate_eid=True) → graphscope.framework.graph.Graph[source]

Load a Arrow property graph using a list of vertex/edge specifications.

  • Use Dict of tuples to setup a graph.

    We can use a dict to set vertex and edge configurations, which can be used to build graphs.

    Examples:

    g = graphscope_session.load_from(
    edges={
        "group": [
            (
                "file:///home/admin/group.e",
                ["group_id", "member_size"],
                ("leader_student_id", "student"),
                ("member_student_id", "student"),
            ),
            (
                "file:///home/admin/group_for_teacher_student.e",
                ["group_id", "group_name", "establish_date"],
                ("teacher_in_charge_id", "teacher"),
                ("member_student_id", "student"),
            ),
        ]
    },
    vertices={
        "student": (
            "file:///home/admin/student.v",
            ["name", "lesson_nums", "avg_score"],
            "student_id",
        ),
        "teacher": (
            "file:///home/admin/teacher.v",
            ["name", "salary", "age"],
            "teacher_id",
        ),
    },
)

    ‘e’ is the label of edges, and ‘v’ is the label for vertices, edges are stored in the ‘both_in_out’ format edges with label ‘e’ linking from ‘v’ to ‘v’.

  • Use Dict of dict to setup a graph.

    We can also give each element inside the tuple a meaningful name, makes it more understandable.

    Examples:

    g = graphscope_session.load_from(
    edges={
        "group": [
            {
                "loader": "file:///home/admin/group.e",
                "properties": ["group_id", "member_size"],
                "source": ("leader_student_id", "student"),
                "destination": ("member_student_id", "student"),
            },
            {
                "loader": "file:///home/admin/group_for_teacher_student.e",
                "properties": ["group_id", "group_name", "establish_date"],
                "source": ("teacher_in_charge_id", "teacher"),
                "destination": ("member_student_id", "student"),
            },
        ]
    },
    vertices={
        "student": {
            "loader": "file:///home/admin/student.v",
            "properties": ["name", "lesson_nums", "avg_score"],
            "vid": "student_id",
        },
        "teacher": {
            "loader": "file:///home/admin/teacher.v",
            "properties": ["name", "salary", "age"],
            "vid": "teacher_id",
        },
    },
)
Parameters

  • edges – Edge configuration of the graph

  • vertices (optional) – Vertices configurations of the graph. Defaults to None. If None, we assume all edge’s src_label and dst_label are deduced and unambiguous.

  • directed (bool, optional) – Indicate whether the graph should be treated as directed or undirected.

  • oid_type (str, optional) – ID type of graph. Can be “int64_t” or “string”. Defaults to “int64_t”.

  • generate_eid (bool, optional) – Whether to generate a unique edge id for each edge. Generated eid will be placed in third column. This feature is for cooperating with interactive engine. If you only need to work with analytical engine, set it to False. Defaults to False.