Choose Event-Driven when...

• •You need loose coupling between services so that teams can develop, deploy, and scale their services independently without coordinating with downstream consumers.
• •Your system involves workflows where multiple downstream services need to react to the same business event (e.g., order placed triggers inventory, shipping, notifications, and analytics).
• •Durability and replay are important: events persisted in a log (Kafka) enable new consumers to reprocess history, supporting event sourcing and CQRS patterns.
• •You need to handle traffic spikes gracefully by buffering events in the broker and processing them at the consumer's own pace, rather than overwhelming downstream services.
• •Temporal decoupling is valuable: downstream services can go offline for maintenance or scaling without losing events, processing them when they come back online.

Choose Request-Response when...

• •The caller needs an immediate response to proceed with its workflow, such as validating a credit card before confirming an order or checking inventory before adding to cart.
• •Your interaction is naturally synchronous (user clicks a button and expects an immediate result) and eventual consistency would degrade user experience.
• •The system is simple and the overhead of event brokers, schema registries, and dead-letter queues is not justified by the decoupling benefits.
• •Debugging and operational simplicity are priorities: request-response produces linear traces that any developer can follow without specialized distributed tracing tools.
• •Strong consistency is required: the caller needs to know that the downstream operation either succeeded or failed before returning a response to the user.

Can I mix event-driven and request-response in the same system?

Yes, and most production systems do. A common pattern is to use request-response for synchronous user-facing interactions (API calls that need immediate responses) and event-driven for asynchronous background processing (sending notifications, updating search indexes, generating reports, syncing data between services). The key is to use each pattern where its strengths match the interaction requirements.

How do I handle errors in event-driven systems?

Event-driven error handling uses dead-letter queues (DLQs) for events that fail processing after multiple retries, idempotent consumers to safely handle duplicate deliveries, and compensation events to undo partial operations in saga patterns. Monitoring consumer lag and DLQ depth is essential for detecting processing failures before they affect users.

What is eventual consistency and when is it acceptable?

Eventual consistency means that after a write, not all parts of the system will immediately reflect the change, but they will converge given time. It is acceptable when users can tolerate brief delays (e.g., a social media post appearing in feeds a few seconds after publishing, or an order confirmation email arriving a minute after checkout). It is not acceptable for financial transactions, inventory checks, or any operation where stale data leads to incorrect outcomes.

What is event sourcing?

Event sourcing is a pattern where you store the full history of state changes as an immutable sequence of events, rather than storing only the current state. The current state is derived by replaying the events. This provides a complete audit trail, enables temporal queries (what was the state at time T?), and supports replaying events to rebuild read models. It adds complexity but is valuable for domains with regulatory audit requirements or complex business logic.

How do I ensure event ordering in distributed systems?

Event ordering is guaranteed within a single partition or queue but not across partitions. To maintain ordering for related events, use a consistent partition key (e.g., user ID, order ID) so all events for the same entity go to the same partition. Across partitions, use logical timestamps (vector clocks or Lamport timestamps) or design consumers to handle out-of-order events through idempotent processing and reconciliation.