Skip to main content

API Design in ASP.NET Core Part V

This week I had the honor to give a training to some of the newly started young professionals in our organisation. The topic of the training was API design in ASP.NET Core. During this training we discussed multiple topics and a lot of interesting questions were raised. I'll try to tackle some of them with a blog post.

The question I try to tackle today is...

Why should I make my action methods asynchronous?

I asked the question during the training why the second example is better than the first example:

Example 1 – Non async

Example 2 – Async

The answer I got the most was better performance, the idea that the second (async) example executes faster than the first example.

Although I wouldn’t say that this answer is completely incorrect, it is not the performance at the individual request level that improves. Instead it will be the general performance of your web application as a whole that will improve as the async version will give you beter scalability.

Why does the async method scales better?

Every time a request hits your server, a worker thread will process the request. A web server will typically have a limited number of worker threads available. Once the worker thread limit is reached, new requests will have to wait until a worker thread becomes available to process the request.

In the synchronous example above, the worker thread only becomes available after the request has completed. Although we have to wait for the database to return the list of products, the worker thread will just stop and wait.

If we compare this to the asynchronous example, the moment we do the database call, the await keyword allows the current worker thread to detach from the program and go back to the thread pool. Once the database call is done, .NET will take another thread from the pool and continue the work. Meanwhile another request can be handled by the worker.

More information: https://learn.microsoft.com/en-us/aspnet/core/performance/performance-best-practices?view=aspnetcore-6.0#avoid-blocking-calls

Popular posts from this blog

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME         ...

Cache stampede: when our cache turned against us

While investigating some performance issues, we ran into an ASP.NET Core API that cached a fairly expensive aggregation query for 60 seconds. Under normal load, that was fine: one request rebuilds the cache, everyone else reads from it. Under peak load, dozens of requests would arrive in that same expiry window, all see a cache miss, and all fire the same expensive query in parallel. The database didn't like that. That was the moment when our caching layer stopped helping and started hurting. A burst of requests comes in at the same time, all miss the cache, and all go hammer the database or the downstream API at once. That's a cache stampede . The cache was supposed to protect our backend, and for a few hundred milliseconds it did the opposite. Why this happens IMemoryCache.GetOrCreate (and its async sibling) looks like it protects you, but it doesn't add any locking on its own. Look at the naive version: public async Task<Report> GetReportAsync(string key) ...

A complex system designed from scratch never works

A few years ago, I worked as an architect on a big mainframe rewrite. I still count it as one of my failures. Not because the technology was wrong, but because I couldn't convince the management team to simplify the approach. Years later, the organization is still struggling to get the new system up and running. I left the project at the time, because I couldn't put my name behind an approach that would take very long and cost a lot of money without a working system to show for it along the way. Gall’s Law That memory keeps coming back to me, because it's a textbook case of Gall's Law playing out in real life. Gall's Law , from John Gall's Systemantics , states it plainly: A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works, and it cannot be patched to make it work. You have to start over with a simple system that works. What does that mean in practice,...