Understanding Resources

Tutorial

Understanding Resources

Learn the Mapping → Instance resource model

25 min Beginner

ResourcesMappingInstanceLifecycle

What You'll Learn

Resource Model - Understand the Mapping → Instance flow
Mappings - Browse and inspect graph schema definitions
Instances - Create, connect, and manage graph databases
Lifecycle - Extend TTL and terminate instances

Setup

Connect to the Graph OLAP platform

# Cell 1 — Parameters
USERNAME = "_FILL_ME_IN_"  # Set your email before running

# Cell 2 — Connect
from graph_olap import GraphOLAPClient
client = GraphOLAPClient(username=USERNAME)

# Cell 3 — Provision
from notebook_setup import provision
personas, conn = provision(USERNAME)
analyst = personas["analyst"]
admin = personas["admin"]
ops = personas["ops"]
client = analyst

print(f"Connected to: {client._config.api_url}")

Working with Mappings

Browse available graph schema definitions

# List all available mappings
mappings = client.mappings.list()
print(f"Available mappings: {len(mappings)}")
for m in mappings:
    print(f"  [{m.id}] {m.name}")

# Find the tutorial mapping by name (not by index — parallel CI has multiple)
tutorial_mappings = [m for m in mappings if "tutorial-customer-graph" in m.name]
mapping = tutorial_mappings[0] if tutorial_mappings else mappings[0]
mapping_detail = client.mappings.get(mapping.id)
print(f"\nMapping detail: {mapping_detail.name} (id={mapping_detail.id})")

Creating Instances

Deploy a queryable graph database from a mapping

from graph_olap_schemas import WrapperType

# Create an instance directly from the tutorial mapping
instance = client.instances.create_and_wait(
    mapping_id=mapping.id,
    name="resources-demo",
    wrapper_type=WrapperType.RYUGRAPH,
    ttl="PT1H",
)
print(f"Instance: {instance.name} (status={instance.status})")

# Connect to OUR instance and query it to prove it works
demo_conn = client.instances.connect(instance.id)
result = demo_conn.query("MATCH (c:Customer) RETURN c.id AS name LIMIT 3")
result.show()

Instance Lifecycle

Extend TTL and terminate instances

# Extend instance TTL to keep it running longer
client.instances.extend_ttl(instance.id, hours=24)
print(f"Extended TTL for instance {instance.name} by 24 hours")

# Clean up the demo instance we created
try:
    client.instances.terminate(instance.id)
    print(f"Terminated instance: {instance.name}")
except Exception as e:
    print(f"Cleanup: {e}")

Key Takeaways

Mappings define the graph schema (nodes, edges, properties from warehouse tables)
Instances are created directly from mappings using create_and_wait()
Use extend_ttl() to keep instances running and terminate() to free resources
Resource lifecycle: pending → running → terminated