Design a Webhook System: (Step-by-Step Guide)

When you’re preparing for a System Design interview, one of the most common questions you’ll face is how to design a webhook system. It sounds straightforward at first, just send data when an event occurs, but designing a reliable, scalable, and fault-tolerant webhook delivery system reveals a lot about how you think about distributed systems, retries, and reliability under load.

In this guide, you’ll learn how to design a webhook system from the ground up, focusing on real-world considerations like event delivery, retries, storage, scalability, and monitoring. We’ll also connect it to broader concepts you’ve seen in other designs, where real-time responsiveness and fault-tolerant architecture also play key roles.

Understanding what a webhook system does

Before diving into architecture, it’s essential to clarify what a webhook system actually does. Understanding this can help you ace System Design interview questions.

A webhook system is a mechanism that allows one system (the provider) to notify another system (the consumer) in real time whenever an event happens. Instead of polling for updates, clients register a callback URL, and the webhook system sends an HTTP request to that URL whenever the event occurs.

For example:

• GitHub notifies your CI/CD service when code is pushed.
• Stripe notifies your backend when a payment succeeds.
• Slack notifies your bot when a message is sent to a channel.

Your goal in the interview is to show how you can design a webhook system that’s reliable, scalable, and guarantees delivery even under failure scenarios.

The problem statement

Here’s what you might be asked:

“Design a webhook system that allows third-party clients to register their endpoints and receive notifications when specific events occur.”

You should confirm a few functional and non-functional requirements before jumping into architecture.

Functional requirements

• Clients can register and manage webhook endpoints.
• The system sends event notifications to these endpoints via HTTP POST.
• Webhook payloads include event details and metadata.
• Support retries in case of failed deliveries.

Non-functional requirements

• High reliability — no lost events.
• Low latency — send notifications as soon as events occur.
• Scalable to millions of subscribers.
• Secure — authenticate webhook requests.
• Fault-tolerant and easy to monitor.

High-level architecture

Let’s look at the high-level flow before diving into components:

Event Source → Event Queue → Webhook Dispatcher → Delivery Workers → Client Endpoints

At a conceptual level, this flow mirrors systems where each keystroke triggers asynchronous processing and cached lookups to maintain responsiveness. Here, each event triggers asynchronous dispatch and retries for guaranteed delivery.

Step-by-step data flow

Let’s walk through what happens when you design a webhook system from the ground up:

1. Event Occurs: The source system generates an event (e.g., user signup, transaction complete).
2. Event Published: The event is pushed to an event bus or message queue.
3. Dispatcher Reads Event: The webhook dispatcher service fetches the event and looks up all subscribers who have registered interest.
4. Enqueue Deliveries: For each subscriber, a message is added to a delivery queue.
5. Delivery Workers Send Requests: Workers pull from the queue and POST the payload to each registered endpoint.
6. Handle Responses:
   • If 2xx: mark as success.
   • If failure or timeout: retry with exponential backoff.
7. Monitor and Log: Every attempt and response is logged for debugging and analytics.

This asynchronous approach ensures reliability and scalability—similar to the event-driven architecture patterns you might’ve seen in systems like typeahead System Design, where responsiveness depends on decoupling producers and consumers.

Core components of a webhook system

1. Event producer

The service or system that generates events. For example, an e-commerce backend may produce “order placed” or “payment processed” events.

2. Event bus or queue

Stores and delivers events in a reliable, asynchronous manner. Kafka, RabbitMQ, or AWS SNS are common choices.

3. Subscription service

Maintains mappings of event types to registered webhook endpoints.

4. Dispatcher

Reads events from the queue, finds all interested subscribers, and enqueues delivery tasks.

5. Delivery worker

Responsible for sending HTTP POST requests to each endpoint. Handles retries, exponential backoff, and dead-letter queues for failed attempts.

6. Monitoring and analytics

Logs event success/failure rates, latency, and endpoint health.

Detailed architecture diagram (conceptual)

Here’s how the system fits together:

    ┌──────────────────┐

     │ Event Producer    │

     └───────┬───────────┘

              │

              ▼

     ┌──────────────────┐

      │ Event Queue (Kafka│

      │   or RabbitMQ)    │

     └───────┬───────────┘

              │

              ▼

     ┌──────────────────┐

      │ Webhook Dispatcher│

     └───────┬───────────┘

              │

       For each subscriber

              │

              ▼

     ┌──────────────────┐

      │ Delivery Workers  │

     └───────┬───────────┘

              │

             ▼

     ┌──────────────────┐

      │ Client Endpoints  │

     └──────────────────┘

Each component can scale independently, ensuring the system can handle large spikes in events.

API design

Your webhook system needs APIs for:

Registering a webhook endpoint

POST /api/webhooks/register

{

  “event_type”: “payment.success”,

  “callback_url”: “https://client.com/webhooks”

}

Deregistering a webhook

DELETE /api/webhooks/{id}

Listing subscriptions

GET /api/webhooks

For quick lookups, the subscription data can be stored in a relational database (like PostgreSQL) or a NoSQL database (like DynamoDB).

Event queuing and dispatching

When an event occurs, the system pushes it to an event queue. This decouples the producer from consumers and ensures resilience if the delivery service slows down or fails temporarily.

For example:

Topic: order.created

Message: { “order_id”: 123, “user_id”: 42 }

The dispatcher reads this message and checks the subscription table for all endpoints registered for “order.created.” For each subscriber, it creates a delivery job that will later be processed by workers.

Delivery mechanism

Each delivery job involves sending an HTTP POST request to a subscriber’s callback URL.

Delivery job format

{

  “event_id”: “abcd-1234”,

  “callback_url”: “https://client.com/webhook”,

  “payload”: { “order_id”: 123, “status”: “created” },

  “attempts”: 0

}

The worker processes these jobs in parallel. If an endpoint responds with a success code (2xx), the event is marked as delivered. Otherwise, the system retries.

Retry mechanism

• Use exponential backoff (e.g., 1s, 2s, 4s, 8s, etc.)
• Limit retries (e.g., 5 attempts).
• Move failed deliveries to a dead-letter queue for manual inspection.

Retries should be idempotent–the same event can safely be delivered multiple times.

Ensuring reliability and idempotency

Reliability is a cornerstone of webhook System Design.

Use unique event IDs

Include a unique event ID in every webhook payload. This helps receivers identify and ignore duplicates.

Store delivery status

Keep track of delivery attempts, timestamps, and response codes in persistent storage.

Deduplication

If your system retries or replays events, ensure duplicate deliveries are filtered using event IDs.

This approach is similar to how some other Systems use caching and deduplication to ensure users don’t see duplicate or stale suggestions.

Scalability considerations

When you design a webhook system for scale, several challenges appear:

1. Burst traffic

A single large event (like a flash sale or global product launch) could trigger millions of webhook deliveries.

Solution:
Use message queues to buffer and distribute load. Autoscale delivery workers based on queue depth.

2. Hot endpoints

Some clients may receive disproportionately high traffic.

Solution:
Implement rate limiting per endpoint and backpressure handling to prevent system overload.

3. Global distribution

To reduce latency, deploy webhook dispatchers and delivery workers in multiple regions, using CDNs or global load balancers.

Caching strategies

Caching can help reduce redundant lookups and accelerate system performance.

Common caches in webhook systems:

1. Subscription Cache: Store recent endpoint lookups (e.g., Redis).
2. Delivery Result Cache: Temporarily store recent delivery results to prevent duplicate attempts.

Cache keys should be simple and normalized:

key = “subscription:event_type:payment.success”

This idea echoes what you might’ve seen in other System Designs, where prefix caching ensures sub-100ms responses. Here, it ensures sub-second event dispatching.

Security considerations

When dealing with webhooks, you must protect both your system and the client.

Best practices:

• Signature verification: Include an HMAC signature header for every payload.
• HTTPS-only: Enforce secure transport.
• Replay protection: Include timestamps and expire old messages.
• Authentication: Validate the sender before processing events.

Failure handling and monitoring

No webhook system is complete without proper monitoring and failure management.

Metrics to track:

• Delivery success rate
• Average delivery latency
• Retry counts
• Queue backlog size
• Endpoint failure ratios

Use tools like Prometheus + Grafana for metrics visualization, and ELK Stack for log analysis.

Set up alerts when failure rates exceed a threshold so you can investigate failing endpoints.

Example scenario: designing a webhook system for a payment platform

Let’s apply this step-by-step.

Imagine you’re designing a webhook system for a payment processor like Stripe:

1. A payment.success event is emitted.
2. The event bus receives and stores it.
3. The dispatcher finds all clients subscribed to “payment.success.”
4. For each subscriber, the system creates a delivery job and pushes it to the queue.
5. Delivery workers process jobs in parallel and POST the event to client endpoints.
6. Failures are retried with exponential backoff.
7. Monitoring dashboards track success rates and latencies.

This scenario demonstrates scalability and fault tolerance—exactly what interviewers want to hear when you design a webhook system.

Extending the design

Once you’ve covered the basics, discuss possible extensions in your interview:

• Event filtering: Clients can subscribe to specific subsets of events.
• Replay mechanism: Allow clients to re-fetch missed events.
• Multi-tenancy: Isolate data and traffic between customers.
• Audit trails: Record complete event histories for compliance.

These advanced features show that you can think beyond the MVP and plan for production-level challenges.

Testing your webhook system

Your design isn’t complete without discussing testing.

Key testing approaches:

• Unit tests for event processing logic.
• Integration tests for queue-to-delivery flow.
• Load tests for burst traffic handling.
• Chaos tests to simulate endpoint failures or network latency.

Testing demonstrates that your system is production-ready, not just theoretically sound.

Monitoring and alerting system

You’ll want automated alerts when webhook deliveries consistently fail or queue depth spikes.

Example alerts:

• “Queue length > 10000 for 5 minutes.”
• “Average delivery latency > 5 seconds.”
• “Failure rate > 10% in past 10 minutes.”

You can also integrate dashboards to visualize metrics and identify slow endpoints.

Learning resource recommendation

If you want to deepen your understanding and practice designing systems like this for interviews, you can explore Grokking the System Design Interview. This course breaks down complex problems, including webhook systems, notification pipelines, and more.

You can also choose the best System Design study material based on your experience:

• Best System Design Certification
• Best System Design Courses
• Best System Design Platforms

Key takeaways

When you design a webhook system in an interview, keep these principles in mind:

• Use event-driven architecture to decouple producers and consumers.
• Make deliveries reliable and idempotent with retries and unique event IDs.
• Leverage caching and queues for scalability.
• Implement robust monitoring and alerting.

Webhook systems may seem simple, but they test your understanding of reliability, asynchronous processing, and scale–the same fundamentals behind every great System Design.