Event-Driven Connector

Unlike synchronous REST or gRPC clients, an event-driven connector operates on a publish-subscribe or observer pattern. It does not block execution waiting for a response. Instead, it registers a listener (or consumer) that is invoked only when a specific event occurs in the source system. This decouples the producer and consumer in time, allowing systems to handle bursts of activity and continue operating independently.

• Example: A connector listening to a Kafka topic for order_created events. The e-commerce service publishes the event and continues; the connector consumes it later to trigger inventory updates.

A robust connector abstracts the underlying event source protocol. It can be configured to listen to events from diverse sources, translating them into a common internal event format. This provides flexibility and reduces vendor lock-in.

Common source protocols include:

• Message Brokers: Apache Kafka, RabbitMQ, Amazon SQS
• Database Change Streams: PostgreSQL LISTEN/NOTIFY, MongoDB Change Streams, Debezium for CDC
• Cloud Platform Events: AWS EventBridge, Google Cloud Pub/Sub, Azure Event Grid
• Webhooks: Inbound HTTP POST calls from external SaaS platforms

Connectors implement content-based routing logic to process only relevant events. This is defined declaratively via rules or schemas, not hard-coded logic. Filtering occurs on properties like event type, payload content, or metadata headers.

• Pattern: event.type == 'PaymentSucceeded' && event.payload.amount > 1000
• Routing: The filtered event is then routed to the appropriate downstream handler or action. This ensures efficient processing and prevents unnecessary load on target systems.

A critical characteristic is providing delivery semantics. Most production-grade connectors guarantee at-least-once delivery, meaning an event will be processed at minimum once, but duplicates are possible. This is achieved through mechanisms like acknowledgments and idempotent handlers.

• Mechanism: The connector only commits an offset or sends an acknowledgment back to the source broker after the event has been successfully processed by the downstream action.
• Contrast: At-most-once delivery is faster but can lose events; exactly-once is complex and often requires coordination with the event source and stateful processing.

To resume processing after a failure without data loss, connectors maintain consumer offsets or checkpoints. This is a form of stateful memory that records the last successfully processed event's position in the stream.

• Function: If the connector restarts, it reads the persisted checkpoint and resumes consumption from that exact point in the event log.
• Storage: Checkpoints are typically stored in a durable backend like a database (e.g., Redis, PostgreSQL) or the broker itself (Kafka's __consumer_offsets topic).

Connectors must manage the rate of incoming events to prevent overwhelming downstream systems. Backpressure signaling is the mechanism by which a connector slows down or pauses consumption when the target system is saturated.

• Implementation: This can be pull-based (the connector requests batches when ready) or use configurable limits like maximum concurrent requests or queue depth.
• Purpose: Prevents memory exhaustion, maintains system stability, and ensures graceful degradation under load, aligning with the Bulkhead resilience pattern.

An HTTP endpoint configured to receive and process asynchronous, event-driven notifications (webhooks) from external systems. It is the counterpart to an event-driven connector's publishing function.

• Core Function: Acts as a passive receiver for inbound HTTP POST requests containing event payloads.
• Trigger Mechanism: Upon receiving a valid webhook, it parses the payload and triggers an internal workflow or updates a system state.
• Security: Typically implements verification via secret tokens or signature validation (e.g., HMAC) to ensure the request's authenticity.

A connector or adapter that transfers messages between different message queuing systems or between a queue and an application API. It is a foundational pattern for event-driven integration.

• Protocol Translation: Bridges technologies like Apache Kafka, RabbitMQ, Amazon SQS, or Google Pub/Sub.
• Function: Consumes messages from a source queue/topic, optionally transforms the payload, and publishes it to a destination. This enables legacy systems to participate in modern event streams.
• Use Case: Allows an AI agent's event-driven connector to publish events to a Kafka topic, which a bridge then forwards to an enterprise service bus (ESB).

A critical resilience design pattern for any component making external calls, including event-driven connectors. It prevents cascading failures in distributed systems.

• Mechanism: Monitors for failures (timeouts, errors). When failures exceed a threshold, the circuit opens and fails fast for subsequent calls, bypassing the failing downstream service.
• States: Closed (normal operation), Open (failing fast), Half-Open (probing for recovery).
• Application: An event-driven connector publishing to an unhealthy endpoint should employ a circuit breaker to stop queuing failed requests and allow the downstream system time to recover.

A dedicated proxy instance deployed alongside a service (or connector) in a service mesh architecture. It handles cross-cutting concerns like security, observability, and reliability for all network communication.

• Function: Intercepts all inbound/outbound traffic for its companion service. For an event-driven connector, the sidecar can manage mTLS encryption, retry logic, load balancing, and telemetry for all event publications.
• Decoupling: Offloads complex networking logic from the connector's business logic, simplifying its implementation.
• Example Technologies: Istio, Linkerd, and Consul Connect implement the sidecar pattern.