kafka-event-driven

Event-driven microservices with Kafka and Dapr Pub/Sub are coordinated by defining versioned event schemas, configuring producers and consumers, and applying partitioning by user_id. This enables decoupled services with reliable delivery and scalable parallelism.

To preserve event ordering for individual users in Kafka, you should apply a partitioning strategy by user_id. This ensures all events for a specific user route to the same partition, maintaining sequence while enabling scalable parallelism across the topic.

Failed Kafka messages are handled by configuring Dead Letter Queues (DLQ) and retry policies within your Dapr Pub/Sub setup. This safeguards resilient operations by capturing unprocessable events and applying idempotent processing to prevent duplicates during retries.

Yes, evolving data contracts are supported safely through versioned event schemas and a registry. This allows you to manage changes to task-events, reminders, and task-updates topics without breaking existing producers or consumers.

Idempotent processing across multiple Kafka topics is achieved by implementing specific safeguards in your consumers. This ensures that duplicate events from task-events, reminders, or task-updates do not cause unintended side effects or duplicate state changes.