What is the best way to design a database for event-driven architecture?

1 Database challenges for EDA

However, designing a database for EDA poses some challenges. Traditional relational databases are not well suited for handling high volumes of events, complex queries, and schema changes. They also introduce dependencies and bottlenecks between the event producers and consumers, which can affect the reliability and availability of the system. Therefore, database engineers need to consider alternative approaches that can overcome these limitations and support the event-driven paradigm.

2 Event sourcing

One approach is event sourcing, which is a technique that stores the state of the system as a sequence of events, rather than as a snapshot of the current state. This way, the database acts as an append-only log of events, which can be replayed, queried, and projected into different views. Event sourcing enables event-driven applications to achieve consistency, auditability, and resilience, as well as to support temporal queries and complex business logic.

3 Stream processing

Another approach is stream processing, which is a technique that processes events as they arrive, without storing them in a database. Stream processing can perform various operations on events, such as filtering, aggregation, transformation, and enrichment. Stream processing enables event-driven applications to handle real-time analytics, monitoring, and alerting, as well as to integrate with external systems and services.

4 Hybrid solutions

Depending on the requirements and trade-offs of the system, database engineers may also opt for hybrid solutions that combine event sourcing and stream processing with other database technologies, such as NoSQL, graph, or in-memory databases. These solutions can leverage the strengths of each technology and provide more flexibility and scalability for event-driven applications. For example, a hybrid solution may use event sourcing to store the core business events, stream processing to perform real-time operations on the events, and NoSQL to store the derived state or views of the system.

5 Database design principles for EDA

No matter the approach taken, database engineers should abide by a few general principles when constructing a database for EDA. These include designing events with clear semantics, structure, and metadata; using unique identifiers and timestamps for events to guarantee ordering and traceability; utilizing schema evolution techniques to manage changes in event formats and versions; implementing security and encryption mechanisms to safeguard the confidentiality and integrity of events; and testing and monitoring the database performance, availability, and reliability under varying loads and scenarios.

6 Database tools for EDA

Database engineers should be knowledgeable in the tools and frameworks that can assist in implementing and managing a database for EDA. These tools include Apache Kafka, a distributed streaming platform for high-throughput event ingestion, processing, and delivery; MongoDB, a document-based NoSQL database that stores and queries events as JSON documents; EventStore, a specialized database that supports event sourcing and provides APIs and projections for event streams; Apache Spark, a distributed computing framework for stream processing and batch processing on large-scale event data; and Apache Flink, a stream processing framework for complex event processing and stateful computations on event streams.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?