nx-arangodb 1.1.0

ArangoDB backend for NetworkX

nx-arangodb

https://github.com/user-attachments/assets/e5f56574-d3ef-452c-ab21-b47b3d5d5900

What is this?

This is a backend to NetworkX that offers ArangoDB as a Persistence Layer to NetworkX Graphs:

1. Persist NetworkX Graphs to ArangoDB.
2. Reload NetworkX Graphs from ArangoDB.
3. Perform CRUD on ArangoDB Graphs via NetworkX.
4. Run algorithms (CPU & GPU) on ArangoDB Graphs via NetworkX.

Benefits of having ArangoDB as a backend to NetworkX include:

1. No need to re-create the graph every time you start a new session.
2. Access to GPU-accelerated graph analytics (nx-cugraph).
3. Access to a database query language (Arango Query Language).
4. Access to a visual interface for graph exploration (ArangoDB Web UI).
5. Access to cross-collaboration on the same graph (ArangoDB Cloud).
6. Access to efficient distribution of graph data (ArangoDB SmartGraphs).

Does this replace NetworkX?

Not really. This is a plugin to NetworkX, which means that you can use NetworkX as you normally would, but with the added benefit of persisting your graphs to a database.

import os
import networkx as nx
import nx_arangodb as nxadb

os.environ["DATABASE_HOST"] = "http://localhost:8529"
os.environ["DATABASE_USERNAME"] = "root"
os.environ["DATABASE_PASSWORD"] = "openSesame"
os.environ["DATABASE_NAME"] = "_system"

G = nxadb.Graph(name="MyGraph")

G.add_node(1, foo='bar')
G.add_node(2, bar='foo')
G.add_edge(1, 2, weight=2)

res = nx.pagerank(G)

for k, v in res.items():
    G.nodes[k]['pagerank'] = v

Does this mean I need to learn ArangoDB?

No. You can use nx-arangodb without knowing anything about ArangoDB. The UX of nx-arangodb is designed to be as close as possible to the UX of NetworkX. See the ReadTheDocs for a list of features that are currently unsupported/in-development.

import os
import networkx as nx
import nx_arangodb as nxadb

# os.environ ...

# Re-connect to the graph
G = nxadb.Graph(name="MyGraph")

assert G.number_of_nodes() == 2
assert G.number_of_edges() == 1

How do I install it?

pip install nx-arangodb

What if I want to use nx-cuGraph with it?

pip install nx-cugraph-cu12 --extra-index-url https://pypi.nvidia.com
pip install nx-arangodb

How can I set up ArangoDB?

1) Local Instance via Docker

Appears on localhost:8529 with the user root & password openSesame.

More info: arangodb.com/download-major.

docker run -e ARANGO_ROOT_PASSWORD=openSesame -p 8529:8529 arangodb/arangodb

2) ArangoDB Cloud Trial

ArangoGraph is ArangoDB’s Cloud offering to use ArangoDB as a managed service.

A 14-day trial is available upon sign up.

3) Temporary Cloud Instance via Python

A temporary cloud database can be provisioned using the adb-cloud-connector python package.

# !pip install adb-cloud-connector

import os
from adb_cloud_connector import get_temp_credentials

credentials = get_temp_credentials()

os.environ["DATABASE_HOST"] = credentials["url"]
os.environ["DATABASE_USERNAME"] = credentials["username"]
os.environ["DATABASE_PASSWORD"] = credentials["password"]
os.environ["DATABASE_NAME"] = credentials["dbName"]

# ...

How does algorithm dispatching work?

nx-arangodb will automatically dispatch algorithm calls to either CPU or GPU based on if nx-cugraph is installed. We rely on a rust-based library called phenolrs to retrieve ArangoDB Graphs as fast as possible.

You can also force-run algorithms on CPU even if nx-cugraph is installed:

import os
import networkx as nx
import nx_arangodb as nxadb

# os.environ ...

G = nxadb.Graph(name="MyGraph")

nx.config.backends.arangodb.use_gpu = False

nx.pagerank(G)
nx.betweenness_centrality(G)
# ...

nx.config.backends.arangodb.use_gpu = True

Can I create an ArangoDB Graph from an existing NetworkX Graph?

Yes, this is actually the recommended way to start using nx-arangodb:

import os
import networkx as nx
import nx_arangodb as nxadb

# os.environ ...

G_nx = nx.karate_club_graph()

G_nxadb = nxadb.Graph(
    incoming_graph_data=G_nx,
    name="MyKarateGraph"
)

assert G_nxadb.number_of_nodes() == G_nx.number_of_nodes()
assert G_nxadb.number_of_edges() == G_nx.number_of_edges()