System Design: Event Sourcing

Instead of storing just the current state of the data in a domain, use an append-only store to record the full series of actions taken on that data. The store acts as the system of record and can be used to materialize the domain objects.

This can simplify tasks in complex domains, by avoiding the need to synchronize the data model and the business domain, while improving performance, scalability, and responsiveness. It can also provide consistency for transactional data, and maintain full audit trails and history that can enable compensating actions.

Event sourcing vs Event-Driven Architecture (EDA)

Event sourcing is seemingly constantly being confused with Event-driven Architecture (EDA). Event-driven architecture is about using events to communicate between service boundaries. Generally, leveraging a message broker to publish and consume events asynchronously within other boundaries.

Whereas, event sourcing is about using events as a state, which is a different approach to storing data. Rather than storing the current state, we're instead going to be storing events. Also, event sourcing is one of the several patterns to implement an event-driven architecture.

Advantages

Let's discuss some advantages of using event sourcing:

• Excellent for real-time data reporting.
• Great for fail-safety, data can be reconstituted from the event store.
• Extremely flexible, any type of message can be stored.
• Preferred way of achieving audit logs functionality for high compliance systems.

Disadvantages

Following are the disadvantages of event sourcing:

• Requires an extremely efficient network infrastructure.
• Requires a reliable way to control message formats, such as a schema registry.
• Different events will contain different payloads.

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This article is part of my open source System Design Course available on Github.

karanpratapsingh / system-design

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System Design

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Table of contents

• Getting Started

  • What is system design?

• Chapter I

  • IP
  • OSI Model
  • TCP and UDP
  • Domain Name System (DNS)
  • Load Balancing
  • Clustering
  • Caching
  • Content Delivery Network (CDN)
  • Proxy
  • Availability
  • Scalability
  • Storage

• Chapter II

  • Databases and DBMS
  • SQL databases
  • NoSQL databases
  • SQL vs NoSQL databases
  • Database Replication
  • Indexes
  • Normalization and Denormalization
  • ACID and BASE consistency models
  • CAP theorem
  • PACELC Theorem
  • Transactions
  • Distributed Transactions
  • Sharding
  • Consistent Hashing
  • Database Federation

• Chapter III

  • N-tier architecture
  • Message Brokers
  • Message Queues
  • Publish-Subscribe
  • Enterprise Service Bus (ESB)
  • Monoliths and Microservices
  • Event-Driven Architecture (EDA)
  • Event Sourcing
  • Command and Query Responsibility Segregation (CQRS)
  • API Gateway
  • REST, GraphQL, gRPC
  • Long polling, WebSockets, Server-Sent Events (SSE)

• Chapter IV

  • …

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