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Grand aims to reduce the difficulty of running graph algorithms at scale, while still preserving a familiar API. You can think of Grand as the Dask.DataFrame of graph technologies: Write using a familiar syntax, but let Grand handle running at scale behind the scenes.

The most obvious way to illustrate this is with an example. Here, we create a new graph using NetworkX syntax, but rather than cramming everything in RAM, data are stored in a SQL database:

import grand
from grand.backends import SQLBackend

G = grand.Graph(backend=SQLBackend("sqlite:///demo.db"))

G.nx.add_edge("foo", "bar")

Most networkx functions will work on a Grand graph: Just call them on G.nx:

print(G.nx.degree())

Now let's talk about Grand magic: Even though we created this graph using networkx function calls, we can interact with it using other "dialects" — such as iGraph:

print(len(G.igraph.vs)) # `2`

The intention of these dialects is for any graph library that is compatible with igraph or networkx to also be compatible with Grand. In other words, Grand automatically re-interprets slow, Python based algorithms as fast operations that run in Grand backends such as SQL or DynamoDB.

Learn more about Dialects here.

Learn more about Backends here.