Connect G.V() to Aerospike Graph

This page guides you through loading sample data into AGS and connecting G.V() to visualize it. You will run a data loading script to populate the graph, then configure G.V() to connect to AGS.

Load sample data

The Java and Python demos from the basics tutorials drop all data after running queries. To visualize the data in G.V(), you need to reload it and keep it persisted.

Same data model

The following script creates the same graph data model used in both the Java and Python tutorials: five users, five accounts, ownership edges, and 50 random transactions. If you’ve already loaded data using a modified version of either example, you can skip to the “Connect G.V() to AGS” section.

1. Navigate to the Python basic example directory.

   cd aerospike-graph/python/basic

2. Create a modified script that loads data without cleanup.

   Create a file named load_data.py with the following content:

   import random
   import datetime
   from gremlin_python.process.anonymous_traversal import traversal
   from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection
   
   HOST = "localhost"
   PORT = 8182
   
   def convert_timestamp_to_long(date):
       timestamp = datetime.datetime.now().timestamp()
       return int(timestamp)
   
   def main():
       print("Connecting to Aerospike Graph Service...")
       cluster = DriverRemoteConnection(
           f"ws://{HOST}:{PORT}/gremlin", "g"
       )
       g = traversal().with_remote(cluster)
   
       if g.inject(0).next() != 0:
           print("Failed to connect to graph instance")
           exit()
       print("Connected to Aerospike Graph Service; Adding Data...")
   
       # Add Users
       user1 = g.add_v("User").property("userId", "U1").property("name", "Alice").property("age", 30).next()
       user2 = g.add_v("User").property("userId", "U2").property("name", "Bob").property("age", 35).next()
       user3 = g.add_v("User").property("userId", "U3").property("name", "Charlie").property("age", 25).next()
       user4 = g.add_v("User").property("userId", "U4").property("name", "Diana").property("age", 28).next()
       user5 = g.add_v("User").property("userId", "U5").property("name", "Eve").property("age", 32).next()
   
       # Add Accounts
       account1 = g.add_v("Account").property("accountId", "A1").property("balance", 5000).next()
       account2 = g.add_v("Account").property("accountId", "A2").property("balance", 3000).next()
       account3 = g.add_v("Account").property("accountId", "A3").property("balance", 4000).next()
       account4 = g.add_v("Account").property("accountId", "A4").property("balance", 2000).next()
       account5 = g.add_v("Account").property("accountId", "A5").property("balance", 6000).next()
   
       # Link Users to Accounts
       g.add_e("owns").from_(user1).to(account1).property("since", "2020").iterate()
       g.add_e("owns").from_(user2).to(account2).property("since", "2021").iterate()
       g.add_e("owns").from_(user3).to(account3).property("since", "2022").iterate()
       g.add_e("owns").from_(user4).to(account4).property("since", "2023").iterate()
       g.add_e("owns").from_(user5).to(account5).property("since", "2024").iterate()
   
       # Add sample transactions
       random.seed(42)
       for i in range(1, 51):
           accounts = g.V().has_label("Account").sample(2).to_list()
           if len(accounts) < 2:
               continue
           amount = random.randint(1, 1000)
           transaction_id = f"T{i}"
           type_ = "debit" if random.choice([True, False]) else "credit"
           timestamp = f"2025-{random.randint(1, 12):02d}-{random.randint(1, 28):02d}"
           g.add_e("Transaction") \
               .from_(accounts[0]).to(accounts[1]) \
               .property("transactionId", transaction_id) \
               .property("amount", amount) \
               .property("type", type_) \
               .property("timestamp", convert_timestamp_to_long(timestamp)) \
               .iterate()
   
       print("Data loaded successfully. Graph is ready for visualization.")
       cluster.close()
   
   if __name__ == "__main__":
       main()

3. Run the data loading script.

   python load_data.py

   Connecting to Aerospike Graph Service...
   Connected to Aerospike Graph Service; Adding Data...
   Data loaded successfully. Graph is ready for visualization.

   Expected result

   Verify: The script prints Data loaded successfully and exits without errors.

Connect G.V() to AGS

Now connect G.V() to your running AGS instance to visualize the loaded data. G.V() uses a connection wizard that guides you through four steps: Hostname Valid, Network Connectivity, Gremlin Compatibility, and Submit Connection.

1. Launch G.V() on your computer.

   Open the G.V() application from your Applications folder (macOS), Start menu (Windows), or by running the AppImage (Linux).

2. Open the connection dialog.

   Click the Connect button in the toolbar or select File > New Connection from the menu. The “Add a new Graph Database Connection” wizard opens.

   

3. Configure the initial connection settings.

   Enter the following connection details:

   Field	Value
   Graph Database Type	Aerospike Graph
   Database Hostname	localhost
   Port	8182 (auto-populated)

   The hostname field shows a “WS” prefix indicating WebSocket protocol.

4. Test the connection.

   Click TEST CONNECTION to verify connectivity. G.V() validates the connection through four steps:

   • Hostname Valid
   • Network Connectivity
   • Gremlin Compatibility
   • Submit Connection

   Verify: All four steps in the progress indicator turn green with checkmarks, and you see the message “Connection to localhost established successfully. Click on Submit to continue.”

   

5. Configure the connection details.

   After a successful connection test, additional fields appear:

   Field	Value
   Connection name	localhost (or your preferred name)
   Connection Color	Optional - choose a color to identify this connection
   Data Model Loading Method	Bulk (recommended for graphs with less than 1 million elements)

6. Submit the connection.

   Click SUBMIT to save the connection and connect to AGS.

   Verify: G.V() shows a connected status in the connection panel. The query editor becomes active.

7. Test the connection with a simple query.

   In the sidebar under your localhost connection, click New Query to open a query editor tab. Enter the following query:

   g.V().count()

   Click the green play button in the toolbar to execute the query, or press Cmd+Enter (macOS) or Ctrl+Enter (Windows/Linux).

   10

   Expected result

   Verify: The JSON Output section displays 10, representing the 5 User vertices and 5 Account vertices you loaded. The status bar shows the query execution time.

Tip

If the connection fails, verify that the Docker containers are running with docker compose ps. The aerospike-graph-service container must show a healthy status.

You are now connected to AGS and ready to run queries and visualize your graph data.

Checklist

• I’ve loaded sample data into AGS.
• I’ve connected G.V() to my AGS instance.
• I’ve verified the connection by viewing the graph.