The art of simplicity

In most enterprise applications authentication is externalized to an external identity provider. For our applications we had to support multiple identity providers. How can we do this without tight coupling to each of these providers? In this post we'll bring OpenIddict into the picture and show how it helps to solve this nicely. What is OpenIddict? OpenIddict is an open-source OAuth2/OpenID Connect server and client stack for .NET. It plugs into ASP.NET Core Identity (or your own user store) and lets you run your own authorization server instead of depending fully on an external one. You get the standard endpoints — /connect/authorize , /connect/token , /connect/userinfo — backed by your own database and your own claims. That's exactly what makes the passthrough pattern possible: OpenIddict is the piece in the middle that can defer to external providers for the actual authentication, then still be the one issuing the token your APIs trust. The naive approach The ...

At a recent talk I compared AI adoption to the introduction of compilers: a new layer of abstraction that lets us work at a higher level, the same way compilers let us stop writing assembly by hand. In the hallway afterwards, a few people pushed back on that comparison. They were right to. Here's the point I should have made from the stage: agents are not deterministic, and that single difference breaks the analogy. Compilers follow rules A compiler is not a magical black box. You give it code, and it gives you a binary, following a fixed set of rules. Given the same input, a compiler will reliably produce the same output, every time. You don't hope the binary does what you wrote. You trust the compiler, because the transformation is deterministic. That determinism is exactly what let us move up the abstraction ladder in the first place. We stopped worrying about registers and memory addresses because we could trust the layer below us to behave the same way twice....

Note: this is part 3 of a series on the Azure Monitor Profiler. Part 1 covered what the Profiler is, part 2 covered enabling it. This post is about the part that actually matters: making sense of a trace once you have one. Having profiler traces is only half the job. I've seen people enable the profiler, open a trace, stare at a wall of unfamiliar method names, and close the tab. The trace explorer isn't self-explanatory the first time - it took me a few real incidents before the views clicked. This post is the walkthrough I wish I'd had. Getting to a trace From your Application Insights resource: Go to the Performance tab Pick an operation from the list or leave Overall selected Click on Profiler traces   Pick one of the captured requests, ideally one with a longer duration than usual - that's where you'll actually find something. Once you're in a trace, you get two views of the same call stack data: Flame graph - the full...

Note: this is part 2 of a series on the Azure Monitor Profiler. Part 1 covered what the Profiler is and where to find the results - this post covers actually turning it on. There are two ways to enable the profiler: through app settings on App Service, or by wiring it into your code directly. Which one you need depends on where your app runs and how much control you want over the setup. Option 1: codeless enablement on App Service If your app runs on App Service (Windows) and your Application Insights resource is in the same subscription, this is the easiest path - no code changes, no redeploy. From the portal: In your App Service instance, select Monitoring > Application Insights Select Turn on Application Insights , then Enable Scroll down to the .NET or .NET Core tab Set Collection level to Recommended Under Profiler and Code Optimizations , select On Apply , then confirm with Yes Or skip the portal entirely and set the app settings directly...

Yesterday one of our Application Insights dashboard showed a P95 latency spike and we had no idea why. The telemetry told us that a request was slow. But it didn't tell us why . Was it a database call? A CPU-bound loop? Lock contention? A GC pause? The naive approach would have to been to add logging statements around the code you suspect, redeploy, wait for the issue to reproduce, and repeat. In production, that's slow and it doesn't scale - you're guessing, and every guess costs a deployment cycle. We stayed away from all that guess work and reached out to the Application Insights Profiler . Instead of reasoning from logs, you get actual flame graphs of real production requests, showing exactly where time was spent. What the profiler actually captures The profiler runs as an agent alongside your application and periodically captures traces of live requests - not synthetic load, actual production traffic. For each captured request, it builds a trace you can in...

Recently I lost way more time than I'd like to admit on an error that turned out to be one missing line of code. The symptom looked like a networking problem. The cause was a missing WithReference call in my Aspire AppHost. Here's the exception my proxy threw the moment it tried to forward a request to my API: System.Net.Http.HttpRequestException: No such host is known. (api:443) ---> System.Net.Sockets.SocketException (11001): No such host is known. at System.Net.Sockets.Socket.AwaitableSocketAsyncEventArgs.ThrowException(SocketError error, CancellationToken cancellationToken) at System.Net.Sockets.Socket.AwaitableSocketAsyncEventArgs.System.Threading.Tasks.Sources.IValueTaskSource.GetResult(Int16 token) at System.Net.Http.HttpConnectionPool.ConnectToTcpHostAsync(String host, Int32 port, HttpRequestMessage initialRequest, Boolean async, CancellationToken cancellationToken) --- End of inner exception stack trace --- at System.Net.Http.HttpConnectionPool....