Event Sourcing

SUMMARY

Event sourcing is an architectural pattern where state changes are captured as an immutable sequence of events, rather than just storing the current state. Each event represents a fact that happened at a specific point in time, providing a complete audit trail and enabling system reconstruction to any historical point.

How event sourcing works

Event sourcing fundamentally changes how systems handle data by storing every change as a discrete event. Instead of updating records in place, new events are appended to an event log, creating an immutable history of all changes.

This approach provides several key benefits:

Complete audit history
System state reconstruction
Temporal queries
Event replay capabilities
Natural fit for time-series data

Next generation time-series database

QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.

Try live demo Read documentation

Event store implementation

The event store is the central component that manages the sequence of events. It typically provides:

Append-only storage for events
Strict ordering guarantees
Event replay functionality
Snapshot management

The implementation often leverages append-only storage for optimal write performance and data integrity.

Next generation time-series database

Try live demo Read documentation

Applications in time-series systems

Event sourcing is particularly valuable in time-series contexts:

Market data systems

Capturing price changes
Order book updates
Trading activity

Industrial systems

Sensor readings
Equipment state changes
Process control events

For example, in financial markets, event sourcing enables:

Trade reconstruction
Regulatory compliance
Performance analysis
Market replay

The pattern naturally supports real-time analytics and complex event processing while maintaining a complete historical record.

Event sourcing vs traditional CRUD

Consider this comparison of approaches:

Event sourcing provides advantages for:

Audit requirements
Compliance needs
System debugging
Historical analysis
State reconstruction

However, it requires careful consideration of:

Storage requirements
Query complexity
Event schema evolution
Snapshot strategies

The pattern works particularly well with time-series databases designed to handle high-volume append operations and temporal queries efficiently.

Best practices

Design events as facts
Ensure events are immutable
Use event versioning
Implement snapshots for performance
Consider event schema evolution
Plan for event replay scenarios
Monitor event store performance

These practices help create robust event-sourced systems that maintain data integrity while providing powerful analytical capabilities.