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Learning Notes #38 – Choreography Pattern | Cloud Pattern
5 January 2025 at 12:21
Today i learnt about Choreography pattern, where each and every service is communicating using a messaging queue. In this blog, i jot down notes on choreography pattern for my future self.
What is the Choreography Pattern?
In the Choreography Pattern, services communicate directly with each other via asynchronous events, without a central controller. Each service is responsible for a specific part of the workflow and responds to events produced by other services. This pattern allows for a more autonomous and loosely coupled system.
Key Features
- High scalability and independence of services.
- Decentralized control.
- Services respond to events they subscribe to.
When to Use the Choreography Pattern
- Event-Driven Systems: When workflows can be modeled as events triggering responses.
- High Scalability: When services need to operate independently and scale autonomously.
- Loose Coupling: When minimizing dependencies between services is critical.
Benefits of the Choreography Pattern
- Decentralized Control: No single point of failure or bottleneck.
- Increased Flexibility: Services can be added or modified without affecting others.
- Better Scalability: Services operate independently and scale based on their workloads.
- Resilience: The system can handle partial failures more gracefully, as services continue independently.
Example: E-Commerce Order Fulfillment
Problem
A fictional e-commerce platform needs to manage the following workflow:
- Accepting an order.
- Validating payment.
- Reserving inventory.
- Sending notifications to the customer.
Each step is handled by an independent service.
Solution
Using the Choreography Pattern, each service listens for specific events and publishes new events as needed. The workflow emerges naturally from the interaction of these services.
Implementation
Step 1: Define the Workflow as Events
- OrderPlaced: Triggered when a customer places an order.
- PaymentProcessed: Triggered after successful payment.
- InventoryReserved: Triggered after reserving inventory.
- NotificationSent: Triggered when the customer is notified.
Step 2: Implement Services
Each service subscribes to events and performs its task.
shared_utility.py
import pika
import json
def publish_event(exchange, event_type, data):
connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
channel.exchange_declare(exchange=exchange, exchange_type='fanout')
message = json.dumps({"event_type": event_type, "data": data})
channel.basic_publish(exchange=exchange, routing_key='', body=message)
connection.close()
def subscribe_to_event(exchange, callback):
connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
channel.exchange_declare(exchange=exchange, exchange_type='fanout')
queue = channel.queue_declare('', exclusive=True).method.queue
channel.queue_bind(exchange=exchange, queue=queue)
channel.basic_consume(queue=queue, on_message_callback=callback, auto_ack=True)
print(f"Subscribed to events on exchange '{exchange}'")
channel.start_consuming()
Order Service
from shared_utils import publish_event
def place_order(order_id, customer):
print(f"Placing order {order_id} for {customer}")
publish_event("order_exchange", "OrderPlaced", {"order_id": order_id, "customer": customer})
if __name__ == "__main__":
# Simulate placing an order
place_order(order_id=101, customer="John Doe")
Payment Service
from shared_utils import publish_event, subscribe_to_event
import time
def handle_order_placed(ch, method, properties, body):
event = json.loads(body)
if event["event_type"] == "OrderPlaced":
order_id = event["data"]["order_id"]
print(f"Processing payment for order {order_id}")
time.sleep(1) # Simulate payment processing
publish_event("payment_exchange", "PaymentProcessed", {"order_id": order_id})
if __name__ == "__main__":
subscribe_to_event("order_exchange", handle_order_placed)
Inventory Service
from shared_utils import publish_event, subscribe_to_event
import time
def handle_payment_processed(ch, method, properties, body):
event = json.loads(body)
if event["event_type"] == "PaymentProcessed":
order_id = event["data"]["order_id"]
print(f"Reserving inventory for order {order_id}")
time.sleep(1) # Simulate inventory reservation
publish_event("inventory_exchange", "InventoryReserved", {"order_id": order_id})
if __name__ == "__main__":
subscribe_to_event("payment_exchange", handle_payment_processed)
Notification Service
from shared_utils import subscribe_to_event
import time
def handle_inventory_reserved(ch, method, properties, body):
event = json.loads(body)
if event["event_type"] == "InventoryReserved":
order_id = event["data"]["order_id"]
print(f"Notifying customer for order {order_id}")
time.sleep(1) # Simulate notification
print(f"Customer notified for order {order_id}")
if __name__ == "__main__":
subscribe_to_event("inventory_exchange", handle_inventory_reserved)
Step 3: Run the Workflow
- Start RabbitMQ using Docker as described above.
- Run the services in the following order:
- Notification Service:
python notification_service.py - Inventory Service:
python inventory_service.py - Payment Service:
python payment_service.py - Order Service:
python order_service.py
- Notification Service:
- Place an order by running the
Order Service. The workflow will propagate through the services as events are handled.
Key Considerations
- Event Bus: Use an event broker like RabbitMQ, Kafka, or AWS SNS to manage communication between services.
- Event Versioning: Include versioning to handle changes in event formats over time.
- Idempotency: Ensure services handle repeated events gracefully to avoid duplication.
- Monitoring and Tracing: Use tools like OpenTelemetry to trace and debug distributed workflows.
- Error Handling:
- Dead Letter Queues (DLQs) to capture failed events.
- Retries with backoff for transient errors.
Advantages of the Choreography Pattern
- Loose Coupling: Services interact via events without direct knowledge of each other.
- Resilience: Failures in one service don’t block the entire workflow.
- High Autonomy: Services operate independently and can be deployed or scaled separately.
- Dynamic Workflows: Adding new services to the workflow requires subscribing them to relevant events.
Challenges of the Choreography Pattern
- Complex Debugging: Tracing errors across distributed services can be difficult.
- Event Storms: Poorly designed workflows may generate excessive events, overwhelming the system.
- Coordination Overhead: Decentralized logic can lead to inconsistent behavior if not carefully managed.
Orchestrator vs. Choreography: When to Choose?
- Use Orchestrator Pattern when workflows are complex, require central control, or involve many dependencies.
- Use Choreography Pattern when you need high scalability, loose coupling, or event-driven workflows.
