Event Store vs Kafka: What are the differences?

Introduction

This markdown code provides a comparison between Event Store and Kafka, highlighting the key differences between the two technologies.

1. Scalability: Event Store is designed to handle large-scale event streams with high throughput and low latency. It provides a distributed architecture that allows for seamless scaling as the number of events and event consumers grow. Kafka, on the other hand, excels at handling massive volumes of data and can scale horizontally to handle billions of events per day.

2. Data Persistence: Event Store stores events as a stream of immutable events, providing a long-term log of all changes to the data. It ensures data durability and allows for rewind and replay capabilities. Kafka, on the other hand, does not persist data indefinitely. It retains data for a configurable period of time or based on the available storage capacity.

3. Ordering Guarantees: Event Store maintains strict ordering guarantees within a single stream, ensuring that events are processed in the order they were written. It provides strong consistency and enables event-driven architectures that rely on ordered processing. Kafka, on the other hand, provides ordered message delivery only within a partition. If events need to be strictly ordered across partitions, additional processing needs to be implemented.

4. Event Replay: Event Store allows for easy event replay, as it retains a log of all events. It is possible to read events from a specific point in time or replay events to rebuild the current state. Kafka also supports event replay but requires additional manual handling, such as marking offsets and managing consumer groups.

5. Event Sourcing: Event Store is optimized for event sourcing, which is the practice of reconstructing the current state of an application by replaying past events. It provides features like snapshots and projections to efficiently rebuild the current state from the event log. Kafka, although it can be used as a component in an event sourcing system, does not have built-in support for event sourcing and requires additional tooling or custom implementation for this purpose.

6. Storage Model: Event Store uses a log-structured storage model where events are continuously written to an append-only log. This provides efficient write performance and enables quick appends to the event log. Kafka also follows a similar log-structured storage model but adds additional indexing and segmenting mechanisms to optimize read and write performance.

In summary, Event Store and Kafka differ in terms of scalability, data persistence, ordering guarantees, event replay capabilities, support for event sourcing, and their storage models. These differences make each technology more suitable for different use cases and architectural requirements.