Instance Lifecycle

Tutorial

Instance Lifecycle

Understand TTL, health checks, and instance states

30 min Advanced

InstanceLifecycleTTLHealth

What You'll Learn

• Lifecycle States - pending → running → terminated
• TTL Management - Automatic and manual termination
• Health Monitoring - Liveness and readiness probes
• Progress Tracking - Monitor long-running operations

# 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

mapping = client.mappings.list(search="tutorial-customer-graph").items[0]
print(f"Connected to: {client._config.api_url}")
print(f"Using mapping: {mapping.name} (id={mapping.id})")

1

Instance States

Lifecycle state machine

from graph_olap_schemas import WrapperType

# Instance state transitions
# ┌─────────┐    ┌──────────┐    ┌─────────┐    ┌────────────┐
# │ pending │───>│ starting │───>│ running │───>│ terminated │
# └─────────┘    └──────────┘    └─────────┘    └────────────┘
#                                     │
#                                     v
#                               ┌──────────┐
#                               │ stopping │
#                               └──────────┘

# Create an instance and observe its initial state
instance = client.instances.create_and_wait(
    mapping_id=mapping.id,
    name="lifecycle-demo",
    wrapper_type=WrapperType.RYUGRAPH,
    timeout=300,
    ttl="PT4H",  # 4-hour time-to-live
)

# Check the current state
print(f"Instance ID: {instance.id}")
print(f"Status:      {instance.status}")
print(f"Wrapper:     {instance.wrapper_type}")
print(f"Expires at:  {instance.expires_at}")

2

TTL and Termination

Automatic cleanup

3

Health Checks

Monitoring instance health

# Health checks — two flavours:

# 1. Detailed health info (returns dict with status details)
health = client.instances.get_health(instance.id)
print("Detailed health:")
for key, value in health.items():
    print(f"  {key}: {value}")

# 2. Quick boolean check
is_healthy = client.instances.check_health(instance.id)
print(f"\nHealthy? {is_healthy}")

4

Progress Events

Track operation progress

# Progress tracking for long-running operations
# (create_and_wait handles polling internally, but you can also
# check progress manually during instance creation)

progress = client.instances.get_progress(instance.id)
print(f"Phase:    {progress.phase}")
print(f"Progress: {progress.progress_percent}%")
print(f"Steps:    {progress.steps}")

Key Takeaways

• Instances transition: pending → starting → running → stopping → terminated
• Use create_and_wait() with a ttl (ISO 8601 duration) to auto-terminate
• extend_ttl(id, hours=N) postpones automatic termination
• get_health() returns detailed status; check_health() returns a boolean
• get_progress() provides phase, progress_percent, and steps for long operations

# Cleanup: terminate the lifecycle-demo instance
try:
    client.instances.terminate(instance.id)
    print(f"Terminated instance {instance.id}")
except Exception as e:
    print(f"Instance cleanup: {e}")