Asynchronous Processing using Hangfire and AsyncFlow in .Net Core | HOW TO - Code Samples

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The Problem
Meet John, a diligent developer at a budding startup that specializes in data analytics. Their platform allows companies to upload vast amounts of raw data, transforming it into insightful visual reports. One sunny afternoon, John's team releases a new feature enabling batch processing of huge datasets. As John sips his coffee, watching real-time user logs, he notices something alarming.

A client, Acme Corp., uploads a massive dataset — one much larger than John ever anticipated. The system begins to process, but it’s not just the spinner on Acme’s dashboard that keeps turning. Several other clients start reporting slow response times, some even face timeouts. Panic ensues in the startup’s Slack channels.

John thinks, “The system is synchronous! It’s holding up resources for Acme’s giant request, affecting all our clients.” The weight of the problem sinks in. Not only are they risking a major client’s project, but they’re also compromising their service for every other client.

As John races against time to apply a hotfix, he wonders, “Isn’t there a better way to handle such massive requests without blocking others? A way where clients know their request is being processed, even if it’s going to take a while?”

Job-based Asynchronous Processing
Realization dawned on John. He recalled reading about a pattern during his research, something that seemed like the perfect fit for such scenarios: Job-based Asynchronous Processing. After another sip of his coffee and a deep breath, John delved into the concept to integrate it into their platform.

Job-based Asynchronous Processing works on a simple yet powerful principle: Instead of making the client wait for a response, immediately acknowledge the request and process it in the background.

This would allow John's platform to instantly free up resources for other clients while still handling Acme’s gargantuan dataset.

He imagined it as a ticket counter at a bustling train station. When a passenger (client) requests a ticket (processes data), instead of making them wait in line until their request is fulfilled, they are handed a token (Job ID). They can go about their other tasks and check back later with the token to see if their ticket is ready. If it’s not, they can continue waiting without holding up the line for other passengers.

This approach had several advantages:

Scalability: The system can scale more efficiently, handling numerous requests simultaneously without straining the system.

User Experience: Users aren’t left in the dark. They get immediate feedback that their request is being processed.

Resource Management: The server can better manage its resources, deciding which job to prioritize based on several factors like size, type, or premium clients.

But like all solutions, it wasn’t without its challenges:

Complexity: Implementing asynchronous processing might complicate the architecture, especially for systems not initially designed for it.

User Education: Users need to be educated about the new asynchronous system, ensuring they understand the importance of the Job ID and the need to check back for results.

With the pros clearly outweighing the cons and the urgency of the situation, John set forth to implement the Job-based Asynchronous Processing in their platform.

Introducing AsyncFlow: A Modern Solution
As John continued his research, he stumbled upon a promising solution: AsyncFlow. This library seemed like the perfect fit to streamline the implementation of the Job-based Asynchronous Processing pattern. Intrigued, John decided to dive deeper.

Before John could dive into AsyncFlow, he knew he needed to get his project environment set up.

He went to NuGet and added two essential packages:

John felt that this combination would allow him to harness the power of the Job-based Asynchronous Processing pattern effortlessly.

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