Request-Response Pattern (Truly Asynchronous)

About

The Request-Response Pattern (Truly Asynchronous) is a communication style where the client sends a request to a server or service without waiting for an immediate, synchronous reply. Instead, the server processes the request in the background and sends the response later via a separate channel or callback mechanism.

This approach is different from non-blocking I/O in synchronous communication:

  • In non-blocking synchronous calls, the same connection eventually returns the result - but still within the same request lifecycle.

  • In truly asynchronous communication, the response might arrive minutes, hours, or even days later, and not necessarily over the same connection.

Common Use Cases

  • Long-running workflows (e.g., large data processing, video transcoding).

  • Business processes requiring human approval steps.

  • Backend services that must integrate with event-driven architectures.

Characteristics

  • Temporal Decoupling

    • The request and response do not need to happen at the same time.

    • The client can move on to other tasks while the server processes the request.

  • Different Communication Channels

    • The request is sent through one channel, and the response may arrive through another (e.g., request via REST, response via Webhook).

    • This allows the server to choose the most suitable channel for delivering results.

  • Correlation Mechanism

    • Every request usually has a Correlation ID or Request ID.

    • The consumer uses this ID to match a response with its original request.

  • Long-Running Operation Friendly

    • Perfect for workloads where processing may take seconds, minutes, or hours.

    • Avoids keeping network resources open for extended periods.

  • Failure Resilience

    • Because communication is decoupled, temporary failures in one system do not immediately break the interaction.

    • Messages or responses can be retried or queued until delivery succeeds.

  • Event-Driven Integration

    • Often combined with message queues, publish-subscribe, or event streams to deliver responses asynchronously.

  • Stateless Request Phase, Stateful Correlation Phase

    • The request phase can be stateless, but the server or orchestration layer must keep track of request status until it is fulfilled.

Execution Flow

1. Client Sends the Initial Request

  • The client initiates a request to the server (e.g., POST /processReport with input data).

  • Instead of processing immediately, the server acknowledges receipt and assigns a Correlation ID.

  • Example Response:

    {
  "requestId": "abc-123",
  "status": "ACCEPTED",
  "message": "Report generation started"
}

2. Server Processes in the Background

  • The server places the request in a background job queue (e.g., RabbitMQ, Kafka, AWS SQS).

  • A worker service picks up the job and processes it asynchronously.

  • This could take seconds, minutes, or longer depending on workload.

3. Client Polls or Waits for Notification

There are two common approaches for receiving the final response:

A. Client Polling

  • The client periodically queries the server for the status: GET /status/abc-123

  • Server replies with progress info or final result.

B. Server Push / Callback

  • Once processing is done, the server sends a callback to the client (e.g., Webhook, event message).

  • This avoids polling and delivers results immediately.

4. Final Response Delivered

  • The client finally gets the completed data:

    {
  "requestId": "abc-123",
  "status": "COMPLETED",
  "reportUrl": "https://example.com/reports/abc-123.pdf"
}

Advantages

1. Improved User Experience

  • The client gets an instant acknowledgment instead of waiting for the entire operation to finish.

  • Allows UI to remain responsive, avoiding browser timeouts or mobile app freezes.

  • Example: A “Your request is being processed” message with a progress bar.

2. Handles Long-Running Tasks

  • Ideal for operations like:

    • Generating large reports

    • Video rendering

    • Machine learning model training

    • Large batch data imports

  • Prevents HTTP connection timeouts and reduces server-side resource locking.

3. Better Scalability

  • Heavy tasks are offloaded to background workers and queued, allowing API servers to focus on handling incoming requests.

  • Can easily scale worker services independently based on workload.

  • Fits well in microservices + message broker architectures.

4. Resilience & Fault Tolerance

  • If a worker crashes, the queued request remains safe in a message broker until another worker picks it up.

  • Enables retry mechanisms and dead-letter queues for failed jobs without affecting the client-facing API.

5. Decoupling Between Client and Processing Logic

  • The client does not need to know how or where the task is processed.

  • Allows backend teams to replace algorithms, change infrastructure, or distribute workloads without breaking the API contract.

6. Better Resource Utilization

  • Frees up HTTP threads quickly, allowing more concurrent requests.

  • Reduces CPU/memory spikes since processing is distributed over time and across workers.

7. Flexibility in Response Delivery

  • Multiple delivery options:

    • Polling (simpler for clients without push capabilities)

    • Webhooks (server pushes result to client)

    • Push Notifications / SSE / WebSockets

  • Can be tailored to client capabilities.

8. Natural Fit for Distributed & Cloud-Native Systems

  • Works perfectly with event-driven architectures and serverless functions.

  • Cloud-native platforms like AWS Lambda, Google Cloud Pub/Sub, and Azure Functions are optimized for this approach.

Limitations

1. Increased Implementation Complexity

  • Requires message queues, job schedulers, or background workers.

  • Needs additional infrastructure like RabbitMQ, Kafka, or cloud-native queue services (SQS, Pub/Sub).

  • More moving parts → more failure points to monitor.

2. Client-Side Complexity

  • The client must handle job status tracking and result retrieval (polling or listening to webhooks).

  • Requires extra logic for timeouts, retries, and handling partial results.

3. Higher Latency for Final Results

  • Since processing is deferred, clients may wait longer to receive the actual outcome.

  • Not suitable for tasks where immediate results are critical (e.g., credit card validation at checkout).

4. Error Propagation Challenges

  • In synchronous APIs, errors are returned instantly.

  • In asynchronous workflows:

    • Errors may occur much later.

    • They must be stored and delivered via polling, push, or callback mechanisms.

    • Requires a robust error payload structure.

5. Debugging and Monitoring Complexity

  • Tracing a single request across multiple services, queues, and workers can be harder.

  • Requires distributed tracing tools (e.g., OpenTelemetry, Jaeger, Zipkin) to track workflow.

6. Potential for Orphaned Requests

  • If the client disappears (e.g., user closes the browser) before fetching results, work might be processed unnecessarily.

  • Requires cleanup strategies or job expiration policies.

7. Security & Authentication Overhead

  • For polling or push updates, secure authentication tokens must be passed repeatedly.

  • Webhooks need to be verified to avoid spoofed callbacks.

8. More Infrastructure Cost

  • Requires extra compute for workers and persistent storage for job states.

  • Cloud-based queues and workers can incur additional costs if not optimized.

Common Technologies & Protocols Used

This pattern depends heavily on message brokers, queue systems, and async-friendly protocols to decouple request handling from processing.

1. Messaging & Queue Systems

These act as buffers to store requests until a worker processes them.

  • RabbitMQ – Reliable queue-based system with routing patterns.

  • Apache Kafka – Distributed streaming platform; great for high-throughput async jobs.

  • Amazon SQS / Google Pub/Sub / Azure Service Bus – Cloud-managed queues with built-in scaling.

  • ActiveMQ / Artemis – JMS-compliant brokers for enterprise systems.

2. Worker & Job Processing Frameworks

These tools manage background job execution.

  • Celery (Python) – Distributed task queue for async processing.

  • Resque / Sidekiq (Ruby) – Queue-backed job processors.

  • Spring Boot with @Async + Executor – Java native async execution.

  • BullMQ / Agenda (Node.js) – Job scheduling for JavaScript backends.

3. API Protocols & Communication Methods

While the submission of requests may be HTTP, result delivery can be through different mechanisms:

  • HTTP Polling – Client repeatedly queries the server for job status.

  • Webhooks (HTTP Callbacks) – Server pushes results back when ready.

  • Server-Sent Events (SSE) – One-way stream from server to client for updates.

  • WebSockets – Full-duplex communication for continuous status updates.

4. Data Serialization Formats

For result exchange between services:

  • JSON – Most common for REST-style APIs.

  • Protocol Buffers (Protobuf) – Compact, fast serialization (especially for gRPC).

  • Avro – Schema-based binary format (popular with Kafka).

5. Tracking & Orchestration Tools

To manage and monitor async workflows:

  • Camunda / Zeebe – BPMN-based workflow engines.

  • Temporal.io – Fault-tolerant workflow orchestration.

  • AWS Step Functions – Serverless workflow orchestration in the cloud.

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