Async API Design: Handling Long-Running Operations

Picture this: your user uploads a 4K video for processing, clicks submit, and... waits. And waits. Meanwhile, your server is churning through transcoding operations that could take minutes or even hours. Do you keep that HTTP connection open the entire time? Do you ask the user to refresh their browser repeatedly?

If you've ever built a system that handles heavyweight operations like video processing, report generation, or data analysis, you know that synchronous APIs just don't cut it. The moment your operation takes longer than a few seconds, you need to think asynchronously. This shift from "request-response" to "request-acknowledge-notify" fundamentally changes how you architect your systems.

The challenge isn't just technical, it's about user experience too. Users need feedback, progress updates, and reliable notification when their job completes. Getting this wrong means frustrated users and brittle systems that timeout under load.

Core Concepts

The Async API Pattern

Unlike traditional synchronous APIs where the client waits for a complete response, async APIs immediately return an acknowledgment and handle the actual work in the background. Think of it like dropping off dry cleaning: you get a ticket right away, but the actual cleaning happens behind the scenes.

The core components of any async API system include:

• Job Queue: Stores pending work items with metadata and priority information
• Worker Services: Background processes that consume jobs from the queue and execute the actual operations
• Status Tracking Service: Maintains the current state of each job (pending, processing, completed, failed)
• Notification System: Delivers updates back to clients through various channels

Communication Patterns

There are several ways to keep clients informed about their long-running operations:

Polling involves clients periodically checking a status endpoint to see if their job has completed. It's simple to implement but can be inefficient, especially with many clients checking frequently.

Webhooks flip the script by having your server call the client's endpoint when something changes. This is event-driven and efficient, but requires clients to expose callable endpoints.

Server-Sent Events (SSE) create a persistent connection where the server can push updates to the client in real-time. This works well for web applications but requires connection management.

WebSockets provide bidirectional real-time communication but add complexity for simple status updates.

How It Works

The Request Flow

When a client initiates a long-running operation, the system follows a predictable flow. First, the API endpoint validates the request and immediately returns a job identifier, typically with a 202 Accepted status. This job ID becomes the key for all future interactions.

Behind the scenes, the system creates a job record in persistent storage and adds it to a processing queue. The job record contains everything needed to execute the operation: input parameters, client information, creation timestamp, and current status.

Worker services continuously monitor the queue for new jobs. When a worker picks up a job, it updates the status to "processing" and begins the actual work. Throughout execution, the worker can publish progress updates to keep clients informed of advancement.

Status Management

Effective job status tracking requires more than just "done" or "not done." A robust system tracks multiple states:

• Queued: Job is waiting to be processed
• Processing: Work is actively underway
• Progress: Intermediate updates with completion percentages or step information
• Completed: Job finished successfully with results
• Failed: Job encountered an error with diagnostic information
• Cancelled: Job was terminated before completion

Each status transition gets recorded with timestamps, enabling clients to understand not just what happened, but when it happened.

Notification Delivery

The notification system acts as the bridge between your backend processing and client applications. When you're planning these architectures, tools like InfraSketch help you visualize how these notification flows connect to your existing systems.

For webhook-based notifications, the system maintains a registry of client callback URLs associated with each job. When status changes occur, a notification service attempts delivery with proper retry logic and failure handling.

SSE implementations maintain active connections in memory or distributed cache, pushing updates immediately when they occur. This requires careful connection lifecycle management and cleanup of stale connections.

Design Considerations

Choosing Your Communication Pattern

The choice between polling, webhooks, and real-time connections depends on your specific requirements and constraints.

Polling works best when updates are infrequent or when clients can't receive incoming connections. It's also the most reliable pattern since it doesn't depend on network connectivity between updates. However, it creates unnecessary load when clients poll too frequently, and introduces delays when they poll too infrequently.

Webhooks excel when you need immediate notifications and your clients can expose HTTP endpoints. They're efficient and event-driven, but require robust retry logic and error handling. Client endpoints must be reliable and secure, which isn't always feasible.

Server-sent events provide a middle ground for web applications, offering real-time updates without the complexity of WebSockets. They work well through proxies and firewalls but require connection state management.

Scaling Considerations

As your system grows, several scaling challenges emerge. Job queues can become bottlenecks if not properly partitioned or if worker capacity doesn't match job creation rates. Consider using multiple queues based on job types or priorities.

Worker scaling needs to account for the nature of your operations. CPU-intensive jobs benefit from horizontal scaling across multiple instances, while I/O-heavy operations might need connection pooling or async processing within workers.

Status storage becomes a concern at scale. Frequently accessed status records should live in fast storage like Redis, while historical job data can move to cheaper persistent storage. You can visualize these storage tiers and their interactions using InfraSketch to ensure your data flow makes sense.

Notification delivery at scale requires queuing and rate limiting to avoid overwhelming client systems or external services. Failed deliveries need dead letter queues and alerting to prevent silent failures.

Reliability and Error Handling

Long-running operations are inherently prone to failures. Network issues, service restarts, and resource constraints can interrupt processing at any point. Design your system with failure as the default case, not the exception.

Implement job persistence that survives service restarts. Use transactional updates when changing job status to prevent inconsistent states. Build comprehensive retry logic with exponential backoff for both job processing and notification delivery.

Consider implementing job timeouts to prevent operations from running indefinitely. Provide clear error messages and diagnostic information when jobs fail, enabling clients to understand what went wrong and whether retrying makes sense.

Security and Authentication

Async operations often span extended time periods, raising questions about authentication and authorization. Job tokens should be separate from user session tokens to prevent jobs from failing when users log out.

For webhook notifications, implement secure delivery mechanisms like signed payloads or mutual TLS. Don't include sensitive data in webhook payloads; instead, provide enough information for clients to fetch details through authenticated channels.

Rate limiting becomes crucial to prevent abuse of expensive long-running operations. Consider implementing both per-user limits and system-wide capacity controls.

Key Takeaways

Successful async API design starts with accepting that long-running operations need fundamentally different patterns than quick request-response cycles. The immediate acknowledgment of work followed by background processing and notification creates better user experiences and more scalable systems.

Choose your notification strategy based on client capabilities and update frequency requirements. Polling works universally but can be inefficient. Webhooks provide immediate notifications but require reliable client endpoints. Real-time connections offer the best user experience but add operational complexity.

Status tracking is more than just "done" or "not done." Rich status information with progress updates and detailed error reporting helps clients provide better experiences to their users. Proper state management prevents lost jobs and enables effective debugging.

Design for failure from the beginning. Long-running operations will fail, networks will disconnect, and services will restart. Robust retry logic, persistent job storage, and comprehensive error handling separate production-ready systems from prototypes.

Security considerations extend beyond the initial API call. Job lifecycles often outlast user sessions, requiring thoughtful authentication design and secure notification delivery.

Try It Yourself

Now that you understand the patterns and trade-offs in async API design, try architecting your own system. Consider a specific use case like image processing, report generation, or data import. What components would you need? How would the data flow between them? Which notification pattern makes sense for your clients?

Head over to InfraSketch and describe your system in plain English. In seconds, you'll have a professional architecture diagram, complete with a design document. No drawing skills required.

Whether you're designing a simple job queue with polling or a complex real-time notification system with multiple worker types, visualizing your architecture helps you spot potential issues before you start coding. Start with your core components, then layer in your scaling and reliability requirements.