cuGraph Adapter

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

Installation

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

conda install -c arangodb adbcug-adapter

Quickstart

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

import cudf
import cugraph
from arango import ArangoClient # Python-Arango driver

from adbcug_adapter import ADBCUG_Adapter

# Let's assume that the ArangoDB "fraud detection" dataset is imported to this endpoint
db = ArangoClient(hosts="http://localhost:8529").db("_system", username="root", password="")

adbcug_adapter = ADBCUG_Adapter(db)

# Use Case 1.1: ArangoDB to cuGraph via Graph name
cug_fraud_graph = adbcug_adapter.arangodb_graph_to_cugraph("fraud-detection")

# Use Case 1.2: ArangoDB to cuGraph via Collection names
cug_fraud_graph_2 = adbcug_adapter.arangodb_collections_to_cugraph(
    "fraud-detection",
    {"account", "bank", "branch", "Class", "customer"},  #  Vertex collections
    {"accountHolder", "Relationship", "transaction"},  # Edge collections
)

# Use Case 2: cuGraph to ArangoDB:
## 1) Create a sample cuGraph
cug_divisibility_graph = cugraph.MultiGraph(directed=True)
cug_divisibility_graph.from_cudf_edgelist(
    cudf.DataFrame(
        [
            (f"numbers/{j}", f"numbers/{i}", j / i)
            for i in range(1, 101)
            for j in range(1, 101)
            if j % i == 0
        ],
        columns=["src", "dst", "weight"],
    ),
    source="src",
    destination="dst",
    edge_attr="weight",
    renumber=False,
)

## 2) Create ArangoDB Edge Definitions
edge_definitions = [
    {
        "edge_collection": "is_divisible_by",
        "from_vertex_collections": ["numbers"],
        "to_vertex_collections": ["numbers"],
    }
]

## 3) Convert cuGraph to ArangoDB
adb_graph = adbcug_adapter.cugraph_to_arangodb("DivisibilityGraph", cug_graph, edge_definitions)

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