Event sourcing is transforming how we design cloud applications by treating events as the ultimate source of truth. In this architecture, an event stream forms the backbone of your system, while the database becomes a consumer, storing a snapshot of state at a specific point in time.
🧬 The origins of event sourcing
Event sourcing builds on a long-standing principle: the database log. Behind every database is a log—a record of atomic changes, or “events,” that shape its state. When you query a database, you’re interacting with a state derived from this log.
Event sourcing turns the database inside out, treating the log as the authoritative source and the database as a derived snapshot. This shift unlocks powerful capabilities while avoiding the bottlenecks of traditional database architectures.
⚡ Real-time responsiveness
With event sourcing, your logic responds to events as they happen, eliminating the need to query a database for the latest state. This approach avoids contention from expensive database queries and enables real-time decision-making.
For example, an "Order Placed" event can immediately trigger inventory updates and shipping notifications, ensuring seamless operations and a superior user experience.
💾 Replayable event streams and state as a map/reduce pattern
An event stream is a complete historical record, invaluable for debugging, analytics, and disaster recovery.
By applying map/reduce patterns to the event stream, the entire state—or specific sub-states—can be continuously calculated or retrospectively reconstructed. This reduces the database’s role to storing these computed sub-states across the system, making it lightweight and optimised for read performance.
AWS Kinesis and Apache Kafka (offered as an AWS service) are the leading technologies for storing and managing event streams, serving as the backbone of event sourcing architectures.
🔄 Scaling challenges and solutions
- Storage costs: Retaining a long event history can become costly as the stream grows. Solution: Use retention policies to archive older events into cost-effective storage like Amazon S3 or Glacier. Compress or summarise events into periodic snapshots to minimise storage costs.
- Stream partitioning: Poorly distributed events can cause bottlenecks in large-scale streams. Solution: Use partitioning features in Kinesis or Kafka to shard streams intelligently, ensuring even load distribution. Use partitioning keys (e.g., user IDs or order IDs) to group related events, reducing contention and improving processing efficiency.
- Integration with backups: Rebuilding a database state from the entire event stream every time is impractical. Solution: Periodically persist snapshots of the database state and combine them with recent events from the stream to restore systems quickly. Tools like AWS Glue or custom Lambda functions can automate this workflow.
🛠 The role of AWS Lambda
AWS Lambda is a natural partner for the event sourcing pattern, particularly with streaming services like Amazon Kinesis and Apache Kafka.
- ⏱ Real-time processing: Lambda processes events from streams as they arrive, enabling instant responses to system changes.
- 📈 Scalable consumption: Lambda automatically scales to handle high event volumes, ensuring seamless operation during traffic spikes.
- 🧩 Partition-aware processing: Lambda can target specific partitions in a stream, enabling efficient parallel processing and tailored event handling.
💡 The benefits of event sourcing
- 🕰 Historical traceability: Every state transition is recorded, enabling precise auditing and analytics.
- 🌀 System flexibility: Decoupling state from logic allows systems to evolve independently.
- 🛡 Resilience: Snapshots and replayable streams ensure robust disaster recovery options.
- 🚀 Performance optimisation: Real-time event handling eliminates costly database queries, reducing contention and improving latency.
Blackstack helps teams leverage event sourcing to build scalable, resilient cloud systems on AWS. If you’re ready to unlock the potential of this architecture, let’s chat! Contact us below👇
