☁️ SaaS & Cloud · C4 Architecture · Pro
Event-Driven Architecture with Kafka
Services that talk through events instead of direct calls: an order service publishes events that inventory, payments, notifications and analytics react to.
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Drawing diagram…
What this diagram shows
- Services publish facts (OrderPlaced) and don't wait for each other
- Each consumer group processes events at its own pace
- Schema registry keeps event formats compatible
Prompt used
Event-driven architecture: the order service writes an order and publishes OrderPlaced to Kafka using the outbox pattern. Inventory, payment, notification and analytics services consume the topic independently; payment publishes PaymentCompleted or PaymentFailed which the order service consumes to update status. A schema registry manages Avro event schemas, and a dead-letter topic holds events that fail processing.
Mermaid code
C4Container
title Event-Driven Order Processing
Person(customer, "Customer", "Places orders")
System_Boundary(eda, "Event-Driven Platform") {
Container(order, "Order Service", "Java", "Writes order and outbox")
ContainerQueue(kafka, "Kafka", "Topics: orders, payments", "Event backbone")
Container(schema, "Schema Registry", "Avro", "Event contracts")
Container(inv, "Inventory Service", "Go", "Reserves stock")
Container(pay, "Payment Service", "Java", "Charges customer")
Container(notify, "Notification Service", "Node.js", "Email and SMS")
Container(analytics, "Analytics Sink", "Kafka Connect", "Streams to warehouse")
ContainerQueue(dlq, "Dead-letter Topic", "Kafka", "Failed events")
}
Rel(customer, order, "Places order")
Rel(order, kafka, "OrderPlaced")
Rel(kafka, inv, "Consumes")
Rel(kafka, pay, "Consumes")
Rel(kafka, notify, "Consumes")
Rel(kafka, analytics, "Consumes")
Rel(pay, kafka, "PaymentCompleted")
Rel(kafka, order, "Updates status")
Rel(order, schema, "Validates schema")
Rel(notify, dlq, "On failure")Related templates
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