The art of simplicity

The timeout appeared without warning. A migration pipeline that had run fine in staging suddenly ground to a halt in production, throwing lock wait timeouts across multiple worker threads. The application was querying and writing to the same table, and somewhere in that dance of reads and writes, things had seized up entirely. Here is a walk through the investigation, from the first diagnostic query all the way to the fix — along with an explanation of why it worked. Find the blocker The first tool in any SQL Server lock investigation is sys.dm_exec_requests joined against sys.dm_exec_sessions . This query shows you every session that is currently blocked, who is blocking it, and what SQL both parties are running: A second query dug into the exact lock modes held on the specific table in question: The output told the story The results were unambiguous. Three sessions. One blocker. Two victims. Blocking Blocked Wait type ...

We recently discovered a bug in one of our legacy ASP.NET MVC applications — the kind that doesn't throw an exception, doesn't log a warning, and doesn't announce itself in any way. It simply silently drops a filter on the floor. Unfortunately, that filter happened to be a security control. Here's the story of what happened, why it happens, and what we're doing about it. Remark: If you don’t have any legacy .NET Full Framework ASP.NET MVC apps remaining (good for you), you can stop reading. What we were trying to do Our application uses action filters for authorization. We had a global filter registered for all controllers that enforced a baseline set of access rules. For certain controllers, we wanted a stricter policy, so we added a local filter attribute directly on those controllers. Both filters shared the same attribute type. The attribute was decorated like this: The intention was clear: the local, more restrictive filter on the controller would...

I only recently started using the ASP.NET Core's minimal API style, but an annoying thing I already encountered is the "long parameter list" problem. Route handlers that accept five, six, or seven parameters start to feel unwieldy fast. The good news is that a solution exists through the [AsParameters] attribute, introduced in .NET 7,  that gives you a clean way out. The problem it solves Minimal APIs are appealing precisely because they're lightweight — no controllers, no ceremony. But that simplicity starts to break down as your endpoints grow more complex. Consider this example endpoint: That's eight(!) parameters before you've written a single line of business logic. It's hard to read, hard to test, and grows more painful every time requirements change. Enter [AsParameters] [AsParameters] lets you group related parameters into a plain C# class or record and bind them all at once. ASP.NET Core inspects the type's constructor and public ...

Minimal APIs are the recommended approach for building fast HTTP APIs with ASP.NET Core. They allow you to build fully functioning REST endpoints with minimal code and configuration. You don't need a controller but can just declare your API using a fluent API approach: This makes it very convenient to build your APIs. However you need to be aware that a lot of magic is going behind the scenes when using this approach. And this magic can bite you in the foot. Exactly what happened to me while building an autonomous agent invoked through a web hook. The code In my application I created the following API endpoint using the minimal API approach: The minimal API injects an AzureDevOpsWebhookParser that looks like this: Nothing special… The problem The problem was when I called this endpoint, it failed with the following error message: InvalidOperationException: TryParse method found on AzureDevOpsWebhookParser with incorrect format. Must be a static method with for...

Over the course of my career, I've been part of multiple initiatives to start internal communities at work. Some of them became something genuinely special — people showing up, contributing, looking forward to the next gathering. Others quietly died after a few months, victims of low attendance and dwindling energy. For a long time I couldn't figure out what separated the successes from the failures. Was it the topic? The timing? The right people involved? I kept searching for the formula. Then I listened to an episode of the ReThinking podcast last week where Adam Grant sat down with Dan Coyle — author of The Culture Code and his new book Flourish — and one thing Coyle said stopped me in my tracks. Community, he pointed out, literally means shared gifts . And shared gifts aren't something you passively receive. They're something you participate in . We've been thinking about it the wrong way Maybe you’ve tried to build an internal community before. You ...

Being new to Microsoft Fabric I noticed that you have multiple options when writing notebooks using Python: run your code with PySpark (backed by a Spark cluster) or with Python (running natively on the notebook's compute). Both options look almost identical on the surface — you're still writing Python syntax either way — but under the hood they behave very differently, and picking the wrong one can cost you time, money, and unnecessary complexity. In this post I try to identify the key differences and give you some heuristics for deciding which engine to reach for. Python vs PySpark: what's actually different? When you select PySpark in a Fabric notebook, your code runs on a distributed Apache Spark cluster. Fabric spins up a cluster, distributes your data across multiple worker nodes, and executes transformations in parallel. The core abstraction is the DataFrame (or RDD), and operations are lazy — nothing actually runs until you trigger an action like .show() ...