Extend adapter with the functionality to export into cuGraph

.github/workflows/build.yml

@@ -24,21 +24,27 @@ env:
   TESTS_DIR: tests
 jobs:
   build:
-    runs-on: ubuntu-latest
+    runs-on: self-hosted
+    defaults:
+      run:
+        shell: bash -l {0}
     strategy:
       matrix:
-        python: ["3.6", "3.7", "3.8", "3.9", "3.10"]
-    name: Python ${{ matrix.python }}
+        python: ["3.7", "3.8"]
+    name: gpu
     steps:
       - uses: actions/checkout@v2
-      - name: Setup Python ${{ matrix.python }}
+      - name: Setup python
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python }}
       - name: Setup pip
         run: python -m pip install --upgrade pip setuptools wheel
-      - name: Install packages
-        run: pip install .[dev]
+      - name: Install dependencies
+        run: |
+          source ~/anaconda3/etc/profile.d/conda.sh
+          conda activate ${{ matrix.python }}
+          pip install .[dev]
       - name: Run black
         run: black --check --verbose --diff --color ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}}
       - name: Run flake8
@@ -47,10 +53,13 @@ jobs:
         run: isort --check --profile=black ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}}
       - name: Run mypy
         run: mypy ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}}
-      - name: Run pytest
-        run: py.test --cov=${{env.PACKAGE_DIR}} --cov-report xml -v --color=yes --no-cov-on-fail --code-highlight=yes
+      - name: Run pytest in conda env
+        run: |
+          source ~/anaconda3/etc/profile.d/conda.sh
+          conda activate ${{ matrix.python }}
+          conda run -n ${{ matrix.python }} py.test --cov=${{env.PACKAGE_DIR}} --cov-report xml -v --color=yes --no-cov-on-fail --code-highlight=yes
       - name: Publish to coveralls.io
         if: matrix.python == '3.8'
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: coveralls --service=github
+        run: coveralls --service=github

.github/workflows/release.yml

@@ -8,21 +8,27 @@ env:
   TESTS_DIR: tests
 jobs:
   build:
-    runs-on: ubuntu-latest
+    runs-on: self-hosted
+    defaults:
+      run:
+        shell: bash -l {0}
     strategy:
       matrix:
-        python: ["3.6", "3.7", "3.8", "3.9", "3.10"]
-    name: Python ${{ matrix.python }}
+        python: ["3.7", "3.8"]
+    name: gpu
     steps:
       - uses: actions/checkout@v2
-      - name: Setup Python ${{ matrix.python }}
+      - name: Setup python
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python }}
       - name: Setup pip
         run: python -m pip install --upgrade pip setuptools wheel
-      - name: Install packages
-        run: pip install .[dev]
+      - name: Install dependencies
+        run: |
+          source ~/anaconda3/etc/profile.d/conda.sh
+          conda activate ${{ matrix.python }}
+          pip install .[dev]
       - name: Run black
         run: black --check --verbose --diff --color ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}}
       - name: Run flake8
@@ -31,8 +37,11 @@ jobs:
         run: isort --check --profile=black ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}}
       - name: Run mypy
         run: mypy ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}}
-      - name: Run pytest
-        run: py.test --cov=${{env.PACKAGE_DIR}} --cov-report xml -v --color=yes --no-cov-on-fail --code-highlight=yes
+      - name: Run pytest in conda env
+        run: |
+          source ~/anaconda3/etc/profile.d/conda.sh
+          conda activate ${{ matrix.python }}
+          conda run -n ${{ matrix.python }} py.test --cov=${{env.PACKAGE_DIR}} --cov-report xml -v --color=yes --no-cov-on-fail --code-highlight=yes
       - name: Publish to coveralls.io
         if: matrix.python == '3.8'
         env:

README.md

@@ -1,4 +1,4 @@
-# ArangoDB-Networkx Adapter
+# ArangoDB-Networkx-cuGraph Adapter
 [![build](https://github.com/arangoml/networkx-adapter/actions/workflows/build.yml/badge.svg?branch=master)](https://github.com/arangoml/networkx-adapter/actions/workflows/build.yml)
 [![CodeQL](https://github.com/arangoml/networkx-adapter/actions/workflows/analyze.yml/badge.svg?branch=master)](https://github.com/arangoml/networkx-adapter/actions/workflows/analyze.yml)
 [![Coverage Status](https://coveralls.io/repos/github/arangoml/networkx-adapter/badge.svg?branch=master)](https://coveralls.io/github/arangoml/networkx-adapter)
@@ -12,9 +12,12 @@
 [![Downloads](https://img.shields.io/badge/dynamic/json?style=for-the-badge&color=282661&label=Downloads&query=total_downloads&url=https://api.pepy.tech/api/projects/adbnx-adapter)](https://pepy.tech/project/adbnx-adapter)
 
 <a href="https://www.arangodb.com/" rel="arangodb.com">![](./examples/assets/logos/ArangoDB_logo.png)</a>
-<a href="https://networkx.org/" rel="networkx.org">![](./examples/assets/logos/networkx_logo.svg)</a>
+<a href="https://networkx.org/" rel="networkx.org">![](./examples/assets/logos/networkx_logo.svg)</a>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
+<a href="https://github.com/rapidsai/cugraph" rel="github.com/rapidsai/cugraph"><img src="./examples/assets/logos/rapids-logo.png" width=30% height=30%>
+</a>
 
-The ArangoDB-Networkx Adapter exports Graphs from ArangoDB, a multi-model Graph Database, into NetworkX, the swiss army knife for graph analysis with python, and vice-versa.
+
+The ArangoDB-Networkx-cuGraph Adapter exports Graphs from ArangoDB, a multi-model Graph Database, into NetworkX, the swiss army knife for graph analysis with python, and vice-versa. Additionally you can export ArangoDB graphs into RAPIDS cuGraph library, which is a collection of GPU accelerated graph algorithms.
 
 
 
@@ -24,13 +27,17 @@ Networkx is a commonly used tool for analysis of network-data. If your analytics
 
     1. An algorithm for your use case is available in Networkx.
     2. A library that you want to use for your use case works with Networkx Graphs as input.
+
+## About RAPIDS cuGraph
+
+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, a Nvidia CUDA enabled GPU is required.
 
-##  Quickstart
+##  Quickstart: ArangoDB &rarr; NetworkX
 
 Get Started on Colab: <a href="https://colab.research.google.com/github/arangoml/networkx-adapter/blob/master/examples/ArangoDB_NetworkX_Adapter.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 
 ```py
-# Import the ArangoDB-NetworkX Adapter
+# Import the ArangoDB-NetworkX-cuGraph Adapter
 from adbnx_adapter.adapter import ADBNX_Adapter
 
 # Import a sample graph from NetworkX
@@ -84,6 +91,37 @@ adb_grid_edge_definitions = [
 ]
 adb_grid_graph = adbnx_adapter.networkx_to_arangodb("Grid", nx_grid_graph, adb_grid_edge_definitions)
 ```
+##  Quickstart: ArangoDB &rarr; cuGraph
+
+```py
+# Import the ArangoDB-NetworkX-cuGraph Adapter
+from adbnx_adapter.adapter import ADBNX_Adapter
+
+# This is the connection information for your ArangoDB instance
+# (Let's assume that the ArangoDB fraud-detection data dump is imported to this endpoint)
+con = {
+    "hostname": "localhost",
+    "protocol": "http",
+    "port": 8529,
+    "username": "root",
+    "password": "rootpassword",
+    "dbName": "_system",
+}
+
+# This instantiates your ADBNX Adapter with your connection credentials
+adbnx_adapter = ADBNX_Adapter(con)
+
+# ArangoDB to cuGraph via Graph
+nx_fraud_graph = adbnx_adapter.arangodb_graph_to_cugraph("fraud-detection")
+
+# ArangoDB to cuGraph via Collections
+nx_fraud_graph_2 = adbnx_adapter.arangodb_collections_to_cugraph(
+        "fraud-detection", 
+        {"account", "bank", "branch", "Class", "customer"}, # Specify vertex collections
+        {"accountHolder", "Relationship", "transaction"} # Specify edge collections
+)
+```
+
 
 ##  Development & Testing
 
@@ -94,4 +132,4 @@ Prerequisite: `arangorestore` must be installed
 3. `python -m venv .venv`
 4. `source .venv/bin/activate` (MacOS) or `.venv/scripts/activate` (Windows)
 5. `pip install -e .[dev]`
-6. `pytest`
+6. `pytest`

adbnx_adapter/abc.py

@@ -5,6 +5,13 @@
 from typing import Any, List, Set
 
 from arango.graph import Graph as ArangoDBGraph
+
+try:
+    from cugraph import MultiGraph as cuGraphMultiGraph
+
+    cugraph = True
+except ImportError:
+    cugraph = False
 from networkx.classes.graph import Graph as NetworkXGraph
 from networkx.classes.multidigraph import MultiDiGraph
 
@@ -33,6 +40,33 @@ def arangodb_collections_to_networkx(
     ) -> MultiDiGraph:
         raise NotImplementedError  # pragma: no cover
 
+    if cugraph is False:
+        pass
+    else:
+
+        def arangodb_to_cugraph(
+            self,
+            name: str,
+            metagraph: ArangoMetagraph,
+            is_keep: bool = True,
+            **query_options: Any,
+        ) -> cuGraphMultiGraph(directed=True):  # type: ignore
+            raise NotImplementedError  # pragma: no cover
+
+        def arangodb_collections_to_cugraph(
+            self,
+            name: str,
+            v_cols: Set[str],
+            e_cols: Set[str],
+            **query_options: Any,
+        ) -> cuGraphMultiGraph(directed=True):  # type: ignore
+            raise NotImplementedError  # pragma: no cover
+
+        def arangodb_graph_to_cugraph(
+            self, name: str, **query_options: Any
+        ) -> cuGraphMultiGraph(directed=True):  # type: ignore
+            raise NotImplementedError  # pragma: no cover
+
     def arangodb_graph_to_networkx(
         self, name: str, **query_options: Any
     ) -> MultiDiGraph:

adbnx_adapter/adapter.py

@@ -8,6 +8,21 @@
 from arango.cursor import Cursor
 from arango.graph import Graph as ArangoDBGraph
 from arango.result import Result
+
+try:
+    from cudf import DataFrame
+
+    cudf = True
+except ImportError as e:
+    print(e)
+    cudf = False
+try:
+    from cugraph import MultiGraph as cuGraphMultiGraph
+
+    cugraph = True
+except ImportError as e:
+    print(e)
+    cugraph = False
 from networkx import MultiDiGraph
 from networkx.classes.graph import Graph as NetworkXGraph
 from networkx.classes.multidigraph import MultiDiGraph as NetworkXMultiDiGraph
@@ -310,6 +325,135 @@ def networkx_to_arangodb(
         print(f"ArangoDB: {name} created")
         return adb_graph
 
+    if cugraph is False or cudf is False:
+        print(
+            "You are currently solely using the NetworkX export functionality.",
+            "Please note that modules 'cudf' and 'cugraph' are required to perform",
+            "exports into cuGraph. ",
+        )
+    else:
+
+        def arangodb_to_cugraph(
+            self,
+            name: str,
+            metagraph: ArangoMetagraph,
+            is_keep: bool = True,
+            **query_options: Any,
+        ) -> cuGraphMultiGraph(directed=True):  # type: ignore
+            """Create a cuGraph graph from graph attributes.
+
+            :param name: The cuGraph graph name.
+            :type name: str
+            :param metagraph: An object defining vertex & edge collections to import to
+                cuGraph, along with their associated attributes to keep.
+            :type metagraph: adbnx_adapter.typings.ArangoMetagraph
+            :param is_keep: Only keep the document attributes specified in **metagraph**
+                when importing to cuGraph (is True by default).
+            :type is_keep: bool
+            :param query_options: Keyword arguments to specify AQL query options when
+                fetching documents from the ArangoDB instance.
+            :type query_options: Any
+            :return: A Multi-Directed cuGraph Graph.
+            :rtype: cugraph.structure.graph_classes.MultiDiGraph
+            :raise ValueError: If missing required keys in metagraph
+
+            Here is an example entry for parameter **metagraph**:
+
+            .. code-block:: python
+            {
+                "vertexCollections": {
+                    "account": {"Balance", "account_type", "customer_id", "rank"},
+                    "bank": {"Country", "Id", "bank_id", "bank_name"},
+                    "customer": {"Name", "Sex", "Ssn", "rank"},
+                },
+                "edgeCollections": {
+                    "accountHolder": {},
+                    "transaction": {
+                        "transaction_amt", "receiver_bank_id", "sender_bank_id"
+                    },
+                },
+            }
+            """
+            self.__validate_attributes("graph", set(metagraph), self.METAGRAPH_ATRIBS)
+
+            # Maps ArangoDB vertex IDs to cuGraph node IDs
+            adb_map: Dict[str, Dict[str, Union[NxId, str]]] = dict()
+            cg_edges: List[Tuple[NxId, NxId]] = []
+
+            adb_v: Json
+            for col, atribs in metagraph["vertexCollections"].items():
+                for adb_v in self.__fetch_adb_docs(col, atribs, is_keep, query_options):
+                    adb_id: str = adb_v["_id"]
+                    nx_id = self.__cntrl._prepare_arangodb_vertex(adb_v, col)
+                    adb_map[adb_id] = {"nx_id": nx_id, "collection": col}
+
+            adb_e: Json
+            for col, atribs in metagraph["edgeCollections"].items():
+                for adb_e in self.__fetch_adb_docs(col, atribs, is_keep, query_options):
+                    from_node_id: NxId = adb_map[adb_e["_from"]]["nx_id"]
+                    to_node_id: NxId = adb_map[adb_e["_to"]]["nx_id"]
+                    self.__cntrl._prepare_arangodb_edge(adb_e, col)
+                    cg_edges.append((from_node_id, to_node_id))
+
+            srcs = [s for (s, _) in cg_edges]
+            dsts = [d for (_, d) in cg_edges]
+            cg_graph = cuGraphMultiGraph(directed=True)
+            cg_graph.from_cudf_edgelist(
+                DataFrame({"source": srcs, "destination": dsts})
+            )
+
+            print(f"cuGraph: {name} created")
+            return cg_graph
+
+        def arangodb_collections_to_cugraph(
+            self,
+            name: str,
+            v_cols: Set[str],
+            e_cols: Set[str],
+            **query_options: Any,
+        ) -> cuGraphMultiGraph(directed=True):  # type: ignore
+            """Create a cuGraph graph from ArangoDB collections.
+            :param name: The cuGraph graph name.
+            :type name: str
+            :param v_cols: A set of vertex collections to import to cuGraph.
+            :type v_cols: Set[str]
+            :param e_cols: A set of edge collections to import to cuGraph.
+            :type e_cols: Set[str]
+            :param query_options: Keyword arguments to specify AQL query options when
+                fetching documents from the ArangoDB instance.
+            :type query_options: Any
+            :return: A Multi-Directed cuGraph Graph.
+            :rtype: cugraph.structure.graph_classes.MultiDiGraph
+            """
+            metagraph: ArangoMetagraph = {
+                "vertexCollections": {col: set() for col in v_cols},
+                "edgeCollections": {col: set() for col in e_cols},
+            }
+
+            return self.arangodb_to_cugraph(
+                name, metagraph, is_keep=True, **query_options
+            )
+
+        def arangodb_graph_to_cugraph(
+            self, name: str, **query_options: Any
+        ) -> cuGraphMultiGraph(directed=True):  # type: ignore
+            """Create a cuGraph graph from an ArangoDB graph.
+            :param name: The ArangoDB graph name.
+            :type name: str
+            :param query_options: Keyword arguments to specify AQL query options when
+                fetching documents from the ArangoDB instance.
+            :type query_options: Any
+            :return: A Multi-Directed cuGraph Graph.
+            :rtype: cugraph.structure.graph_classes.MultiDiGraph
+            """
+            graph = self.__db.graph(name)
+            v_cols = graph.vertex_collections()
+            e_cols = {col["edge_collection"] for col in graph.edge_definitions()}
+
+            return self.arangodb_collections_to_cugraph(
+                name, v_cols, e_cols, **query_options
+            )
+
     def __validate_attributes(
         self, type: str, attributes: Set[str], valid_attributes: Set[str]
     ) -> None: