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Quick launch Amazon Neptune and graph-app-kit

Amazon Neptune is a:

... "fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets"

Amazon Neptune supports both property graph queries with Apache Gremlin/Tinkerpop queries, and RDF graphs with SPARQL queries. By using graph-app-kit with Amazon Neptune, you can visually explore graph database data and share point-and-click dashboard tools.

This guides walks through quick launch scripts for Neptune and Neptune-aware graph-app-kit. Alternatively, you may follow our manual Neptune setup guide.

1. Setup Amazon Neptune with identity graph demo data

Launch using a button at the bottom of the identity graph sample cloud formation templates tutorial:

  1. Click the Launch Stack button for your region:
    • If launching the Full graph-app-kit template or using a GPU instance, use an AWS region with 4+ vCPU quota of g4dn/p3/p4 (or request it)
  2. Check the acknowledgement boxes in the Capabilities section
  3. Click Create Stack (5-20min)

Important Output: Open the root Identity-Graph-Sample item's Output tab to see the values you will fill in to configure the next steps:

  • VPC: ID vpc-abc
  • PublicSubnet1: ID subnet-abc
  • DBClusterReadEndpoint: URL abc.cluster-ro-xyz.zzz.neptune.amazonaws.com

Manage:

  • Neptune: AWS console -> Services -> Neptune -> Databases

  • Stack (inspect/delete): AWS Console -> Services -> CloudFormation -> Stacks

  • Resize ($):

    • Above Neptune console -> Databases -> Modify the Writer
    • Change DB instance class to db.r4.large -> Continue -> check Apply immediately -> Modify DB Instance

2. Launch graph-app-kit configured for Amazon Neptune

Configuration 1: Full (Recommended)

Launch Stack Full stack

Launches:

  • GPU EC2 instance in your Neptune VPC (request 4+ vCPU of g4, p3, or p4 capacity if none)
  • Start making views for Neptune data immediately
  • Web-based live editing
  • Graphistry, public + private Streamlit dashboards, Jupyter notebooks, RAPIDS.ai Python GPU ecosystem

If AWS warns Please select another region, use the Select a Region dropdown in the top right menu.

Configuration 2: Minimal

  1. Get a free or self-managed Graphistry server account with username+password

  2. Launch Stack Minimal stack

Launches:

  • CPU EC2 instance in your Neptune VPC
  • Create Neptune views from your terminal
  • Public Streamlit dashboards linked against a remote Graphistry account
  • Not included: Local Graphistry server, private dashboards, Jupyter notebooks, RAPIDS.ai GPU ecosystem

If AWS reports Please select another region, use the Select a Region dropdown in the top right menu.

Launch configuration: Details parameters

* Set stack name to anything, such as `graph-app-kit-a`
* Set `VPC` to the `VPC` ID value ("`vpc-...`") from `1. Setup Amazon Neptune`
* Set `Subnet` to the `PublicSubnet1` subnet ID value ("`subnet-...`") from `1. Setup Amazon Neptune`
* Set `GraphAppKitKeyPair` to one where you have a private.key for SSH'ing
* If using the minimal template, fill in details for connecting to a remote Graphistry server

Optional: Monitor instance launch for progress and errors

  • Resources tab -> EC2 instance link -> click instance for details (public IP, ...)

  • Login and watch:

ssh -i /my/private.key ubuntu@the.instance.public.ip 
### ssh -i /my/private.key ec2-user@the.instance.public.ip for Minimal launcher

tail -f /var/log/cloud-init-output.log -n 1000

3. Graph!

  • Go to your public Streamlit dashboard: http://[the.public.ip.address]/public/dash
  • Select GREMLIN: SIMPLE SAMPLE from the dropdown to load a random sample of nodes from whatever Neptune database is connected

Login

  • Login into the web portal at http://[the.public.ip.address] using credentials admin / i-theInstanceID

    • The minimal launcher has no web-based account portal

  • Optional: SSH using the instructions from step 2

Launched URLs for full stack

  • Graphistry: GPU-accelerated visual analytics + account login
    • http://[the.public.ip.address]
    • Login as admin / your-aws-instance-id
    • Installed at /home/ubuntu/graphistry
    • You can change your admin password using the web UI
  • Streamlit: Public dashboards
    • http://[the.public.ip.address]/public/dash
    • Installed at /home/ubuntu/graph-app-kit/public/graph-app-kit
    • Run as src/docker $ docker-compose -p pub run -d --name streamlit-pub streamlit
  • Streamlit: Private dashboards
    • http://[the.public.ip.address]/private/dash
    • Installed at /home/ubuntu/graph-app-kit/private/graph-app-kit
    • Run as src/docker $ docker-compose -p priv run -d --name streamlit-priv streamlit
  • Jupyter: Data science notebooks + Streamlit dashboard live-editing
    • http://[the.public.ip.address]/notebook
    • Live-edit graph-app-kit view folders notebook/graph-app-kit/[public,private]/views

Launched URLs for minimal stack

  • Streamlit: Public dashboards
    • http://[the.public.ip.address]/public/dash
    • Installed at /home/ubuntu/graph-app-kit/public/graph-app-kit
    • Run as src/docker $ docker-compose up -d streamlit

4. Next steps

Continue to the instructions for creating custom views and adding common extensions like TLS, public/private dashboards, and more

For more advanced Neptune configuration options, see the manual Amazone Neptune setup guide.