Quick start using AWS CloudShell
You can connect to your Neptune Analytics graph with a single click through
AWS CloudShell
directly from the AWS Management Console.
CloudShell provides a pre-authenticated shell environment with the
graphsh tool pre-installed, connecting to your graph's
public endpoint. You don't need to provision notebooks to interact with the graph.
Time to complete: Approximately 2 minutes
Cost: CloudShell is available at no additional charge.
Neptune Analytics graphs incur charges based on provisioned memory.
For pricing details, see
Neptune Analytics pricing
Prerequisites
An AWS account with appropriate IAM permissions.
A Neptune Analytics graph with public connectivity enabled and status Available. If you don't already have one, see Create an empty Neptune graph. When creating your graph, you must enable public connectivity.
Step 1: Connect to your Neptune Analytics graph
Connect to your Neptune Analytics graph using AWS CloudShell.
Note
You must enable public connectivity to connect to a Neptune Analytics graph using CloudShell.
To connect to your graph
-
In the Neptune console, in the navigation pane, choose Graphs.
-
Select the check box next to the graph that you created.
-
Choose Connect to graph.
Note
This button is active when you select a graph with Available status and public connectivity turned on.
The CloudShell Run command screen appears.
-
Choose Run to connect to the graph.
After you connect, the graphsh console appears.
Step 2: Insert and query data
Now that you are connected to your graph, run a few queries to get familiar with Neptune Analytics. The following examples use openCypher, the query language supported by Neptune Analytics.
To insert nodes and relationships
Run the following query to create two person nodes and a relationship:
CREATE (a:Person {name: 'Alice', age: 30}), (b:Person {name: 'Bob', age: 40}), (a)-[:KNOWS]->(b)
The output looks similar to the following:
╭────────╮ │ result │ ├────────┤ │ [] │ ╰────────╯
To find nodes
Run the following query to return all person names in alphabetical order:
MATCH (p:Person) RETURN p.name AS name ORDER BY name
The output looks similar to the following:
╭───────╮ │ name │ ├───────┤ │ Alice │ │ Bob │ ╰───────╯
To run an algorithm
Run a breadth-first search (BFS) to find nodes reachable from Alice:
MATCH (n {name: "Alice"}) CALL neptune.algo.bfs(n, {edgeLabels: ["KNOWS"]}) YIELD node RETURN node
The output looks similar to the following (order might vary):
╭──────────────────────────────────────────────────────────────────────╮ │ node │ ├──────────────────────────────────────────────────────────────────────┤ │ (:Person {~id: "...", age: 40, name: "Bob"}) │ │ (:Person {~id: "...", age: 30, name: "Alice"}) │ ╰──────────────────────────────────────────────────────────────────────╯
Next steps
You have completed this quick start. To learn more, explore the following features:
Managing your Neptune Analytics graphs – Manage your graphs, endpoints, and configurations.
Neptune Analytics algorithms – Run built-in graph algorithms on your data.
Working with vector similarity in Neptune Analytics – Use vector embeddings for similarity search.
To avoid ongoing charges, delete the graph if you created it only for this quick start. For instructions, see Managing your Neptune Analytics graphs.
