Why Use an Event Store?
Understanding why purpose-built event sourcing databases outperform conventional databases for event-sourced systems.
The Case for Specialized Event Stores
When implementing event sourcing, one of the first architectural decisions you'll face is where to store your events. While it's technically possible to use a conventional relational database like MySQL or PostgreSQL, purpose-built event stores offer significant advantages that can make or break your event-sourced system at scale.
Event stores are databases designed from the ground up for the specific access patterns and guarantees that event sourcing requires. They understand that events are immutable, append-only, and need to be read in sequence characteristics that don't align well with how traditional databases are optimized.
Optimized Append Operations
Event stores are built for append-only writes, the primary operation in event sourcing. They skip the overhead of indexes, foreign keys, and update mechanisms that slow down conventional databases.
Stream-Based Queries
Native support for reading events in order by stream, by time, or by category. No complex queries or joins needed. Just efficient sequential reads.
Built-in Concurrency Control
Optimistic concurrency with expected version checking is a first-class feature, not something you have to build on top of row-level locking.
Where MySQL and PostgreSQL Struggle
Performance at Scale
Relational databases are optimized for random reads and updates, not the sequential append and read patterns that event sourcing requires. As your event count grows into millions or billions, you'll encounter serious performance degradation.
- •Index bloat: B-tree indexes become increasingly expensive to maintain for append-heavy workloads
- •Table fragmentation: Frequent inserts lead to page splits and scattered data on disk
- •Lock contention: Row-level locking creates bottlenecks when multiple writers target the same stream
Missing Event Sourcing Primitives
Conventional databases lack native support for event sourcing concepts, forcing you to build these features yourself, often incorrectly or inefficiently.
- •No stream abstraction: You must query by aggregate ID with ORDER BY, which is less efficient than native stream support
- •DIY subscriptions: Polling for new events is wasteful; you need to build your own change notification system
- •Global ordering challenges: Getting a globally ordered event log across all streams requires careful schema design
Operational Complexity
Running event sourcing on a relational database introduces operational challenges that purpose-built stores handle automatically.
- •Partitioning complexity: Sharding event tables while maintaining ordering guarantees is difficult
- •Backup and archival: Standard backup strategies don't account for the immutable nature of events
- •Projection rebuilds: Replaying millions of events through application code is slow without database-level support
Feature Comparison
Conventional Databases
- •Optimized for CRUD operations and random access
- •No native event stream concept
- •Polling required for event subscriptions
- •Manual implementation of optimistic concurrency
- •Complex projection rebuild process
- •Performance degrades with large event volumes
Purpose-Built Event Stores
- •Optimized for append-only, sequential workloads
- •First-class stream and category abstractions
- •Push-based subscriptions with catch-up support
- •Built-in optimistic concurrency control
- •Native projection and subscription support
- •Designed to handle billions of events efficiently
What Event Stores Provide
- Append-Only Storage:Immutable event logs with efficient sequential writes and no update overhead
- Stream Management:Native support for organizing events by aggregate, category, or global position
- Real-Time Subscriptions:Push-based notifications when new events are written, with catch-up from any position
- Optimistic Concurrency:Built-in version checking to prevent conflicting writes to the same stream
- Projections:Server-side event processing to build read models and materialized views
- Temporal Queries:Efficient queries for events by time range, position, or custom criteria
When Conventional DBs Can Work
- Small event volumes (thousands, not millions)
- Proof-of-concept or early-stage projects
- Teams with strong existing database expertise
- Infrastructure constraints that mandate SQL databases
- Using libraries like Marten that optimize PostgreSQL for events
When to Use an Event Store
- Production systems with significant event volumes
- Applications requiring real-time event subscriptions
- Systems with complex projection requirements
- High-throughput event-driven architectures
- When you want to focus on domain logic, not infrastructure
Ready to Choose an Event Store?
Explore purpose-built event stores that can power your event-sourced applications at any scale. Each offers unique strengths for different requirements and environments.