In the realm of modern software development, managing and maintaining data integrity is paramount. Traditional approaches often involve updating the state of an application directly within a database. However, as systems grow in complexity, ensuring data consistency and traceability becomes more challenging. This is where Event Sourcing, coupled with a powerful distributed streaming platform like Memphis.dev, emerges as a robust solution and a great data structure to work with.
At its core, Event Sourcing is a design pattern that captures every change or event that occurs in a system as an immutable and sequentially ordered log of events. Instead of persisting the current state of an entity, Event Sourcing stores a sequence of state-changing events. These events serve as a single source of truth for the system’s state at any given point in time.
Imagine a banking application that tracks an account’s balance. Instead of only storing the current balance in a database, Event Sourcing captures all the events that alter the balance. Deposits, withdrawals, or any adjustments are recorded as individual events in chronological order.
Let’s break down how this might work:
FundsDeposited
, with relevant metadata (timestamp, amount, account number) is created.Memphis.dev, an open-source distributed event streaming platform, is perfect for implementing Event Sourcing due to its features:
Events occur and are pushed by their order of creation into some Memphis Station (=topic)
Event Log:
class EventLog:
def __init__(self):
self.events = []
def append_event(self, event):
self.events.append(event)
def get_events(self):
return self.events
Memphis Producer:
from __future__ import annotations
import asyncio
from memphis import Memphis, Headers, MemphisError, MemphisConnectError, MemphisHeaderError, MemphisSchemaError
import json
class MemphisEventProducer:
def __init__(self,host="my.memphis.dev"):
try:
self.memphis = Memphis()
await self.memphis.connect(host=host, username="<application type username>", password="<password>")
def send_event(self, topic, event):
await self.memphis.produce(station_name=topic, producer_name='prod_py',
message=event,nonblocking=False)
except (MemphisError, MemphisConnectError, MemphisHeaderError, MemphisSchemaError) as e:
print(e)
finally:
await self.memphis.close()
Usage:
# Initialize Event Log
event_log = EventLog()
# Initialize Memphis Producer
producer = MemphisEventProducer()
# Append events to the event log and produce them to Memphis
events_to_publish = [
{"type": "Deposit", "amount": 100},
{"type": "Withdrawal", "amount": 50},
# Add more events as needed
]
for event in events_to_publish:
event_log.append_event(event)
producer.send_event('account-events', event)
When implementing Event Sourcing with a message broker, several key features are crucial for a streamlined and efficient system:
1. Persistent Message Storage:
2. Ordered and Immutable Event Logs:
3. Scalability and Performance:
4. Fault Tolerance and High Availability:
5. Consumer Flexibility and State Rebuilding:
6. Retention Policies and Archiving:
7. Monitoring and Management:
8. Security and Compliance:
9. Seamless Integration and Ecosystem Support:
Choosing a message broker that aligns with these critical features is essential for implementing robust Event Sourcing, ensuring data integrity, scalability, and resilience within your application architecture.
Use Case Complexity: For simpler applications or where scalability isn’t a primary concern, databases might suffice. For higher reliability, distributed systems needing high scalability, and real-time processing, a message broker can be more suitable.
Replay: In event streaming platforms or message brokers, events are stored in a FIFO manner, one after the other as they first appear. That nature also makes it easier for the consumer on the other side to understand the natural flow of events and replay the entire “scene,” whereas in databases, it is not the case, and additional fields must be added, like timestamps, to organize the data based on time. It also requires additional logic to understand the latest state of an entity.
Continue your learning: read how and why event sourcing outgrows the database.