ArangoDB v3.10 reached End of Life (EOL) and is no longer supported.

This documentation is outdated. Please see the most recent stable version.

cuGraph Adapter

The cuGraph Adapter exports graphs from ArangoDB into RAPIDS cuGraph, a library of collective GPU-accelerated graph algorithms, and vice-versa

While offering a similar API and set of graph algorithms to NetworkX, RAPIDS cuGraph  library is GPU-based. Especially for large graphs, this results in a significant performance improvement of cuGraph compared to NetworkX. Please note that storing node attributes is currently not supported by cuGraph. In order to run cuGraph, an Nvidia-CUDA-enabled GPU is required.

Resources

The cuGraph Adapter repository  is available on Github. Check it out!

Installation

To install the latest release of the cuGraph Adapter, run the following command:

pip install --extra-index-url=https://pypi.nvidia.com cudf-cu11 cugraph-cu11
pip install adbcug-adapter

Quickstart

The following examples show how to get started with the cuGraph Adapter. Check also the interactive tutorial .

import cudf
import cugraph

from arango import ArangoClient
from adbcug_adapter import ADBCUG_Adapter, ADBCUG_Controller

# Connect to ArangoDB
db = ArangoClient().db()

# Instantiate the adapter
adbcug_adapter = ADBCUG_Adapter(db)

ArangoDB to cuGraph 

#######################
# 1.1: via Graph name #
#######################

cug_g = adbcug_adapter.arangodb_graph_to_cugraph("fraud-detection")

#############################
# 1.2: via Collection names #
#############################

cug_g = adbcug_adapter.arangodb_collections_to_cugraph(
    "fraud-detection",
    {"account", "bank", "branch", "Class", "customer"},  #  Vertex collections
    {"accountHolder", "Relationship", "transaction"},  # Edge collections
)

cuGraph to ArangoDB 

#################################
# 2.1: with a Homogeneous Graph #
#################################

edges = [("Person/A", "Person/B", 1), ("Person/B", "Person/C", -1)]
cug_g = cugraph.MultiGraph(directed=True)
cug_g.from_cudf_edgelist(cudf.DataFrame(edges, columns=["src", "dst", "weight"]), source="src", destination="dst", edge_attr="weight")

edge_definitions = [
    {
        "edge_collection": "knows",
        "from_vertex_collections": ["Person"],
        "to_vertex_collections": ["Person"],
    }
]

adb_g = adbcug_adapter.cugraph_to_arangodb("Knows", cug_g, edge_definitions, edge_attr="weight")

##############################################################
# 2.2: with a Homogeneous Graph & a custom ADBCUG Controller #
##############################################################

class Custom_ADBCUG_Controller(ADBCUG_Controller):
    """ArangoDB-cuGraph controller.

    Responsible for controlling how nodes & edges are handled when
    transitioning from ArangoDB to cuGraph & vice-versa.
    """

    def _prepare_cugraph_node(self, cug_node: dict, col: str) -> None:
        """Prepare a cuGraph node before it gets inserted into the ArangoDB
        collection **col**.

        :param cug_node: The cuGraph node object to (optionally) modify.
        :param col: The ArangoDB collection the node belongs to.
        """
        cug_node["foo"] = "bar"

    def _prepare_cugraph_edge(self, cug_edge: dict, col: str) -> None:
        """Prepare a cuGraph edge before it gets inserted into the ArangoDB
        collection **col**.

        :param cug_edge: The cuGraph edge object to (optionally) modify.
        :param col: The ArangoDB collection the edge belongs to.
        """
        cug_edge["bar"] = "foo"

adb_g = ADBCUG_Adapter(db, Custom_ADBCUG_Controller()).cugraph_to_arangodb("Knows", cug_g, edge_definitions)

###################################
# 2.3: with a Heterogeneous Graph #
###################################

edges = [
   ('student:101', 'lecture:101'), 
   ('student:102', 'lecture:102'), 
   ('student:103', 'lecture:103'), 
   ('student:103', 'student:101'), 
   ('student:103', 'student:102'),
   ('teacher:101', 'lecture:101'),
   ('teacher:102', 'lecture:102'),
   ('teacher:103', 'lecture:103'),
   ('teacher:101', 'teacher:102'),
   ('teacher:102', 'teacher:103')
]
cug_g = cugraph.MultiGraph(directed=True)
cug_g.from_cudf_edgelist(cudf.DataFrame(edges, columns=["src", "dst"]), source='src', destination='dst')

# ...

# Learn how this example is handled in Colab:
# https://colab.research.google.com/github/arangoml/cugraph-adapter/blob/master/examples/ArangoDB_cuGraph_Adapter.ipynb#scrollTo=nuVoCZQv6oyi

On this page