graph-notebook 4.2.0

jupyter notebook extension to connect to graph databases

Graph Notebook: easily query and visualize graphs

The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Using this open-source Python package, you can connect to any graph database that supports the Apache TinkerPop, openCypher or the RDF SPARQL graph models. These databases could be running locally on your desktop or in the cloud. Graph databases can be used to explore a variety of use cases including knowledge graphs and identity graphs.

Visualizing Gremlin queries

Visualizing openCypher queries

Visualizing SPARQL queries

Instructions for connecting to the following graph databases:

Endpoint	Graph model	Query language
Gremlin Server	property graph	Gremlin
Blazegraph	RDF	SPARQL
Amazon Neptune	property graph or RDF	Gremlin, openCypher, or SPARQL
Neo4J	property graph	Cypher

We encourage others to contribute configurations they find useful. There is an additional-databases folder where more information can be found.

Features

Notebook cell 'magic' extensions in the IPython 3 kernel

%%sparql - Executes a SPARQL query against your configured database endpoint. Documentation

%%gremlin - Executes a Gremlin query against your database using web sockets. The results are similar to those a Gremlin console would return. Documentation

%%opencypher or %%oc Executes an openCypher query against your database. Documentation

%%graph_notebook_config - Sets the executing notebook's database configuration to the JSON payload provided in the cell body.

%%graph_notebook_vis_options - Sets the executing notebook's vis.js options to the JSON payload provided in the cell body.

%%neptune_ml - Set of commands to integrate with NeptuneML functionality, as described here. Documentation

TIP :point_right: %%sparql, %%gremlin, and %%oc share a suite of common arguments that be used to customize the appearance of rendered graphs. Example usage of these arguments can also be found in the sample notebooks under 02-Visualization.

TIP :point_right: There is syntax highlighting for language query magic cells to help you structure your queries more easily.

Notebook line 'magic' extensions in the IPython 3 kernel

%gremlin_status - Obtain the status of Gremlin queries. Documentation

%sparql_status - Obtain the status of SPARQL queries. Documentation

%opencypher_status or %oc_status - Obtain the status of openCypher queries. Documentation

%load - Generate a form to submit a bulk loader job. Documentation

%load_ids - Get ids of bulk load jobs. Documentation

%load_status - Get the status of a provided load_id. Documentation

%cancel_load - Cancels a bulk load job. You can either provide a single load_id, or specify --all-in-queue to cancel all queued (and not actively running) jobs. Documentation

%neptune_ml - Set of commands to integrate with NeptuneML functionality, as described here. You can find a set of tutorial notebooks here. Documentation

%status - Check the Health Status of the configured host endpoint. Documentation

%seed - Provides a form to add data to your graph, using sets of insert queries instead of a bulk loader. Sample RDF and Property Graph data models are provided with this command. Alternatively, you can select a language type and provide a file path(or a directory path containing one or more of these files) to load the queries from.

%stream_viewer - Interactively explore the Neptune CDC stream (if enabled)

%graph_notebook_config - Returns a JSON payload that contains connection information for your host.

%graph_notebook_host - Set the host endpoint to send queries to.

%graph_notebook_version - Print the version of the graph-notebook package

%graph_notebook_vis_options - Print the Vis.js options being used for rendered graphs

TIP :point_right: You can list all the magics installed in the Python 3 kernel using the %lsmagic command.

TIP :point_right: Many of the magic commands support a --help option in order to provide additional information.

Example notebooks

This project includes many example Jupyter notebooks. It is recommended to explore them. All of the commands and features supported by graph-notebook are explained in detail with examples within the sample notebooks. You can find them here. As this project has evolved, many new features have been added. If you are already familiar with graph-notebook but want a quick summary of new features added, a good place to start is the Air-Routes notebooks in the 02-Visualization folder.

Keeping track of new features

It is recommended to check the ChangeLog.md file periodically to keep up to date as new features are added.

Prerequisites

You will need:

• Python 3.8.x-3.10.13
• A graph database that provides one or more of:
  • A SPARQL 1.1 endpoint
  • An Apache TinkerPop Gremlin Server compatible endpoint
  • An endpoint compatible with openCypher

Installation

Begin by installing graph-notebook and its prerequisites, then follow the remaining instructions for either Jupyter Classic Notebook or JupyterLab.

# install the package
pip install graph-notebook

Jupyter Classic Notebook

# Enable the visualization widget
jupyter nbextension enable  --py --sys-prefix graph_notebook.widgets

# copy static html resources
python -m graph_notebook.static_resources.install
python -m graph_notebook.nbextensions.install

# copy premade starter notebooks
python -m graph_notebook.notebooks.install --destination ~/notebook/destination/dir

# create nbconfig file and directory tree, if they do not already exist
mkdir ~/.jupyter/nbconfig
touch ~/.jupyter/nbconfig/notebook.json

# start jupyter notebook
python -m graph_notebook.start_notebook --notebooks-dir ~/notebook/destination/dir

JupyterLab 3.x

# install jupyterlab
pip install "jupyterlab>=3,<4"

# copy premade starter notebooks
python -m graph_notebook.notebooks.install --destination ~/notebook/destination/dir

# start jupyterlab
python -m graph_notebook.start_jupyterlab --jupyter-dir ~/notebook/destination/dir

Loading magic extensions in JupyterLab

When attempting to run a line/cell magic on a new notebook in JupyterLab, you may encounter the error:

UsageError: Cell magic `%%graph_notebook_config` not found.

To fix this, run the following command, then restart JupyterLab.

python -m graph_notebook.ipython_profile.configure_ipython_profile

Alternatively, the magic extensions can be manually reloaded for a single notebook by running the following command in any empty cell.

%load_ext graph_notebook.magics

Upgrading an existing installation

# upgrade graph-notebook
pip install graph-notebook --upgrade

After the above command completes, rerun the commands given at Jupyter Classic Notebook or JupyterLab 3.x based on which flavour is installed.

Connecting to a graph database

Configuration options can be set using the %graph_notebook_config magic command. The command accepts a JSON object as an argument. The JSON object can contain any of the configuration options listed below. The command can be run multiple times to change the configuration. The configuration is stored in the notebook's metadata and will be used for all subsequent queries.

Configuration Option	Description	Default Value	Type
auth_mode	The authentication mode to use for Amazon Neptune connections	DEFAULT	string
aws_region	The AWS region to use for Amazon Neptune connections	your-region-1	string
host	The host url to form a connection with	localhost	string
load_from_s3_arn	The ARN of the S3 bucket to load data from [Amazon Neptune only]		string
neptune_service	The name of the Neptune service for the host url [Amazon Neptune only]	neptune-db	string
port	The port to use when creating a connection	8182	number
proxy_host	The proxy host url to route a connection through [Amazon Neptune only]		string
proxy_port	The proxy port to use when creating proxy connection [Amazon Neptune only]	8182	number
ssl	Whether to make connections to the created endpoint with ssl or not [True/False]	False	boolean
ssl_verify	Whether to verify the server's TLS certificate or not [True/False]	True	boolean
sparql	SPARQL connection object	{ "path": "sparql" }	string
gremlin	Gremlin connection object	{ "username": "", "password": "", "traversal_source": "g", "message_serializer": "graphsonv3" }	string
neo4j	Neo4J connection object	{ "username": "neo4j", "password": "password", "auth": true, "database": null }	string

Gremlin Server

In a new cell in the Jupyter notebook, change the configuration using %%graph_notebook_config and modify the fields for host, port, and ssl. Optionally, modify traversal_source if your graph traversal source name differs from the default value, username and password if required by the graph store, or message_serializer for a specific data transfer format. For a local Gremlin server (HTTP or WebSockets), you can use the following command:

%%graph_notebook_config
{
  "host": "localhost",
  "port": 8182,
  "ssl": false,
  "gremlin": {
    "traversal_source": "g",
    "username": "",
    "password": "",
    "message_serializer": "graphsonv3"
  }
}

To setup a new local Gremlin Server for use with the graph notebook, check out additional-databases/gremlin server

Blazegraph

Change the configuration using %%graph_notebook_config and modify the fields for host, port, and ssl. For a local Blazegraph database, you can use the following command:

%%graph_notebook_config
{
  "host": "localhost",
  "port": 9999,
  "ssl": false,
  "sparql": {
    "path": "sparql"
  }
}

You can also make use of namespaces for Blazegraph by specifying the path graph-notebook should use when querying your SPARQL like below:

%%graph_notebook_config

{
  "host": "localhost",
  "port": 9999,
  "ssl": false,
  "sparql": {
    "path": "blazegraph/namespace/foo/sparql"
  }
}

This will result in the url localhost:9999/blazegraph/namespace/foo/sparql being used when executing any %%sparql magic commands.

To setup a new local Blazegraph database for use with the graph notebook, check out the Quick Start from Blazegraph.

Amazon Neptune

Change the configuration using %%graph_notebook_config and modify the defaults as they apply to your Neptune instance.

Neptune DB

%%graph_notebook_config
{
  "host": "your-neptune-endpoint",
  "neptune_service": "neptune-db",
  "port": 8182,
  "auth_mode": "DEFAULT",
  "load_from_s3_arn": "",
  "ssl": true,
  "ssl_verify": true,
  "aws_region": "your-neptune-region"
}

Neptune Analytics

%%graph_notebook_config
{
  "host": "your-neptune-endpoint",
  "neptune_service": "neptune-graph",
  "port": 443,
  "auth_mode": "IAM",
  "ssl": true,
  "ssl_verify": true,
  "aws_region": "your-neptune-region"
}

To setup a new Amazon Neptune cluster, check out the Amazon Web Services documentation.

When connecting the graph notebook to Neptune via a private endpoint, make sure you have a network setup to communicate to the VPC that Neptune runs on. If not, you can follow this guide.

In addition to the above configuration options, you can also specify the following options:

Amazon Neptune Proxy Connection

%%graph_notebook_config
{
  "host": "clustername.cluster-ididididid.us-east-1.neptune.amazonaws.com",
  "neptune_service": "neptune-db",
  "port": 8182,
  "ssl": true,
  "proxy_port": 8182,
  "proxy_host": "host.proxy.com",
  "auth_mode": "IAM",
  "aws_region": "us-east-1",
  "load_from_s3_arn": ""
}

See also: Connecting to Amazon Neptune from clients outside the Neptune VPC using AWS Network Load Balancer

Authentication (Amazon Neptune)

If you are running a SigV4 authenticated endpoint, ensure that your configuration has auth_mode set to IAM:

%%graph_notebook_config
{
  "host": "your-neptune-endpoint",
  "neptune_service": "neptune-db",
  "port": 8182,
  "auth_mode": "IAM",
  "load_from_s3_arn": "",
  "ssl": true,
  "ssl_verify": true,
  "aws_region": "your-neptune-region"
}

Additionally, you should have the following Amazon Web Services credentials available in a location accessible to Boto3:

• Access Key ID
• Secret Access Key
• Default Region
• Session Token (OPTIONAL. Use if you are using temporary credentials)

These variables must follow a specific naming convention, as listed in the Boto3 documentation

A list of all locations checked for Amazon Web Services credentials can also be found here.

Neo4J

Change the configuration using %%graph_notebook_config and modify the fields for host, port, ssl, and neo4j authentication.

If your Neo4J instance supports multiple databases, you can specify a database name via the database field. Otherwise, leave the database field blank to query the default database.

For a local Neo4j Desktop database, you can use the following command:

%%graph_notebook_config
{
  "host": "localhost",
  "port": 7687,
  "ssl": false,
  "neo4j": {
    "username": "neo4j",
    "password": "password",
    "auth": true,
    "database": ""
  }
}

Ensure that you also specify the %%oc bolt option when submitting queries to the Bolt endpoint.

To setup a new local Neo4J Desktop database for use with the graph notebook, check out the Neo4J Desktop User Interface Guide.

Building From Source

A pre-release distribution can be built from the graph-notebook repository via the following steps:

# 1) Clone the repository and navigate into the clone directory
git clone https://github.com/aws/graph-notebook.git
cd graph-notebook

# 2) Create a new virtual environment

# 2a) Option 1 - pyenv
pyenv install 3.10.13  # Only if not already installed; this can be any supported Python 3 version in Prerequisites
pyenv virtualenv 3.10.13 build-graph-notebook
pyenv local build-graph-notebook

# 2b) Option 2 - venv
rm -rf /tmp/venv
python3 -m venv /tmp/venv
source /tmp/venv/bin/activate

# 3) Install build dependencies
pip install --upgrade pip setuptools wheel twine
pip install "jupyterlab>=3,<4"

# 4) Build the distribution
python3 setup.py bdist_wheel

You should now be able to find the built distribution at

./dist/graph_notebook-4.2.0-py3-none-any.whl

And use it by following the installation steps, replacing

pip install graph-notebook

with

pip install ./dist/graph_notebook-4.1.0-py3-none-any.whl

Contributing Guidelines

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.