Practical .NET and Software Architecture Tips | Microsoft MVP

Hi there! 👋

I'm a senior software engineer, primarily working in the .NET ecosystem.

Things I talk about on LinkedIn:
- C# and .NET ❤
- Software engineering and architecture 
- Distributed systems and how to build them
- Databases and optimization techniques
- Career and personal growth

One of my goals (i̶s̶) was to become a Microsoft MVP in Developer Technologies.
I became an MVP in March of 2023.

If this sounds interesting, let's connect!

Milan Jovanović

You don’t need a Dockerfile anymore.

Yes, really.

Modern .NET can publish your app directly as a container image using the SDK. No scaffolding. No boilerplate. No hand-written Dockerfiles.

Here's what you'll learn:
- How dotnet publish builds a Linux container
- How to run it locally with Docker
- How to push it to GitHub Container Registry
- Why this makes self-hosting .NET apps on a VPS much simpler

If you’ve ever copied a Dockerfile you didn’t fully understand, this is for you.

Check it out: https://lnkd.in/dUFzz3du

This is also the first step in a new series I’m working on around self-hosting .NET apps without overengineering.

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AI in .NET is way easier than people think.

And you can start for free.

If you want a practical first project, build vector search:
- Turn text into embeddings (vectors)
- Store them in your vector store (pg_vector)
- Turn the user’s query into a vector
- Compare with cosine similarity to find the closest matches

That’s why it feels like “search by meaning” instead of keywords.

To do this cleanly in .NET, start with MEAI (Microsoft Extensions AI), the official library for LLM + embeddings.

Start here: https://lnkd.in/dkpadmVc

---

Do you want to simplify your development process? Grab my Clean Architecture template here and save 7 days of development time: https://lnkd.in/dfGYcyFC

I think this makes me an Azure core developer now

/s

Should you stop using random UUIDs in .NET?

Traditional UUIDs (Guid.NewGuid()) are randomized. Great for uniqueness, but terrible for database performance.

They cause:

❌ Expensive index rebalancing
❌ Poor insert performance
❌ High fragmentation

That’s why many developers switched to ULIDs. It's 128-bit, sortable, and still unique.

They preserve temporal order, which makes them more index-friendly.

But now, there’s something new:

✅ Guid.CreateVersion7() in .NET 9+

UUID V7 is time-ordered and compatible with ULIDs, without needing a 3rd-party package.

You get the benefits of ULID, but natively supported in .NET.

Would you switch from ULID to V7 UUIDs?

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Not all Load Balancing strategies are created equal.

If you are using YARP (Yet Another Reverse Proxy) in .NET, you have 5 built-in ways to distribute traffic. But which one should you choose?

Here is the breakdown:

1. RoundRobin: The classic. It cycles through servers evenly. Simple, but it assumes all servers are equally fast.

2. LeastRequests: The smart choice. It sends traffic to the server currently doing the least work.

3. Random: It picks a server at random. Surprisingly effective for high-volume, uniform workloads.

4. PowerOfTwoChoices: The balanced approach. It picks two random servers and chooses the one with fewer requests. It avoids the overhead of checking every server.

5. FirstAlphabetical: The "sticky" one. It always picks the first available server. Good for failover setups, bad for load distribution.

Most people stick with RoundRobin, but switching to LeastRequests can significantly improve tail latency in systems where request times vary.

I dive deeper into how to scale horizontally using YARP here: https://lnkd.in/daDNMpmB

Which strategy is your default choice?

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Stop calling DateTime.UtcNow in business logic.

This line looks harmless... and it’s the reason your tests are a mess.

Here’s the trap:

- DateTime.UtcNow > user.SubscriptionEnd

Seems harmless, right?

But the moment you try to test this logic, you realize the problem:

- You can’t control DateTime.UtcNow
- Your tests become flaky or complicated
- Business rules are tied to infrastructure

There are better ways:

1. Inject DateTime as a parameter — simplest, great for small methods

2. Introduce an IClock interface — more scalable, and test-friendly

By inverting the dependency on time, you make your domain logic:

✅ Easier to test
✅ More deterministic
✅ Separation of concerns

Next time you write a time-based condition, stop and ask:

“Will I need to test this?”

If the answer is yes, extract the clock.

---
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The best HttpClient config is the one you don’t repeat in every service.

In .NET, you set it up once with IHttpClientFactory (and Aspire if you’re using it):
- Discovery for service-to-service calls
- Resilience defaults so transient failures don’t hurt you

Then you just inject the client and write business code.

Want the full walkthrough?

Start here: https://lnkd.in/dQNxjHix

---

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Here are 9 excellent libraries I use in my projects:

1. YARP
2. Polly
3. EF Core
4. Refit
5. Testcontainers
6. OpenTelemetry
7. FluentValidation
8. xUnit
9. Dapper

Which library would you add to the list?

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Stop trying to force Domain-Driven Design into every feature.

Sometimes, the best architecture is the simplest one: The Transaction Script.

It’s a classic pattern from Martin Fowler, and it fits 80% of CRUD scenarios perfectly.

Instead of building complex aggregates and value objects, you just write a simple, procedural script that handles a single request:

1. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 the input data.
2. 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 the result (business logic).
3. 𝗣𝗲𝗿𝘀𝗶𝘀𝘁 the changes to the database.
4. 𝗥𝗲𝘁𝘂𝗿𝗻 the response.

It is fast, readable, and easy to test.

I wrote an article showing a side-by-side comparison of this vs. a Rich Domain Model.

See the code here: https://lnkd.in/d9dwgpdq

👉 When to switch: Start with a Transaction Script. Only refactor to a Domain Model when the business logic becomes "chatty", meaning you have too many if/else blocks or complicated state transitions that are hard to track in a single method.

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In .NET, you usually have to choose between Speed and Consistency.

In-Memory Caching (IMemoryCache):
✅ Blazing fast (nanoseconds).
❌ Data is stuck on one server. If you have 10 nodes, you have 10 caches.

Distributed Caching (IDistributedCache / Redis):
✅ Shared across all servers. Consistent data.
❌ Network latency. Serialization costs. It's slower.

The Solution: HybridCache.

Don't choose one. Use both.

A Hybrid Cache creates a tiered architecture:

- L1 (Local): The app checks RAM first. If it's there, return instantly.
- L2 (Distributed): If missing locally, check Redis.
- Source: If missing in Redis, fetch from DB, then populate both L1 and L2.

While Microsoft is working on a native implementation, I still swear by FusionCache as the most robust, battle-tested library for this.

I wrote a guide on how to set this up properly: https://lnkd.in/d6NKgMVp

Are you currently using just Redis, or a multi-layer cache?

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Most OAuth demos are useless in the real world.

They skip the part that matters: how public clients should actually get tokens.

I just published a step-by-step video on running Keycloak in Docker + OAuth 2.0 Authorization Code flow with PKCE.

What you’ll see:
- Keycloak (latest) running locally with Docker/Compose
- Realm + user + public client setup
- Using .well-known/openid-configuration so you don’t guess endpoints
- Full PKCE flow in Postman (authorize → code → token exchange)
- Quick JWT inspection + the “why is this 401/403?” scope moment

If you’re building SPAs or mobile apps, PKCE isn’t optional. It’s the safe default.

Watch it here: https://lnkd.in/deZWiEbi

The worst kind of bug is the one that explodes 3 days after deployment.

This is the hidden danger of the standard Options Pattern in .NET.

You might bind your appsettings.json to a class, inject it, and everything looks fine. The app starts up perfectly.

But if a required API Key is missing? You won't know until a user triggers that specific feature, and the app crashes.

This is why "Fail Fast" is crucial for configuration.

If your config is invalid, your application should refuse to start. Period.

You can achieve this by extending the Options Pattern with IValidateOptions.

Instead of just binding data, you attach validation logic:

1. Define rules (e.g., "ApiKey cannot be null", "RetryCount must be > 0").
2. Register the validator.
3. If the config breaks the rules, the DI container throws an exception immediately at startup.

I took this a step further and integrated FluentValidation to make the rules cleaner and more expressive.

Here is the complete implementation code: https://lnkd.in/djz5Yfux

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Hidden danger of Options Pattern in .NET

I failed a .NET interview question that looked easy.

“Add caching to an async API.”

The tricky part isn’t caching.
It’s what happens when 50 requests hit the same cold cache at once:
- race conditions
- cache stampedes (thundering herd)
- why ConcurrentDictionary still isn’t enough
- why you can’t await inside a lock
- the SemaphoreSlim async-lock pattern (with try/finally, double-check, timeout)
- scaling it with per-key locks to reduce contention

I break it down step-by-step using a currency conversion example, and show the exact refactor path from “works locally” to “works under load.”

Watch it here: https://lnkd.in/dyN7_acX

How did we make a simple idea so complicated?

CQRS is the simplest pattern out there.

First, there was CQS: Command Query Separation.

What does this mean? We split an object's methods into write (command) and read (query) methods.

CQRS is the evolution of CQS. CQRS works on the architectural level. At the same time, CQS works on the method (or class) level.

The CQRS pattern uses separate models for reading and updating data.

This separation can be logical OR physical.

My preferred approach is starting with logical CQRS.

One database + separate flows for writes and reads.

You can design a data model fine-tuned for each operation.

A lot of developers stay away from CQRS because they think it's complex.

The reality is you're probably already applying CQRS principles without knowing it.

Here's a simple implementation: https://lnkd.in/d-qUdpn3

What do you think about the CQRS pattern?

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If you need to run a task "Every Friday at 5 PM," how do you implement it?

A While(true) loop with a Thread.Sleep() is not the answer.

You need a robust scheduler that understands Cron Expressions.

A Cron expression allows you to define complex recurring schedules with a compact string.

For example:

• 15 10 * * * = 10:15 AM every day.
• 15 10 * * MON-FRI = 10:15 AM, but only on weekdays.

In the .NET ecosystem, the heavyweight champion for this is Quartz.

It parses these expressions for you and ensures your background services fire exactly when they are supposed to.

I broke down how to configure your first job and trigger in this article: https://lnkd.in/dfy-U7jp

What library do you currently use for background jobs?

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Stop wasting the first 3 days of every project setting up boilerplate.

I built a free, production-ready Clean Architecture template to handle the plumbing for you.

It's already used by 40,000+ developers.

Here is what's inside the latest version:

- 𝗨𝗽𝗱𝗮𝘁𝗲𝗱 𝘁𝗼 .𝗡𝗘𝗧 𝟭𝟬
- 𝗡𝗲𝘄 .𝘀𝗹𝗻𝘅 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗙𝗼𝗿𝗺𝗮𝘁
- CQRS (No MediatR dependency)
- Structured Logging & Validation
- JWT Authentication & RBAC
- Docker Compose support

It saves you hours of setup time.

Download it for free here: https://lnkd.in/dbU3m97Q

💡 Why the shift to .slnx? The new Solution format in .NET is a game-changer. It replaces the old bloated syntax with a clean XML syntax. It makes merge conflicts in your solution file a thing of the past.

AI won’t replace you in 2026.

But a developer who knows how to engineer with AI absolutely will.

I recorded a new video on how I’m using AI tooling for .NET development (mainly Cursor), and the point isn’t Cursor.

It’s the workflow.

Here’s what I do every time:
- Plan first (write the steps before touching code)
- Give AI tight constraints (what to change, what NOT to change)
- Review like it’s a PR (DI lifetimes, packages, SQL correctness, connection handling)
- Verify (build + tests) so you don’t ship “confidently wrong” code

In the video, I walk through a real refactor: moving query handlers from EF Core to raw SQL/Dapper, and show the exact spots where AI gets you 90% there... and where that last 10% can break things.

You can watch it here: https://lnkd.in/dzZiKR66

If you’re trying to “stay relevant” in the AI era, the skill isn’t prompting.

It’s owning correctness.

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Data Annotations are great for simple things.

[Required]? Fine.
[MaxLength(50)]? Sure.

But what happens when you need to validate something against a database?
Or validate based on a configuration setting in appsettings.json?

Data Annotations hit a wall. You can't easily inject dependencies into an Attribute.

This is where FluentValidation shines.

Because your validators are just standard classes registered in the DI container, you can inject anything you need.

Want to check if a CouponCode is valid by calling the IPricingService? Just inject it into the OrderValidator constructor.

It turns validation from a static check into a dynamic, intelligent logic layer.

I explain how to wire this up seamlessly in ASPNET Core here: https://lnkd.in/d9nVR8fu

It is a game changer for complex business rules.

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Vedran B.

Many such cases. 

OOP pushed by DDD has become a dog chasing it's own tail.

I've been seeing more and more posts on tech Reddit like that.

Good. Push back against lunacy.

Vedran Bilopavlović

NpgsqlRest Automatic PostgreSQL Web Server

Here is a really, really interesting side-by-side comparison I did with a little help from AI. You tell me, is it far off from reality?

1. PostgreSQL analytical query

Fairly complex zero-islands detection analytical query in a hypothetical IOT scenario. Something that you would probably have to write ad-hoc if some of your devices malfunctions. It detects consecutive zeros in data, and it runs very fast.

2. Same query on MongoDB

I'm not sure how this would even run, but AI says not too good. Query planners are not even comparable. If we ignore the sheer ugliness of that query (if you can even call it that), and let's just say that it is a matter of personal taste (although the Mongo version is longer), why would you want to deprive your system of these kinds of ad-hoc analytics?

But wait, it gets worse. Let's say, we don't want this to be ad-hoc anymore - we want this to be on our dashboard.

3. Same PostgreSQL version in modern C# and .NET with EF and modern DDD

As we can see, modern EF has limited support for window functions. You can do one window function (and with a limited subset) via an extension that would translate a grouping expression into a window function (which makes it even more confusing, is it a grouping or a window function), but it cannot do two consecutive window functions one after another, so we must fall back to raw SQL.

But wait, it gets worse.

4. Modern C#/.NET fully Linq/DDD and everything

But we can always listen to wise advice from our beloved gurus and keep this business logic out of the database and SQL out of our codebase.

Nevermind that our codebase has now doubled again and bloated out of proportion (we haven't even got to the unit tests, oh boy), we are now actually downloading the database into memory in order to be able to process it properly. Performances of something like this are going to be abysmal.

And yet, so many teams are prepared to do exactly this - they are making their users suffer and wait for responses and pay huge cloud bills, just to make their domain pure and clean.

Insanity.

But, wait, it gets worse.

5. MongoDB with modern C# and .NET and DDD

At this point, I don't know what to tell you anymore. Just learn SQL. Thank me later.

I don't have anything smart to say about AI. The worlds best and brightest minds are on the case. But I do about PostgreSQL. What else to do on hangover Sunday, and I'm right?

Did you know you can format PostgreSQL intervals to a human-readable format directly in PostgreSQL?

Well, neither have I.

If we go and create an interval:  "select 8197156465 * interval '1 ms'" It returns boring "2276:59:16.465". Not very nice.

We can use the function "justify_interval" that adds days and months if the interval exceeds 24 hours, and so this "select justify_interval(8197156465 * interval '1 ms')" returns this: "3 mons 4 days 20:59:16.465". A little bit better, but we can do even better than this.

There is a setting called "intervalstyle" which is by default set to "sql_standard". We can set it to "postgres_verbose".

However, doing "set intervalstyle = 'postgres_verbose';" would affect the entire connection (session), which is problematic if we use a connection pooler (and who doesn't). So we must set it locally to a transaction scope, and in order to do that, our query needs to run in a dedicated transaction. So now we can do this and try with and without "justify_interval" function:

begin;
set local intervalstyle = 'postgres_verbose';
select 
    8197156465 * interval '1 ms',
    justify_interval(8197156465 * interval '1 ms');
rollback;

This returns two columns:
@ 2276 hours 59 mins 16.465 secs	
@ 3 mons 4 days 20 hours 59 mins 16.465 secs

For some reason, this PostgreSQL interval includes "@ " in front of every interval. So we can trim that:

begin;
set local intervalstyle = 'postgres_verbose';
select ltrim(justify_interval(8197156465 * interval '1 ms')::text, '@ ');
rollback;

Yields "3 mons 4 days 20 hours 59 mins 16.465 secs". Much better than "2276:59:16.465".

Just don't forget to read from the 3rd command (instead of the 1st), and that is it.

This Bun thingy is getting crazier and crazier but in a good way. 30% faster, 50% faster, etc 

I'm using it for a frontend build scripts and occasionally for some server services when needed for simple tasks, but more and more.

Crazy how we all got used of living woth JavaScript and all it's quirks and logical black holes.

Can't beat them, join them.

https://lnkd.in/d2zcYh5C

I am actually thinking of buying a pizza and coca cola to Claude Code to show my gratitude. Like every responsible and good manager does. Am I right.

Scrolling trough tech interwebs is new doom scrolling but only for software development:

- Software Engineering is just about to die
- Software Engineering is not dead yet, it just smells real bad
- Software Engineering is dying
- Software Engineering is almost dead
- Software Engineering is dying and is dead
- Software Engineering is buried

You mean to tell me no more ORMs, no more DTO mappings, no more DI configurations, no more Repository Service Unit of Work Query Builder Domain Object Immutable Record bullshit? Is that what you saying? Good riddance.

And why we were doing that in the first place? 

Just to execute the query on something like PostgreSQL that looks something like in this picture bellow?

If software engineering is dead, and AI generates all the code, then we certainly can skip all of that and just instruct AI to construct raw SQL — and cover everything with AI-generated tests.

Think about it:

☑️ Schema is the full context, everything that AI needs actually a single source of truth
☑️ SQL is the best-understood language, LLMs have seen more SQL than any framework in existence.
☑️ One artifact per feature, no coordinating across layers, fewer hallucinations
☑️ Instant verification — test the function with Pg/PlSql asserts, not through 7 layers of abstraction

That is, if you use automatic web server you your PostgreSQL: https://lnkd.in/dVsE-3Q8

☑️ Less context means fewer hallucinations, AI can't mess up your DI config, domain whatever or immutable thingy if there is none 
☑️ If so much code is either automated (http, endpoints, infrastructure, etc) or auto-generated (TypeScript fetch modules, HTTP files for testing, etc) - then we have reduced our chances for bugs and errors.

It is a radical concept, but such are these challenging times. I believe this is the way.

taplio

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taplio

The Battle

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Vedran B.

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