Event Sourcing + CQRS

Introduction

In the world of microservices, designing a robust and scalable architecture is of utmost importance. One of the most popular patterns for designing microservices is the Command Query Responsibility Segregation (CQRS) pattern, which separates the read and write responsibilities of the system. Another important pattern is Event Sourcing, which models the state of the system as a sequence of events. In this blog post, we will explore how to use CQRS and Event Sourcing together in a financial data analysis platform using various microservices written in Python, and using Confluent Kafka as the event store.

What is CQRS?

CQRS is a pattern that separates the read and write operations of an application into two different models. The write model is responsible for handling commands that modify the application state, while the read model is responsible for querying the application state. This separation of concerns can improve scalability and performance by allowing the read model to be optimized for querying, while the write model can be optimized for handling commands.

What is Event Sourcing?

Event Sourcing is a pattern that captures all changes to an application's state as a sequence of events. Instead of updating the current state, Event Sourcing stores all state changes as events in an event log. The current state can then be reconstructed by replaying these events in sequence. This approach can provide benefits such as auditability, scalability, and the ability to rebuild an application's state to any point in time.

Combining CQRS and Event Sourcing

When combined, CQRS and Event Sourcing can provide a powerful solution for microservice-based architectures. The write model can publish events to an event log, which can be consumed by the read model to update its queryable state. This approach can provide scalability and eventual consistency by allowing the read model to be updated asynchronously from the write model.

System Architecture

The following is an ASCII illustration of the system architecture for our financial data analysis platform:

+--------------+        +---------------+        +---------------+
|              |        |               |        |               |
|  Write Model |        |  Event Store  |        |  Read Model   |
|              |        |               |        |               |
+--------------+        +---------------+        +---------------+
        |                       |                          |
        |  Publishes events to   |                          |
        +---------------------->|                          |
                                 |  Consumes events from    |
                                 +------------------------>|
 

The system consists of three components: the Write Model, the Event Store, and the Read Model. The Write Model is responsible for handling commands that modify the application state. It publishes events to the Event Store, which captures all changes to the application state as events. The Read Model consumes events from the Event Store and updates its queryable state.

To develop reliable, scalable, and eventually-consistent microservices, we need to follow some best practices:

1. Use a messaging system: Use a messaging system to decouple microservices and improve scalability and reliability. Avro serialization is a good choice because it provides a compact binary format for the messages.

2. Use idempotent operations: Since events can be replayed, it's important to ensure that write operations are idempotent. This means that executing the operation multiple times should have the same effect as executing it once.

3. Handle failure gracefully: Microservices should be designed to handle failure gracefully. This means that they should be able to recover from failures and resume processing without losing any data.

4. Use eventual consistency: Microservices should be designed to eventually become consistent. This means that it's okay for the read model to be slightly behind the write model as long as it eventually catches up.

Code Examples

Write Model Microservice

from confluent_kafka import avro
from confluent_kafka.avro import AvroProducer
 
# Define schema for event
event_schema = {
    "type": "record",
    "name": "Event",
    "fields": [
        {"name": "id", "type": "int"},
        {"name": "amount", "type": "double"},
        {"name": "timestamp", "type": "long"}
    ]
}
 
# Configure Avro producer
producer = AvroProducer({
    'bootstrap.servers': 'localhost:9092',
    'schema.registry.url': 'http://localhost:8081'
}, default_value_schema=avro.loads(json.dumps(event_schema)))
 
# Handle command to update balance
def handle_update_balance_command(account_id, amount):
    # Process command and update balance
    balance = get_account_balance(account_id)
    new_balance = balance + amount
    update_account_balance(account_id, new_balance)
 
    # Publish event to event store
    event = {"id": account_id, "amount": amount, "timestamp": int(time.time() * 1000)}
    producer.produce(topic='account_events', value=event)
 
# Other write model methods...

Read Model Microservice

from confluent_kafka import avro
from confluent_kafka.avro import AvroConsumer, CachedSchemaRegistryClient
 
# Define schema for event
event_schema = {
    "type": "record",
    "name": "Event",
    "fields": [
        {"name": "id", "type": "int"},
        {"name": "amount", "type": "double"},
        {"name": "timestamp", "type": "long"}
    ]
}
 
# Configure Avro consumer
consumer = AvroConsumer({
    'bootstrap.servers': 'localhost:9092',
    'group.id': 'read_model'
},
schema_registry=CachedSchemaRegistryClient({"url": "http://localhost:8081"}),
reader_value_schema=avro.loads(json.dumps(event_schema)))
 
# Handle event and update account balance in read model
def handle_event(event):
    # Update account balance in read model
    balance = get_account_balance(event["id"])
    new_balance = balance + event["amount"]
    update_account_balance(event["id"], new_balance)
 
# Consume events from event store and handle them
consumer.subscribe(['account_events'])
while True:
    msg = consumer.poll(1.0)
    if msg is None:
        continue
    if msg.error():
        print("Consumer error: {}".format(msg.error()))
        continue
    event = msg.value()
    handle_event(event)
 
# Other read model methods...

Conclusion

CQRS and Event Sourcing are powerful patterns that can provide significant benefits to microservice-based architectures. By separating read and write operations and capturing all state changes as events, we can achieve scalability, auditability, and eventual consistency. In our financial data analysis platform, we use Avro serialization to write messages containing events to an event log, which is consumed by the read model to update its queryable state.