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[**Vertical Slice Architecture**](vertical-slice-architecture) (VSA) seems like a breath of fresh air when you first encounter it.
You stop jumping between seven layers to add a single field.
You delete the dozens of projects in your solution.
You feel liberated.

But when you start implementing more complex features, the cracks begin to show.

You build a `CreateOrder` slice.
Then `UpdateOrder`.
Then `GetOrder`.
Suddenly, you notice the repetition.
The address validation logic is in three places.
The pricing algorithm is needed by both Cart and Checkout.

You feel the urge to create a `Common` project or `SharedServices` folder.
This is the most critical moment in your VSA adoption.

Choose wrong, and you'll reintroduce the coupling you were trying to escape.
Choose right, and you maintain the independence that makes VSA worthwhile.

Here's how I approach **shared code** in **Vertical Slice Architecture**.

## The Guardrails vs. The Open Road

To understand why this is hard, we need to look at what we left behind.

[**Clean Architecture**](clean-architecture-the-missing-chapter) provides strict guardrails.
It tells you exactly where code lives: Entities go in Domain, interfaces go in Application, implementations go in Infrastructure.
It's safe.
It prevents mistakes, but it also prevents shortcuts when they're appropriate.

[**Vertical Slice Architecture**](vertical-slice-architecture-structuring-vertical-slices) removes the guardrails.
It says, "Organize code by feature, not technical concern".
This gives you speed and flexibility, but it shifts the burden of discipline onto _you_.

So what can you do about it?

## The Trap: The "Common" Junk Drawer

The path of least resistance is to create a project (or folder) named `Shared`, `Common`, or `Utils`.

This is almost always a mistake.

Imagine a `Common.Services` project with an `OrderCalculationService` class.
It has a method for cart totals (used by Cart), another for historical revenue (used by Reporting),
and a helper for invoice formatting (used by Invoices).
Three unrelated concerns.
Three different change frequencies.
One class coupling them all together.

A `Common` project inevitably becomes a junk drawer for anything you can't be bothered to name properly.
It creates a tangled web of dependencies where unrelated features are coupled together because they happen to use the same helper method.

You've reintroduced the very coupling you tried to escape.

## The Decision Framework

When I hit a potential sharing situation, I ask three questions:

**1. Is this infrastructural or domain?**

Infrastructure (database contexts, logging, HTTP clients) almost always gets shared. Domain concepts need more scrutiny.

**2. How stable is this concept?**

If it changes once a year, share it. If it changes with every feature request, keep it local.

**3. Am I past the "Rule of Three"?**

Duplicating the same code once is fine.
However, creating three duplicates should raise an eyebrow.
Don't abstract until you hit three.

We solve this by refactoring our code.
Let's look at some examples.

## The Three Tiers of Sharing

Instead of binary "Shared vs. Not Shared," think in three tiers.

### Tier 1: Technical Infrastructure (Share Freely)

Pure plumbing that affects all slices equally: logging adapters, database connection factories, auth middleware,
the [**Result pattern**](functional-error-handling-in-dotnet-with-the-result-pattern), validation pipelines.

Centralize this in a `Shared.Kernel` or `Infrastructure` project.
Note that this can also be a folder within your solution.
It rarely changes due to business requirements.

```csharp
// ✅ Good Sharing: Technical Kernel
public readonly record struct Result
{
    public bool IsSuccess { get; }
    public string Error { get; }

    private Result(bool isSuccess, string error)
    {
        IsSuccess = isSuccess;
        Error = error;
    }

    public static Result Success() => new(true, string.Empty);
    public static Result Failure(string error) => new(false, error);
}
```

### Tier 2: Domain Concepts (Share and Push Logic Down)

This is one of the best places to share logic.
Instead of scattering business rules across slices, push them into entities and value objects.

Here's an example:

```csharp
// ✅ Good Sharing: Entity with Business Logic
public class Order
{
    public Guid Id { get; private set; }
    public OrderStatus Status { get; private set; }
    public List<OrderLine> Lines { get; private set; }

    public bool CanBeCancelled() => Status == OrderStatus.Pending;

    public Result Cancel()
    {
        if (!CanBeCancelled())
        {
            return Result.Failure("Only pending orders can be cancelled.");
        }

        Status = OrderStatus.Cancelled;
        return Result.Success();
    }
}
```

Now `CancelOrder`, `GetOrder`, and `UpdateOrder` all use the same business rules.
The logic lives in one place.

<div className="note-panel">
  This implies an important concept: **different vertical slices can share the
  same domain model.**
</div>

### Tier 3: Feature-Specific Logic (Keep It Local)

Logic shared between related slices, like `CreateOrder` and `UpdateOrder`, doesn't need to go global.
Create a `Shared` folder (there's an exception to every rule) within the feature:

```
📂 Features
└──📂 Orders
    ├──📂 CreateOrder
    ├──📂 UpdateOrder
    ├──📂 GetOrder
    └──📂 Shared
        ├──📄 OrderValidator.cs
        └──📄 OrderPricingService.cs
```

This also has a hiddene benefit.
If you delete the Orders feature, the shared logic goes with it.
No zombie code left behind.

Let's explore some advanced scenarios most people overlook.

## Cross-Feature Sharing

What about **sharing code between unrelated features** in Vertical Slice Architecture?

The `CreateOrder` slice needs to check if a customer exists.
`GenerateInvoice` needs to calculate tax.
Orders and Customers both need to format notification messages.

This doesn't fit neatly into a feature's `Shared` folder.
So where does it go?

**First, ask: do you actually need to share?**

Most cross-feature "sharing" is just data access in disguise.

If `CreateOrder` needs customer data, it queries the database directly.
It doesn't call into the Customers feature.
Each slice owns its data access.
The `Customer` entity is shared (it lives in `Domain`), but there's no shared service between them.

**When you genuinely need shared logic**, ask what it _is_:

- **Domain logic** (business rules, calculations) → `Domain/Services`
- **Infrastructure** (external APIs, formatting) → `Infrastructure/Services`

```csharp
// Domain/Services/TaxCalculator.cs
public class TaxCalculator
{
    public decimal CalculateTax(Address address, decimal subtotal)
    {
        var rate = GetTaxRate(address.State, address.Country);
        return subtotal * rate;
    }
}
```

Both `CreateOrder` and `GenerateInvoice` can use it without coupling to each other.

Before creating any cross-feature service, ask: could this logic live on a domain entity instead?
Most "shared business logic" is actually data access, domain logic that belongs on an entity, or premature abstraction.

If you need to trigger a side effect in another feature, I recommend using [**messaging and events**](event-driven-architecture-in-dotnet-with-rabbitmq).
Alternatively, the feature you want to call into can explore a facade ([**public API**](internal-vs-public-apis-in-modular-monoliths)) for that operation.

## When Duplication Is the Right Call

Sometimes "shared" code isn't actually shared.
It just looks that way.

```csharp
// Features/Orders/GetOrder
public record GetOrderResponse(Guid Id, decimal Total, string Status);

// Features/Orders/CreateOrder
public record CreateOrderResponse(Guid Id, decimal Total, string Status);
```

They're identical.
The temptation to create a `SharedOrderDto` is overwhelming.
Resist it.

Next week, `GetOrder` needs a tracking URL.
But `CreateOrder` happens before shipping, so there's no URL yet.
If you'd shared the DTO, you'd now have a nullable property that's confusingly empty half the time.

**Duplication is cheaper than the wrong abstraction.**

## The Practical Structure

Here's what a mature Vertical Slice Architecture project looks like:

```
📂 src
└──📂 Features
│   ├──📂 Orders
│   │   ├──📂 CreateOrder
│   │   ├──📂 UpdateOrder
│   │   └──📂 Shared          # Order-specific sharing
│   ├──📂 Customers
│   │   ├──📂 GetCustomer
│   │   └──📂 Shared          # Customer-specific sharing
│   └──📂 Invoices
│       └──📂 GenerateInvoice
└──📂 Domain
│   ├──📂 Entities
│   ├──📂 ValueObjects
│   └──📂 Services            # Cross-feature domain logic
└──📂 Infrastructure
│   ├──📂 Persistence
│   └──📂 Services
└──📂 Shared
    └──📂 Behaviors
```

- **Features** — Self-contained slices. Each owns its request/response models.
- **Features/[Name]/Shared** — Local sharing between related slices.
- **Domain** — Entities, value objects, and domain services. Shared business logic lives here.
- **Infrastructure** — Technical concerns.
- **Shared** — Cross-cutting behaviors only.

## The Rules

After building several systems this way, here's what I've landed on:

1. **Features own their request/response models.** No exceptions.

2. **Push business logic into the domain.** Entities and value objects are the best place to share business rules.

3. **Keep feature-family sharing local.** If only Order slices need it, keep it in `Features/Orders/Shared` (feel free to find a better name than `Shared`).

4. **Infrastructure is shared by default**. Database contexts, HTTP clients, logging. These are technical concerns.

5. **Apply the Rule of Three.** Don't extract until you have three real usages with identical, stable logic.

## Takeaway

Vertical Slice Architecture asks: "What feature does _this_ belong to?"

The shared code question is really asking: "What do I do when the answer is _multiple features_?"

Acknowledge that some concepts genuinely span features.
Give them a home based on their _nature_ (domain, infrastructure, or cross-cutting behavior).
Resist the urge to share everything just because you could.

The goal isn't zero duplication.
It's code that's easy to change when requirements change.

And requirements always change.

Thanks for reading.

And stay awesome!

---



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**Let's be honest: We've all written this code.**

It's Monday morning, you have a deadline, and you need to implement a user registration feature.
It's simple enough: save the user, send a welcome email, and track the signup in your analytics dashboard.

You write this:

```csharp
public class UserService(
        IUserRepository userRepository,
        IEmailService emailService,
        IAnalyticsService analyticsService)
{
    public async Task RegisterUser(string email, string password)
    {
        var user = new User(email, password);
        await userRepository.SaveAsync(user);

        // 1. Directly coupled to email service (external API)
        await emailService.SendWelcomeEmail(user.Email);

        // 2. Directly coupled to analytics (this could be an external API)
        await analyticsService.TrackUserRegistration(user.Id);

        // What if we need to add more features?
        // This method will keep growing...
    }
}
```

It looks clean. It's readable. It works on your machine.

But this method is a **ticking time bomb**.

It assumes the "Happy Path" is the _only_ path.
It assumes the network is reliable, the email provider is up, and the analytics API is fast.
In production, none of these are guaranteed.

Thinking further, I'm sure you can imagine similar code in your own projects.
It might not be this exact scenario, but the pattern is common: a single method that orchestrates multiple side effects in a linear fashion.

Let's break down why this code is dangerous and how we can refactor it into a robust, event-driven architecture.

## The Hidden Dangers of the "God Method"

There are three major issues hiding in those ten lines of code.

### 1. Temporal Coupling (Latency)

When a user clicks "Register," they have to wait for:

1.  The Database **+**
2.  The SMTP Server **+**
3.  The Analytics API

If your analytics provider is having a bad day and takes 3 seconds to respond, **your user waits 3 seconds**.
You are punishing your user for the slowness of a background system they don't even care about.

### 2. The Partial Failure State

This is the **most critical risk**. Imagine this scenario:

1.  `SaveAsync(user)` succeeds. The user is in the DB.
2.  `SendWelcomeEmail` succeeds. The user gets an email.
3.  `TrackUserRegistration` throws a `503 Service Unavailable`.

**What happens now?**
If you wrap this in a transaction and rollback, you have deleted the user from the DB... **but you already sent them a welcome email.**
The user tries to log in, but they don't exist.
Now what?

If you _don't_ rollback, you have a user in your system that is missing from your analytics.
You have data inconsistency.

### 3. Violation of Single Responsibility (SRP)

You might argue that because we are using interfaces (`IEmailService`), we are decoupled.
That is true for _implementation details_, but false for _orchestration_.

The `UserService` currently has two reasons to change:

1.  **Core Domain Logic:** "We now require a username in addition to email."
2.  **Notification Policy:** "Marketing wants to send an SMS in addition to the Email."

The `UserService` should strictly be responsible for the **state change** (creating the user).
It should not be responsible for **orchestrating the side effects** of that change.

## Level 1: Logical Decoupling with Domain Events

The first step to fixing this is to invert the control.
Instead of the `UserService` _commanding_ other services to do things, it should simply _announce_ that something happened.

We can use [**Domain Events**](how-to-use-domain-events-to-build-loosely-coupled-systems) to achieve this.

Here is the refactored `UserService`:

```csharp
public class UserService(
        IUserRepository userRepository,
        IDomainEventDispatcher dispatcher,
        IUnitOfWork unitOfWork)
{
    public async Task RegisterUser(string email, string password)
    {
        // 1. Create the User Entity
        var user = new User(email, password);

        // 2. Capture the side effect as an event object
        var userRegisteredEvent = new UserRegisteredEvent(user.Id, user.Email);

        // 3. Add the entity to the repository
        await userRepository.AddAsync(user);

        // 4. Dispatch the event (Assuming in-process dispatching here for simplicity)
        // Note: Handlers for Email and Analytics are now completely separate classes.
        await dispatcher.Dispatch(userRegisteredEvent);

        await unitOfWork.SaveChangesAsync();
    }
}
```

The `UserService` is now stable.
Adding a "Loyalty Points" feature later doesn't require touching this method.
You just add a new handler for the `UserRegisteredEvent`.

**However, we haven't solved the reliability problem yet.**
If the process crashes immediately after `Dispatch` but before `SaveChangesAsync` completes, we might send an email for a user that failed to save.
Or, if we save first and dispatch later, we might save the user but lose the event if the server crashes.

## Level 2: Reliability with the Outbox Pattern

To fix this, we need **Atomicity**.
Atomicity means that a set of operations either all succeed or all fail together.

We need to guarantee that if the `User` is saved, the `UserRegisteredEvent` is also saved.

Enter the [**Outbox Pattern**](implementing-the-outbox-pattern).

Instead of publishing the event immediately to a message bus, we save the event to an `OutboxMessages` table in the **same database transaction** as the user.

Here is the complete implementation logic:

```csharp
public async Task RegisterUser(string email, string password)
{
    // 1. Create the Domain Event
    var user = new User(email, password);
    var domainEvent = new UserRegisteredEvent(user.Id, user.Email);

    // 2. Open a Transaction
    using var transaction = dbContext.Database.BeginTransaction();

    try
    {
        // 3. Save the User to the Users Table
        dbContext.Users.Add(user);

        // 4. Serialize the Event and Save to Outbox Table
        var outboxMessage = new OutboxMessage
        {
            Id = Guid.NewGuid(),
            Type = nameof(UserRegisteredEvent),
            Content = JsonSerializer.Serialize(domainEvent),
            OccurredOn = DateTime.UtcNow,
            ProcessedOn = null // Null means it hasn't been handled yet
        };

        dbContext.OutboxMessages.Add(outboxMessage);

        // 5. Commit BOTH changes atomically
        await dbContext.SaveChangesAsync();
        await transaction.CommitAsync();
    }
    catch
    {
        await transaction.RollbackAsync();
        throw;
    }
}
```

Now, a [**background worker**](scheduling-background-jobs-with-quartz-net) (running in a separate process) polls the `OutboxMessages` table.
It picks up the message and publishes it to your message bus (RabbitMQ, Azure Service Bus, etc.).

If the email service is down, the background worker just retries later.
**We have achieved At-Least-Once delivery.**

## Level 3: Distributed Consistency with Sagas

The Outbox pattern is perfect for side effects (fire-and-forget actions like emails).
But what if the subsequent action is **mandatory**?

**Scenario:** When a user registers, we _must_ create a crypto-wallet for them in the `WalletService`.
If the wallet creation fails (e.g., due to regulations), we cannot allow the user to exist in our system.

We can't just "retry later" if the `WalletService` says "Fraud Detected."
We need to **undo** the user creation.

This is a distributed transaction, and we handle it with the [**Saga Pattern**](implementing-the-saga-pattern-with-masstransit).
A Saga coordinates a series of steps.
If one fails, it executes **Compensating Transactions** to undo the previous work.

Here is how the failure scenario looks when using a [**Choreography-based Saga**](orchestration-vs-choreography):

<Image
  src="blogs/mnw_169/saga_sequence_diagram.png"
  width={1406}
  height={1860}
  alt="A Saga Sequence Diagram showing UserService creating a user, WalletService attempting to create a wallet, failing, and UserService deleting the user as a compensation action."
/>

Here's the step-by-step breakdown of the flow:

1.  **UserService:** Creates User → Publishes `UserCreated`
2.  **WalletService:** Listens to `UserCreated` → Tries to create wallet
    - _Failure:_ Wallet creation fails
    - _Action:_ Publishes `WalletCreationFailed`
3.  **UserService:** Listens to `WalletCreationFailed` → **Deletes/Deactivates the User**

This ensures **Eventual Consistency**.
The system might be inconsistent for a few seconds (the user exists without a wallet),
but it will eventually settle into a valid state (the user is removed).

## Summary: A Heuristic for Decision Making

You don't need Sagas for everything.
Over-engineering is just as bad as tight coupling.
Use this simple rule of thumb:

1.  **Is it a simple notification?** (Email, Analytics, Cache Invalidation)
    - **Use Domain Events + Outbox.** It's okay if it happens 5 seconds later.
2.  **Is it a critical business dependency?** (Payments, Inventory, Account Status)
    - **Use a Saga.** If step B fails, step A must be reverted.

Coupling isn't just about code structure.
It's about understanding and managing **failure boundaries**.
If your Analytics Service goes down, it shouldn't prevent a user from registering.
Build your systems to survive the unhappy path.

Hope this was helpful.

See you next week.

---



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C# has always been a bit ceremony-heavy, and we all know it.
Even the simplest "Hello World" program traditionally needed a solution file, a project file,
and enough boilerplate to make you wonder if you should've just used a scripting language instead.

Well, Microsoft finally heard us.
With .NET 10, they've introduced [**file-based apps**](run-csharp-scripts-with-dotnet-run-app-no-project-files-needed).
And honestly, it's about time.

## What are File-based Apps?

The idea is simple: write your C# code in a single `.cs` file and run it directly. That's it.

No need to set up a whole project structure for that quick utility script you need to parse some CSV files or test an API endpoint.

You get to keep everything that makes C# great: the **type safety**, the **performance**, the **rich standard library**.
But now you can use it for those throwaway scripts where setting up a full project would've been overkill.
And yes, you can still **reference NuGet packages**, **reference other C# projects**, target specific SDKs,
and configure project properties, all from within that single file using special directives that start with `#:`.

The feature builds on **top-level statements** (remember those from C# 9?) and takes them to their logical conclusion.
If we're already letting people skip the class and `Main` method ceremony, why not let them skip the project ceremony too?

## Getting Started with File-based Apps

Let's say you want to quickly check what day of the week a specific date falls on.
Create a file called `date-checker.cs`:

```csharp
var targetDate = new DateTime(2025, 12, 31);
Console.WriteLine($"New Year's 2025 falls on a {targetDate.DayOfWeek}");
Console.WriteLine($"That's {(targetDate - DateTime.Today).Days} days from now");
```

Run it with:

```bash
dotnet run date-checker.cs
```

The first time you run this, the CLI does some behind-the-scenes magic.
It creates a virtual project, compiles your code, and caches everything.
Subsequent runs are nearly instant because it's smart enough to know when nothing has changed.

## Real-world Example: Quick Data Processing

Here's where things get interesting.
Say you need to quickly process some JSON data and generate a report.

Let's do something practical with `System.Text.Json` and `CsvHelper`:

```csharp {1}
#:package CsvHelper@33.1.0
using System.Text.Json;
using CsvHelper;
using System.Globalization;

var json = await File.ReadAllTextAsync("sales_data.json");
var sales = JsonSerializer.Deserialize<List<SaleRecord>>(json);

var topProducts = sales
    .GroupBy(s => s.Product)
    .Select(g => new {
        Product = g.Key,
        TotalRevenue = g.Sum(s => s.Amount),
        UnitsSold = g.Count()
    })
    .OrderByDescending(p => p.TotalRevenue)
    .Take(10);

using var writer = new StreamWriter("top_products.csv");
using var csv = new CsvWriter(writer, CultureInfo.InvariantCulture);
csv.WriteRecords(topProducts);

Console.WriteLine("Report generated! Check top_products.csv");

record SaleRecord(string Product, decimal Amount, DateTime Date);
```

Notice how we're mixing package references, async operations, LINQ, and record types, all in a single file that reads like a cohesive script.
This is the kind of thing you'd typically reach for Python for, but now you can stay in C# land.

## Building Something More Ambitious with Aspire

You can actually create an **Aspire AppHost** in a single file, which is pretty wild when you think about it:

```csharp
#:sdk Aspire.AppHost.Sdk@13.0.0
#:package Aspire.Hosting.AppHost@13.0.0

var builder = DistributedApplication.CreateBuilder(args);

var cache = builder.AddRedis("cache")
    .WithDataVolume();

var postgres = builder.AddPostgres("postgres")
    .WithDataVolume()
    .AddDatabase("tododb");

var todoApi = builder.AddProject<Projects.TodoApi>("api")
    .WithReference(cache)
    .WithReference(postgres);

builder.AddNpmApp("frontend", "../TodoApp")
    .WithReference(todoApi)
    .WithReference("api")
    .WithHttpEndpoint(env: "PORT")
    .WithExternalHttpEndpoints();

builder.Build().Run();
```

With the `#:sdk Aspire.AppHost.Sdk@13.0.0` directive, your single file becomes a full orchestrator for a distributed application.
You're defining infrastructure, wiring up dependencies, and setting up a complete development environment, all without creating a project file.
It's particularly useful when you're prototyping architectures or need to quickly spin up a test environment.

## Migrating to a Full Project

Eventually, some scripts outgrow their single-file roots.
Maybe you need to split things into multiple files, or maybe you want proper IDE support for debugging.
The transition is painless:

```bash
dotnet project convert MyUtility.cs
```

This generates a proper project structure while preserving all your package references and SDK choices.
Your code moves to `Program.cs`, and you get a `.csproj` that reflects all those `#:` directives you were using.

## Current Limitations

Right now, it's strictly single-file.
If you need **multiple files**, you'll have to [wait for .NET 11](https://github.com/dotnet/sdk/issues/48174) or convert to a full project.
Of course, you can still reference other projects or packages, but the main script has to be in one file.

The caching mechanism can occasionally get confused if you're rapidly iterating on package versions.
And while the IDE support is getting better, it's not quite at the same level as full projects yet, especially for IntelliSense with dynamically referenced packages.

But for what it's designed to do (make C# approachable for scripting scenarios), it works remarkably well.
You can use it for **build scripts**, **data migration one-offs**, quick API tests, or even teaching C# without overwhelming beginners with project structure.

## Where This Leaves Us

**File-based apps** feel like C# finally acknowledging that not every piece of code needs to be an enterprise application.
Sometimes you just need to parse a log file, sometimes you want to quickly test an algorithm,
and sometimes you're teaching someone programming and don't want to explain what a solution file is on day one.

The feature doesn't revolutionize C# development, but it **fills a gap** that's been annoying developers for years.
If you've been keeping a folder of scripts for quick tasks because C# felt too heavy, maybe it's time to give your favorite language another shot.

After all, the best language for a quick script is the one you already know, and now C# makes that argument a lot more compelling.

---



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Distributed systems are unreliable by nature.

I always recommend reading about the [Fallacies of Distributed Computing](https://en.wikipedia.org/wiki/Fallacies_of_distributed_computing) to understand the common pitfalls.

One of the key challenges is ensuring that messages are processed **exactly once**.
Theoretically, that's impossible to guarantee in most systems.
I won't dive into the [CAP theorem](https://en.wikipedia.org/wiki/CAP_theorem) or the
[Two Generals Problem](https://en.wikipedia.org/wiki/Two_Generals%27_Problem) here, but suffice to say:

- Messages can arrive out of order
- Messages can be duplicated
- Deliveries can be delayed

If you design your system assuming every message will be processed exactly once, you're setting yourself up for subtle data corruption.

But we can design our system to apply side effects **exactly once** using the [**Idempotent Consumer**](idempotent-consumer-handling-duplicate-messages) pattern.

Let's unpack what can go wrong, how brokers help with idempotency, and how you can build an idempotent consumer in .NET.

## What Can Go Wrong When Publishing

Let's say your service publishes an event when a new note is created:

```csharp
await publisher.PublishAsync(new NoteCreated(note.Id, note.Title, note.Content));
```

We don't have to worry about the specific implementation of `publisher` or the message broker here.
It could be RabbitMQ, SQS, Azure Service Bus, etc.

Now imagine:

- The publisher sends the message to the broker
- The broker stores it and sends an ACK
- **Network glitch**: the ACK never reaches the producer
- Producer times out and **retries** the publish
- The broker now has two `NoteCreated` events

From the producer's perspective, it "fixed" a timeout.
But from the consumer's perspective, it received two events for the same note creation.

<Image
  src="blogs/mnw_167/distributed_messaging_with_error.png"
  width={1991}
  height={1296}
  alt="Distributed messaging with network error causing duplicate messages."
/>

And that's just one failure path.
You can also get duplicates from:

- Broker redeliveries
- Consumer failures + retries

So even if you do everything "right" on the publisher, the **consumer still has to be defensive**.

## Publisher-Side Idempotency (Let the Broker Handle It)

Many message brokers already support idempotent publishing via message deduplication if you include a unique message ID.
[**Azure Service Bus**](messaging-made-easy-with-azure-service-bus), for instance, can detect duplicates and ignore re-publishes for the same message ID within a configured window.
[**Amazon SQS**](complete-guide-to-amazon-sqs-and-amazon-sns-with-masstransit) and other brokers also offer similar guarantees.

You don't need to reinvent this logic in your application.
The key is to assign each message a stable identifier that uniquely represents the logical event you're sending.

For example, when publishing a NoteCreated event:

```csharp {3}
var message = new NoteCreated(note.Id, note.Title, note.Content)
{
    MessageId = Guid.NewGuid() // or you can use note.Id
};

await publisher.PublishAsync(message);

```

If the network drops after you send the message, your app might retry.
But when the broker sees the same `MessageId`, it knows this is a duplicate and safely discards it.
You get deduplication without any custom tracking tables or extra state in your service.

This broker-level idempotency solves a large class of **producer-side issues**: network retries, transient failures, and duplicated publishes.

What it doesn't handle are **consumer retries**, which happen when messages are redelivered or your service crashes mid-processing.

That's where the idempotent consumer pattern comes in.

## Implementing an Idempotent Consumer in .NET

Here's an example of an idempotent consumer for a `NoteCreated` event:.

```csharp
internal sealed class NoteCreatedConsumer(
    TagsDbContext dbContext,
    HybridCache hybridCache,
    ILogger<Program> logger) : IConsumer<NoteCreated>
{
    public async Task ConsumeAsync(ConsumeContext<NoteCreated> context)
    {
        // 1. Check if we've already processed this message for this consumer
        if (await dbContext.MessageConsumers.AnyAsync(c =>
                c.MessageId == context.MessageId &&
                c.ConsumerName == nameof(NoteCreatedConsumer)))
        {
            return;
        }

        var request = new AnalyzeNoteRequest(
            context.Message.NoteId,
            context.Message.Title,
            context.Message.Content);

        try
        {
            using var transaction = await dbContext.Database.BeginTransactionAsync();

            // 2. Deterministic processing: derive tags from note content
            var tags = AnalyzeContentForTags(request.Title, request.Content);

            // 3. Persist tags
            var tagEntities = tags.Select(ProjectToTagEntity(request.NoteId)).ToList();
            dbContext.Tags.AddRange(tagEntities);

            // 4. Record that this message was processed
            dbContext.MessageConsumers.Add(new MessageConsumer
            {
                MessageId = context.MessageId,
                ConsumerName = nameof(NoteCreatedConsumer),
                ConsumedAtUtc = DateTime.UtcNow
            });

            await dbContext.SaveChangesAsync();
            await transaction.CommitAsync();

            // 5. Update cache
            await CacheNoteTags(request, tags);
        }
        catch (Exception ex)
        {
            logger.LogError(ex, "Error analyzing note {NoteId}", request.NoteId);
            throw;
        }
    }
}
```

This is a typical idempotent consumer with a few important details.

**1. The Idempotency Key**

```csharp
if (await dbContext.MessageConsumers.AnyAsync(c =>
        c.MessageId == context.MessageId &&
        c.ConsumerName == nameof(NoteCreatedConsumer)))
{
    return;
}
```

You use:

- `MessageId` from the transport (`context.MessageId`)
- `ConsumerName` (so multiple consumers can safely process the same message)

If a duplicate message arrives, you short-circuit and do nothing.

What's also important here is having a **unique constraint** on `(MessageId, ConsumerName)` in the `MessageConsumers` table to prevent
[**race conditions**](solving-race-conditions-with-ef-core-optimistic-locking).
So even if you have concurrent processing of the same message, only one will succeed in inserting the record.

**2. Atomic Side Effects + Idempotency Record**

The processing and storing the message consumer record happen **in the same transaction**:

```csharp {1,10}
using var transaction = await dbContext.Database.BeginTransactionAsync();

// write tags
dbContext.Tags.AddRange(tagEntities);

// write message-consumer record
dbContext.MessageConsumers.Add(new MessageConsumer { ... });

await dbContext.SaveChangesAsync();
await transaction.CommitAsync();
```

Why this matters:

- If processing fails, there is no entry in `MessageConsumers`, so the message can be retried
- If processing succeeds, both the tags and the `MessageConsumer` row are committed together
- You never end up in a state where the work is done but the message is not marked as processed, or vice versa

This is the core of idempotency:

> Do this work exactly once per message ID, even under retries.

**3. Handling At-Least-Once Delivery**

Most realistic setups are **at-least-once**:

- Consumer processes message
- ACK fails / times out
- Broker redelivers
- Your code runs again

With this pattern, the second run hits the `MessageConsumers` table and returns early.

No duplicate side effects.

This works, except for one caveat...

## Deterministic vs Non-Deterministic Handlers

What happens when your handler calls something _outside_ the database?
An email API, a payment gateway, or a background job queue?

These are all common side effects that need to be idempotent too.

Those calls sit outside your transaction boundary.
Your database might commit successfully, but if the network hiccups before the external service responds, you can't tell if the action happened or not.
On retry, your consumer might send another email or charge the credit card twice.

You've now crossed into the messy territory of non-deterministic handlers: operations that can't be repeated safely.

There are two main strategies to deal with this.

**1. Use an Idempotency Key in the External Call**

If the external service supports it, pass a stable identifier, like the message's `MessageId` with every request.
Many APIs, including payment processors and email platforms, let you specify an idempotency key header.
The service ensures that identical requests with the same key only execute once.

For example:

```csharp {6}
await emailService.SendAsync(new SendEmailRequest
{
    To = user.Email,
    Subject = "Welcome!",
    Body = "Thanks for signing up.",
    IdempotencyKey = context.MessageId
});

```

Even if the request is retried, the provider will recognize the key and skip the duplicate.
This is the simplest and most reliable approach, if your external dependency supports it.

**2. Store the Intent Locally**

If the external service doesn't support idempotency keys, you can simulate it.
Store a **record of the intended action** in your database before calling the external system.
For example, create a `PendingEmails` table that records which messages should be sent, keyed by message ID or user ID.

A background process can later read these pending records and perform the action once.
This makes the process deterministic, but at the cost of more complexity, extra tables, and background workers.
It's often overengineering unless the side effect is critical or irreversible, like payments or account provisioning.

The trade-off comes down to confidence.
If repeating the action has real consequences, introduce idempotency explicitly.
If not, retrying the operation might be acceptable.

## When Idempotent Consumer Isn't Needed

Not every consumer needs the overhead of idempotency checks.
If your operation is already naturally idempotent, you can often skip the extra table and transaction logic.

Updating a projection, setting a status flag, or refreshing a cache are all examples of deterministic actions that can safely run multiple times.
For instance, "set user's status to Active" or "rebuild the read model" are operations that overwrite state rather than append to it.

Some handlers also use precondition checks to avoid duplication.
If the handler updates an entity, it can first check whether that entity is already in the desired state and return early.
That simple guard clause can be enough.

Don't blindly apply the **Idempotent Consumer** pattern everywhere.
Apply it where it protects you from real harm, where duplicate processing causes financial or data inconsistencies.

For everything else, **simpler is better**.

## Takeaway

Distributed systems are unpredictable.
Retries, duplicates, and partial failures are part of normal operation.
You can't avoid them, but you can design your system so they don't impact you as much.

Use your broker's built-in **message deduplication** to prevent duplicates from the producer side.
For the consumer side, apply the **Idempotent Consumer** pattern to ensure side effects happen once, even under retries.
Keep the record of processed messages and the actual side effect in the same transaction.

Not every message handler needs this.
If your consumer is naturally idempotent or can short-circuit with a simple precondition, skip the extra complexity.
But for anything that modifies persistent state or calls external systems, idempotency isn't optional, it's the only way to keep your system consistent.

Build your consumers to tolerate retries.
And your distributed system will be that much more reliable.
The interesting part is that once you understand this principle, you start seeing it everywhere in real-world systems.

If you want to dive deeper into messaging patterns and learn how this is implemented in a production-grade system,
check out my [**Modular Monolith Architecture**](/modular-monolith-architecture) course.
We'll build a full-featured application with distributed messaging, CQRS, and DDD patterns from scratch.

Hope this was helpful.

---



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If you've ever worked with databases, you know about `LEFT JOIN` (and conversely `RIGHT JOIN`).
It's one of those things we use often, if not all the time.
But in Entity Framework Core, doing a left join has always been... well, a bit of a pain.

I like joins that read like what they do.
Unfortunately, until now, LINQ didn't have a straightforward way to express left/right joins.
You had to jump through hoops with `GroupJoin` and `DefaultIfEmpty`, which made the code harder to read and maintain.

But **.NET 10** finally fixes this with the brand new `LeftJoin` and `RightJoin` methods.

## What's a LEFT JOIN (in plain words)?

A **LEFT JOIN** returns **all rows from the left side** and the **matching rows from the right side**.
If there's **no match**, the right side is **null**.
Why we use it: to keep "owners" even when they have **no related rows** (e.g., show all products even if some don't have a review).

<figure className="figure-center bordered">
  <Image
    src="blogs/mnw_166/left_join.gif"
    width={988}
    height={554}
    alt="Animated left join."
  />
  <figcaption>
    Source: [Data
    School](https://dataschool.com/how-to-teach-people-sql/left-right-join-animated/)
  </figcaption>
</figure>

## The Old Way (`GroupJoin` + `DefaultIfEmpty`)

Before .NET 10, a left join in LINQ needed a **group join** (`GroupJoin`), then `DefaultIfEmpty` to keep left rows with no match.
It worked, but the intent was buried in noise.

There are two ways you could write it: **query syntax** and **method syntax**.

### Query syntax

```csharp
var query =
    from product in dbContext.Products
    join review in dbContext.Reviews on product.Id equals review.ProductId into reviewGroup
    from subReview in reviewGroup.DefaultIfEmpty()
    orderby product.Id, subReview.Id
    select new
    {
        ProductId = product.Id,
        product.Name,
        product.Price,
        ReviewId = (int?)subReview.Id ?? 0,
        Rating = (int?)subReview.Rating ?? 0,
        Comment = subReview.Comment ?? "N/A"
    };
```

Here's the SQL generated by EF Core for the above query:

```sql
SELECT
    p."Id" AS "ProductId",
    p."Name",
    p."Price",
    COALESCE(r."Id", 0) AS "ReviewId",
    COALESCE(r."Rating", 0) AS "Rating",
    COALESCE(r."Comment", 'N/A') AS "Comment"
FROM "Products" AS p
LEFT JOIN "Reviews" AS r ON p."Id" = r."ProductId"
ORDER BY p."Id", COALESCE(r."Id", 0)
```

### Method syntax

```csharp
var query = dbContext.Products
    .GroupJoin(
        dbContext.Reviews,
        product => product.Id,
        review => review.ProductId,
        (product, reviewList) => new { product, subgroup = reviewList })
    .SelectMany(
        joinedSet => joinedSet.subgroup.DefaultIfEmpty(),
        (joinedSet, review) => new
        {
            ProductId = joinedSet.product.Id,
            joinedSet.product.Name,
            joinedSet.product.Price,
            ReviewId = (int?)review!.Id ?? 0,
            Rating = (int?)review!.Rating ?? 0,
            Comment = review!.Comment ?? "N/A"
        })
    .OrderBy(result => result.ProductId)
    .ThenBy(result => result.ReviewId);
```

Why this works: `GroupJoin` matches rows, `DefaultIfEmpty` inserts a single **default** (null) when no matches exist, so the left row still appears.
We then **flatten** with `SelectMany`.

I think we can all agree that this is way too verbose for something as common as a left join.

## The New Way in EF 10: `LeftJoin`

Now we can write what we mean.
`LeftJoin` is **first-class LINQ** and EF Core translates it to a **LEFT JOIN** in SQL.

```csharp
var query = dbContext.Products
    .LeftJoin(
        dbContext.Reviews,
        product => product.Id,
        review => review.ProductId,
        (product, review) => new
        {
            ProductId = product.Id,
            product.Name,
            product.Price,
            ReviewId = (int?)review.Id ?? 0,
            Rating = (int?)review.Rating ?? 0,
            Comment = review.Comment ?? "N/A"
        })
    .OrderBy(x => x.ProductId)
    .ThenBy(x => x.ReviewId)
```

The generated SQL is identical to the previous example.

Why this is better:

- **Intent is clear**: you see `LeftJoin`, you know what to expect.
- **Less code**, fewer moving parts (no `GroupJoin`, no `DefaultIfEmpty`, no `SelectMany`).
- **Same result**: all products kept, reviews may be null.

<div className="note-panel">
  **Note**: At the time of writing this article, C# **query syntax** (`from …
  select …`) doesn't have a `left join` or `right join` keyword yet. You should
  use the method syntax shown above.
</div>

## Also New: `RightJoin`

`RightJoin` keeps **all rows from the right side** and only matching rows from the left.
EF Core translates it to **RIGHT JOIN**.
It's handy when the "must keep" side is the **second** sequence.

Conceptually:

```csharp
var query = dbContext.Reviews
    .RightJoin(
        dbContext.Products,
        review => review.ProductId,
        product => product.Id,
        (review, product) => new
        {
            ProductId = product.Id,
            product.Name,
            product.Price,
            ReviewId = (int?)review.Id ?? 0,
            Rating = (int?)review.Rating ?? 0,
            Comment = review.Comment ?? "N/A"
        });
```

Why use `RightJoin`: when your reporting starts from **Reviews** (keep all), and bring in matching **Products** if they exist.

Here's the generated SQL:

```sql
SELECT
    p."Id" AS "ProductId",
    p."Name",
    p."Price",
    COALESCE(r."Id", 0) AS "ReviewId",
    COALESCE(r."Rating", 0) AS "Rating",
    COALESCE(r."Comment", 'N/A') AS "Comment"
FROM "Reviews" AS r
RIGHT JOIN "Products" AS p ON r."ProductId" = p."Id"
```

## Wrapping Up

Think about how often you need left joins.
Showing all users with optional profile settings.
All products with optional reviews.
All orders with optional shipping info.
It's everywhere!

Before, developers would sometimes skip the proper left join and do two separate queries instead.
Or worse, they'd use an inner join and miss data.
Now there's no excuse - it's just as easy as any other LINQ method.

A few quick tips when writing LINQ queries:

- In the projection, guard nullable side: `review.Comment ?? "N/A"`
- Keep projections **small** to avoid pulling more columns than needed
- Add indexes on the **join keys** for better query plans

That's it.
With `LeftJoin` and `RightJoin`, the code finally matches the mental model.

See you next Saturday.

---



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About six or seven years ago, I went through an interview for a mid-level .NET role.

One of the questions has stayed with me ever since:

> A user clicks a button on the UI to generate an Excel or PDF report.
> The report generation takes around five minutes (time can be arbitrary).
> The user has to wait for it to finish.
> How would you optimize this flow?

At the time, I focused on what I knew best: **performance**.
I started thinking about how to make the report generation faster.
Maybe I could optimize the SQL queries, reduce data transformations, or [**cache**](caching-in-aspnetcore-improving-application-performance) parts of the result.
If I could get the process down from five minutes to one, that felt like a big win.

But even if I made it five times faster, the user still had to wait.
If the browser crashed, they lost everything.
If the network dropped, the process stopped.
If they closed the tab, all progress was gone.

It wasn't really a performance issue at all, it was a **design issue**.

## What I Missed Back Then

Looking back, I realize I was **stuck** in the mindset of "make the code faster".
Not that there's anything wrong with that, **performance optimization is a valuable skill**.
What I didn't see immediately was the **bigger problem**.
The app was doing all this work **synchronously**, holding the user hostage until it finished.
I did eventually figure it out, with a few nudges from the interviewer.

<Image src="blogs/mnw_165/sync_reporting_flow.png" width={2258} height={1201} />

The better question wasn't _"How can I make this faster?"_

It was _"Why is the user waiting in the first place?"_

If something takes minutes (or hours, days) to complete, it shouldn't block the user.
It should happen in the **background**, out of the **main request flow**, while the user moves on with their work.

Still, don't forget to optimize the code itself.
Database queries, data processing, and file generation all matter.
Maybe there's a missing index, an inefficient loop, or a better library for creating Excel files.
But those optimizations are just part of the solution, not the whole picture.

## How I'd Solve It Today

Today, I'd still start with the same UI button.
The user clicks "Generate Report," but instead of waiting, the **backend accepts the request**,
saves it somewhere (maybe as a job record in a database), and **returns right away**.
This is the essence of building [**asynchronous APIs**](building-async-apis-in-aspnetcore-the-right-way).
The job is then picked up by a background worker.

The worker can be a [**hosted service**](running-background-tasks-in-asp-net-core),
a [**Quartz job**](scheduling-background-jobs-with-quartz-in-dotnet-advanced-concepts),
or even an [**AWS Lambda Function**](building-fast-serverless-apis-with-minimal-apis-on-aws-lambda) triggered by a queue message.
It handles the heavy lifting: pulling the data, building the file, and uploading it to storage like S3 or Azure Blob.

Once the report is ready, the worker updates the job status to "completed" and notifies the user.
That could be an email with a download link or a real-time SignalR message that shows up in the app.
The link points to the stored report, served securely from the backend.

<Image
  src="blogs/mnw_165/async_reporting_flow.png"
  width={3566}
  height={2033}
/>

Now, the user isn't waiting on a long-running HTTP request.
The server isn't holding open connections for minutes.
**If something fails, it can be retried automatically**.
You also have the option to **track progress** or **cancel** the job if needed.
And if a hundred users request reports at once, the system can scale without locking up.

The experience feels faster, even if the actual report generation time hasn't changed.
Because in the end, users don't care about performance metrics, they care about responsiveness.

## Why I Still Use This Question

A few years later, I started using this exact question when interviewing other developers.
Not to trick anyone, but because it reveals how people think.

Some candidates go straight to optimizing code and queries, just like I did back then.
This is a good sign that they know their way around performance tuning.
I can proceed with further technical questions around **algorithms**, **data structures**, or **database optimization**.

Others pause for a moment and start thinking about user experience, **background processing**, and **fault tolerance**.
That's when the real conversation begins: queues, retries, notifications, secure file sharing, etc.
There are so many ways you can spin off this one scenario into a broader system design discussion.

There's **no single right answer**.
But there's a big difference between someone who focuses only on _code_ and someone who can design a _scalable system_.

## The Lesson

When I first heard this question, I thought about making the code faster.
Now I think about making the experience better.

Optimizing a query or loop can help, but it doesn't fix waiting, failures, or scalability.
If many users start the same report at once, a synchronous design breaks down fast.
An asynchronous flow keeps the system responsive and resilient, no matter the load.

That shift from optimizing functions to designing scalable systems is the difference between a good developer and a great one.

If you want to go deeper into building systems that scale, my [**Clean Architecture course**](/pragmatic-clean-architecture) walks you through exactly that.
You'll learn how to structure applications, separate concerns, and design systems that grow without breaking.

Hope this was helpful.

See you next week.

---



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Starting a new .NET project is always exciting.
But it's also easy to skip the groundwork that makes a project scalable and maintainable.

Before you write your first line of business logic, there are a few key setup steps that make your life (and your teammates) much easier later on.

Here's how I usually set up a new .NET project in **six simple steps**.

## 1. Enforce a Consistent Code Style

The first thing I add is an `.editorconfig` file.

This file ensures everyone on your team uses the same formatting and naming conventions, reducing inconsistent indents or random naming rules.

You can create one directly in Visual Studio:

<div class="centered">
  <Image src="blogs/mnw_164/add_editorconfig.png" width={654} height={190} />
</div>

The default configuration is a great start.
But you can customize it further to fit your team's preferences.

Place it at the **solution root** so all projects follow the same rules.
You can still override specific settings in subfolders if needed by placing an `.editorconfig` file there.

Here are two sample `.editorconfig` files you can use:

- [From the .NET runtime repo](https://github.com/dotnet/runtime/blob/main/.editorconfig)
- [Created by me for general .NET projects](https://gist.github.com/m-jovanovic/417b7d0a641d7dd7d1972550fba298db)

## 2. Centralize Build Configuration

Next, I add a `Directory.Build.props` file to the solution root.
This file lets you define build settings that apply to every project in the solution.

Here's an example:

```xml
<Project>
  <PropertyGroup>
    <TargetFramework>net10.0</TargetFramework>
    <ImplicitUsings>enable</ImplicitUsings>
    <Nullable>enable</Nullable>
    <TreatWarningsAsErrors>true</TreatWarningsAsErrors>
  </PropertyGroup>
</Project>
```

This keeps your `.csproj` files clean and consistent, since there's no need to repeat these properties in every project.

If you later want to enable static analyzers or tweak build options, you can do it once here.

What's cool about this is your `.csproj` files become basically empty, with only NuGet package references most of the time.

## 3. Centralize Package Management

As your solution grows, managing NuGet package versions across multiple projects gets painful.

That's where [**central package management**](central-package-management-in-net-simplify-nuget-dependencies) helps.

Create a file named `Directory.Packages.props` at the root:

```xml
<Project>
  <PropertyGroup>
    <ManagePackageVersionsCentrally>true</ManagePackageVersionsCentrally>
  </PropertyGroup>

  <ItemGroup>
    <PackageVersion Include="Microsoft.AspNetCore.OpenApi" Version="10.0.0" />
    <PackageVersion Include="SonarAnalyzer.CSharp" Version="10.15.0.120848" />
  </ItemGroup>
</Project>
```

Now, when you reference a NuGet package in your project, you don't specify the version.
You can only use the package name like this:

```xml
<PackageReference Include="Microsoft.AspNetCore.OpenApi" />
```

All versioning is handled centrally.
This makes dependency upgrades trivial and avoids version drift between projects.

You can still override versions in individual projects if needed.

## 4. Add Static Code Analysis

[**Static code analysis**](improving-code-quality-in-csharp-with-static-code-analysis) helps catch potential bugs and maintain code quality.
.NET has a set of built-in analyzers, but I like to add **SonarAnalyzer.CSharp** for more comprehensive checks.

Let's install **SonarAnalyzer.CSharp** to catch potential code issues early:

```powershell
Install-Package SonarAnalyzer.CSharp
```

Add it as a global package reference inside your `Directory.Build.props`:

```xml
<ItemGroup>
  <PackageReference Include="SonarAnalyzer.CSharp" />
</ItemGroup>
```

Combine this with:

```xml
<Project>
  <PropertyGroup>
    <TreatWarningsAsErrors>true</TreatWarningsAsErrors>
    <AnalysisLevel>latest</AnalysisLevel>
    <AnalysisMode>All</AnalysisMode>
    <CodeAnalysisTreatWarningsAsErrors>true</CodeAnalysisTreatWarningsAsErrors>
    <EnforceCodeStyleInBuild>true</EnforceCodeStyleInBuild>
  </PropertyGroup>
</Project>
```

…and your build will fail on serious code quality issues.
This can be a great safety net.

But it can also be noisy at first.
If some rules don't fit your context, you can adjust or suppress them in `.editorconfig` by setting the rule severity to `none`.

## 5. Set Up Local Orchestration

For a consistent local environment across your team, you'll want container orchestration.

You have two main options:

**Option 1: Docker Compose**

Add **Docker Compose support** in Visual Studio.
It will scaffold a `docker-compose.yml` file where you can define services like:

```yaml
services:
  webapi:
    build: .
  postgres:
    image: postgres:18
    environment:
      POSTGRES_PASSWORD: password
```

This lets every developer spin up the same stack locally with one command.

**Option 2: .NET Aspire**

[**.NET Aspire**](dotnet-aspire-a-game-changer-for-cloud-native-development) takes orchestration a step further.
It provides [**service discovery**](how-dotnet-aspire-simplifies-service-discovery),
[**telemetry**](introduction-to-distributed-tracing-with-opentelemetry-in-dotnet), and streamlined configuration, all integrated with your .NET projects.
It's become a **personal favorite of mine**.

You can add a .NET project and a Postgres resource with a few lines of code:

```csharp
var postgres = builder.AddPostgres("demo-db");

builder.AddProject<WebApi>("webapi")
       .WithReference(postgres)
       .WaitFor(postgres);

builder.Build().Run();
```

Aspire still uses Docker under the hood but provides a richer developer experience.

Whether you pick Docker Compose or Aspire, the goal is the same: a repeatable, reliable local setup that works the same on every machine.

## 6. Automate Builds with CI

Finally, I set up a simple [**GitHub Actions**](how-to-build-ci-cd-pipeline-with-github-actions-and-dotnet) workflow to validate each commit.

Example `.github/workflows/build.yml`:

```yaml
name: Build

on:
  push:
    # Filter to only run on main branch
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-dotnet@v5
        with:
          dotnet-version: 10.0.x
      - run: dotnet restore
      - run: dotnet build --no-restore --configuration Release
      - run: dotnet test --no-build --configuration Release
```

This ensures your project always builds and passes tests, and it catches issues before they reach production.
If the CI build fails, you know something's wrong right away.

When it comes to testing, I highly recommend exploring:

- [**Architecture testing**](shift-left-with-architecture-testing-in-dotnet) to enforce architectural rules in your codebase
- [**Integration testing with Testcontainers**](testcontainers-integration-testing-using-docker-in-dotnet)
  to spin up real dependencies in Docker during tests (you can run this locally and in CI)

This will give you confidence that your code works as expected in an (as close as possible) production-like environment.

## Wrapping Up

That's a wrap.
Your **new .NET project** is now set up with:

- consistent code style
- centralized build and package management
- code quality enforcement
- reproducible local orchestration
- continuous integration

These small setup steps save countless hours down the road and keep your codebase clean, predictable, and ready to scale.

Once your project setup is solid, the next step is designing scalable boundaries.
In my [**Modular Monolith Architecture**](/modular-monolith-architecture) course, I show how to grow a .NET application without turning it into a tangled mess,
through clear module boundaries, messaging, and domain isolation.

If you're looking for a practical walkthrough of these steps, check out [**this video**](https://youtu.be/QRgtcbxJlo0).

Thanks for reading.

And stay awesome!

---



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Have you ever wondered if you could host a tiny .NET API without running servers 24/7?
You can!

AWS Lambda lets you run code on-demand, and with the [Amazon.Lambda.AspNetCoreServer.Hosting](https://www.nuget.org/packages/Amazon.Lambda.AspNetCoreServer.Hosting) library,
you can plug an ASP.NET Core [Minimal APIs](https://learn.microsoft.com/en-us/aspnet/core/fundamentals/minimal-apis) straight into [AWS Lambda](https://aws.amazon.com/lambda/).

In this article we'll set up a minimal API, deploy it, and discuss how it performs.

Don't worry if you're new to serverless, I'll keep everything simple enough to follow along.

## Getting Your Minimal APIs Lambda-Ready

Let's start with the basics. You need just three things to turn your Minimal API into a Lambda function.

First, create your API:

```bash
dotnet new webapi -n MyLambdaApi
cd MyLambdaApi
```

Second, add Amazon's hosting package:

```powershell
Install-Package Amazon.Lambda.AspNetCoreServer.Hosting
```

Third, add one line to your `Program.cs`:

```csharp {4}
var builder = WebApplication.CreateBuilder(args);

// This line does all the Lambda magic
builder.Services.AddAWSLambdaHosting(LambdaEventSource.HttpApi);

var app = builder.Build();

app.MapGet("/", () => "Hello from Lambda!");

app.Run();
```

That's it.
Your API now runs both locally (for testing) and in Lambda (for production).
When you run locally, it uses [Kestrel](https://learn.microsoft.com/en-us/aspnet/core/fundamentals/servers/kestrel) like normal.
When you deploy the app to AWS, Lambda takes over.

## Ship It to AWS

You'll need the [Lambda tools](https://github.com/aws/aws-extensions-for-dotnet-cli) installed:

```bash
dotnet tool install -g Amazon.Lambda.Tools
```

Then deploy with one command:

```bash
dotnet lambda deploy-function
```

The tool asks you a few questions (like the function name, which IAM role to use).
Pick the defaults if you're just testing.
In a minute or two, your API is live with a URL like: `https://[abc123xyz].lambda-url.[region-name].on.aws/`.

From the AWS Management Console, you can find your function with its URL and all the other details.

<div class="bordered">
  <Image
    src="blogs/mnw_163/aws_console_lambda_function.png"
    width={1906}
    height={883}
  />
</div>

## Measuring Cold Starts

Here's where things become interesting (and problematic).
Lambda functions "go to sleep" when nobody uses them.
Waking them up takes time, this is the famous **"cold start" problem**.

I ran some simple tests with a basic Minimal API:

- First request (cold): 2,153 ms
- Second request (warm): 154 ms
- Third request (warm): 143 ms
- After 10 minutes idle (cold again): 2,074 ms

As you can see, the first request is slow due to the cold start.
Subsequent requests are much faster, around 150 ms.
After 10 minutes of inactivity, the function goes cold again, and the next request takes over 2 seconds.
There's an optimization feature called [SnapStart](https://docs.aws.amazon.com/lambda/latest/dg/snapstart.html) that can help reduce cold start times,
but it has its own tradeoffs and isn't always suitable for every application.

Another thing to note is that I'm in Europe, and my Lambda function is in the US East (N. Virginia) region.
This adds network latency, so your results may vary based on your location and the region you choose.

Even when warm, the latency is higher than a typical server-hosted API.
This may be acceptable for low-traffic or non-critical endpoints, but it's something to consider.

Let's move beyond a "Hello World" example to something more realistic.

## CRUD Operations Benchmark

To see how a simple API performs in the real world, I built a tiny CRUD app that creates a record, fetches it, updates it and then deletes it.
First I ran each operation once to see the raw latency.
Then I used a load-testing tool with 100 virtual users (VUs) to measure average latency under a bit more stress.

```csharp
// POST /products - Create new product
app.MapPost("/products", async (CreateProductRequest request, NpgsqlDataSource dataSource) =>
{
    const string sql =
        """
        INSERT INTO Products (Name, Description, Price, CreatedAt)
        VALUES (@Name, @Description, @Price, @CreatedAt)
        RETURNING Id, Name, Description, Price, CreatedAt
        """;

    await using var connection = await dataSource.OpenConnectionAsync();
    var product = await connection.QueryFirstAsync<Product>(sql, new
    {
        request.Name,
        request.Description,
        request.Price,
        CreatedAt = DateTime.UtcNow
    });

    return Results.Created(
        $"/products/{product.Id}",
        new ProductResponse(
            product.Id,
            product.Name,
            product.Description,
            product.Price,
            product.CreatedAt));
});
// Other endpoints omitted for brevity:
// - GET /products/{id} - Get product by ID
// - PUT /products/{id} - Update product
// - DELETE /products/{id} - Delete product
```

The application uses .NET 8, [Npgsql](https://www.npgsql.org/) and [Dapper](https://www.learndapper.com/)
to interact with a PostgreSQL database running on [Amazon RDS](https://aws.amazon.com/rds/).
You can find the source code for this example (and the previous one) in [this repository](https://github.com/m-jovanovic/minimal-apis-on-lambda).

Here are the results:

**Single-call latency**

```text
| Operation  | Latency (ms) | Notes                           |
| ---------- | ------------ | ------------------------------- |
| Create     |     537      | Cold start plus object creation |
| Read       |     134      | Simple GET of the new record    |
| Update     |     140      | Changing one property           |
| Delete     |     167      | Removing the record             |
```

The create call took half a second because it included a cold start and initialization overhead.
Once the function was warmed up, the other operations completed in under two hundred milliseconds.

**Load test with 100 virtual users**

During the load test, I simulated 100 clients hitting the API at once.
AWS automatically scaled the Lambda function to handle the traffic, and average latencies dropped because the functions were already warm.
Here are the averages:

- **CREATE avg**: 129 ms
- **READ avg**: 132 ms
- **UPDATE avg**: 152 ms
- **DELETE avg**: 144 ms

These numbers show that once your function is up and running, Lambda can respond quite quickly even when many users are making requests.
Of course, actual times will vary depending on what your API does and how it stores data.

Remember that each of these operations involves network calls to the database, which adds latency.
Overall, I don't find these numbers bad for a serverless setup.

## Summary

Hosting minimal APIs in AWS Lambda is **surprisingly straightforward**.
With one library and a single method call, your ASP.NET Core code can run "without servers".
You pay only for the compute you use.
Also, the AWS Lambda free tier includes one million free requests per month, which is great for testing and light usage.

However, there are tradeoffs.
Because of cold starts and the overhead of starting the .NET runtime, latency isn't as low as hosting on a dedicated server.
If your API is mission-critical or requires sub-100 ms responses at all times, you may need to look at provisioned concurrency, containers, or a traditional host.

Lambda shines for small, [**event-driven**](event-driven-architecture-in-dotnet-with-rabbitmq) or intermittent workloads that don't justify a full-time server.
But it's less suitable for latency-sensitive or heavy, long-running applications.
For occasional or low-traffic API endpoints, though, **Lambda** offers a **cost-effective** and **simple** option.

That's all for today.

See you next week.

---



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You're building a .NET application with [EF Core](https://learn.microsoft.com/en-us/ef/core/).
Most of your queries work fine with [**LINQ**](why-i-write-tall-linq-queries), but now you're hitting scenarios where you need something more.

Maybe you have a complex report that joins five tables with aggregations and window functions.
Your LINQ query generates SQL that's slower than you'd like, and you know you could write better SQL by hand.

Or maybe you need to update inventory with proper locking to prevent race conditions.
You could manage [**transactions**](working-with-transactions-in-ef-core) and explicit locks in C#, but it feels like you're fighting the framework.

Here's what usually happens: you search for "EF Core stored procedures" and find conflicting advice.
Some articles say avoid raw SQL at all costs.
Others suggest abandoning EF entirely and writing ADO.NET.
Neither feels right.

Actually, **EF Core works great with database functions and procedures**.
You get the database's power for what it does best, and EF's convenience for everything else.
Let me show you how this actually works.

I'll use [PostgreSQL](https://www.postgresql.org/) for examples, but the same principles apply to SQL Server and other relational databases.

## When Should You Even Use Raw SQL?

Let's be honest: most of the time, **LINQ is fine**.
EF Core translates your C# into decent SQL, and you get type safety and refactoring support.

But there are times when raw SQL makes more sense:

**You need performance you can't get from LINQ**.
Complex aggregations with multiple joins, [window functions](https://www.postgresql.org/docs/current/tutorial-window.html), or reporting queries often run faster when written directly in SQL.
You can test and tune the query in your database tool before bringing it into your code.

**You're using database-specific features**.
PostgreSQL has powerful capabilities like [full-text search](https://www.postgresql.org/docs/current/textsearch.html), JSON operators,
and [common table expressions (CTEs)](https://www.postgresql.org/docs/current/queries-with.html) that don't always have clean LINQ equivalents.
Sometimes the straightest path is just writing the SQL.

**You have existing database logic**.
If your database already has stored procedures and functions (maybe from a [**legacy system**](what-rewriting-a-40-year-old-project-taught-me-about-software-development)),
calling them directly beats rewriting everything in C#.

**You need atomic operations with proper locking**.
A stored procedure that coordinates multiple updates with `FOR UPDATE` locks ([**here's a good use case**](scaling-the-outbox-pattern))
is simpler and safer than trying to manage that from application code.

**You want to reduce round trips**.
One function call that aggregates data from five tables is more efficient than five separate LINQ queries.

Now let's see how to actually do this.

## Example 1: Simple Scalar Function

Here's a straightforward function that tells you how many tickets are left:

```sql
CREATE OR REPLACE FUNCTION ticketing.tickets_left(p_ticket_type_id uuid)
RETURNS numeric
LANGUAGE sql
AS $$
  SELECT tt.available_quantity
  FROM ticketing.ticket_types tt
  WHERE tt.id = p_ticket_type_id
$$;
```

Nothing fancy, just a query wrapped in a function.

Calling it from EF Core is straightforward:

```csharp {6}
app.MapGet("ticket-types/{ticketTypeId}/available-quantity",
async (Guid ticketTypeId, EventManagementContext dbContext) =>
{
    var result = await dbContext.Database.SqlQuery<int>(
            $"""
             SELECT ticketing.tickets_left({ticketTypeId}) AS "Value"
             """)
        .FirstAsync();

    return Results.Ok(result);
});
```

Notice the `AS "Value"` alias.
When EF Core maps to a primitive type, it expects a property named `Value`.
The quotes preserve the exact casing (PostgreSQL lowercases unquoted identifiers by default).

The interpolated string syntax (`$"{ticketTypeId}"`) might look dangerous, but EF Core converts this into a parameterized query automatically.
You're not building SQL strings, you're using C# interpolation as a convenient syntax for parameters.

## Example 2: Table-Valued Function

Functions can return entire result sets, which is where they really shine:

```sql
CREATE OR REPLACE FUNCTION ticketing.customer_order_summary(p_customer_id uuid)
RETURNS TABLE (
    order_id uuid,
    created_at_utc timestamptz,
    total_price numeric,
    currency text,
    item_count numeric
)
LANGUAGE sql
AS $$
SELECT
    o.id,
    o.created_at_utc,
    o.total_price,
    o.currency,
    COALESCE(SUM(oi.quantity), 0) AS item_count
FROM ticketing.orders o
LEFT JOIN ticketing.order_items oi ON oi.order_id = o.id
WHERE o.customer_id = p_customer_id
GROUP BY o.id, o.created_at_utc, o.total_price, o.currency
ORDER BY o.created_at_utc DESC
$$;
```

This function joins orders with their items, aggregates quantities, and returns multiple rows.
You could write this in LINQ, but the SQL is clearer and you can test it directly in your database tool.

To use it from C#, create a DTO that matches the function's output:

```csharp
public class OrderSummaryDto
{
    public Guid OrderId { get; set; }
    public DateTime CreatedAtUtc { get; set; }
    public decimal TotalPrice { get; set; }
    public string Currency { get; set; }
    public int ItemCount { get; set; }
}
```

Then query the function like any other table:

```csharp {13}
app.MapGet("customers/{customerId}/order-summary",
async (Guid customerId, EventManagementContext dbContext) =>
{
    var orders = await dbContext.Database
        .SqlQuery<OrderSummaryDto>(
            $"""
             SELECT
                order_id AS OrderId,
                created_at_utc AS CreatedAtUtc,
                total_price AS TotalPrice,
                currency AS Currency,
                item_count AS ItemCount
             FROM ticketing.customer_order_summary({customerId})
             """)
        .ToListAsync();

    return Results.Ok(orders);
});
```

The key is mapping column names to your DTO properties using aliases.
EF Core handles the rest automatically.

This is a simple case without joins, but you can use this pattern in more complex queries too.
However, you will have to project into DTOs manually since EF Core can't translate joins in raw SQL into entity graphs.
Usually, you'll return a flat structure from functions anyway, and then map to richer models in C# if needed.

## Understanding PostgreSQL Functions vs Procedures

PostgreSQL distinguishes between functions and procedures in important ways:

**Functions** are designed to **return values**.
They can return scalar values, tables, or even complex JSON objects.
You call them with `SELECT` and can use them in queries like any other expression.
Functions run within a transaction and can be used in `WHERE` clauses, joins, and other query contexts.

**Procedures** are designed for **side effects**.
They don't return values directly but can modify data and have `OUT` parameters.
You call them with `CALL` and they're ideal for complex operations that need to manage transactions explicitly or perform multiple related updates.

Think of it this way: use functions when you need data, use procedures when you need to change something.

This distinction matters because it affects how you design your database logic and how you call these routines from C#.

Let's see an example of a procedure.

## Example 3: Stored Procedure with Validation

Here's where procedures really prove their worth.
Let's say you need to adjust ticket inventory, but you want to prevent [**race conditions**](solving-race-conditions-with-ef-core-optimistic-locking) and validate the operation:

```sql
CREATE OR REPLACE PROCEDURE ticketing.adjust_available_quantity(
    p_ticket_type_id uuid,
    p_delta numeric,
    p_reason text DEFAULT 'manual-adjust'
)
LANGUAGE plpgsql
AS $$
DECLARE
    v_qty numeric;
    v_avail numeric;
    v_new_avail numeric;
BEGIN
    SELECT quantity, available_quantity
    INTO v_qty, v_avail
    FROM ticketing.ticket_types
    WHERE id = p_ticket_type_id
    FOR UPDATE;

    IF NOT FOUND THEN
        RAISE EXCEPTION 'ticket_type % not found', p_ticket_type_id;
    END IF;

    v_new_avail := v_avail + p_delta;

    IF v_new_avail < 0 THEN
        RAISE EXCEPTION 'Cannot reduce below zero';
    END IF;

    IF v_new_avail > v_qty THEN
        RAISE EXCEPTION 'Cannot exceed quantity';
    END IF;

    UPDATE ticketing.ticket_types
    SET available_quantity = v_new_avail
    WHERE id = p_ticket_type_id;
END;
$$;
```

This procedure does several important things:

- **Locks the row** with `FOR UPDATE` so no other transaction can modify it until we're done
- **Validates business rules** before making changes
- **Provides clear error messages** when something goes wrong
- **Keeps everything atomic** in a single database round trip

You could do all this in C# with manual transaction management and explicit locking, but it's more complex and error-prone.
Let the database handle what it's good at.

Here's how you call it from EF Core:

```csharp {10}
app.MapPut("ticket-types/{ticketTypeId}/available-quantity", async (
    Guid ticketTypeId,
    int quantity,
    EventManagementContext dbContext) =>
{
    try
    {
        await dbContext.Database.ExecuteSqlAsync(
            $"""
             CALL ticketing.adjust_available_quantity({ticketTypeId},{quantity})
             """);

        return Results.Ok(result);
    }
    catch (Exception e)
    {
        return Results.BadRequest(e.Message);
    }
});
```

The procedure doesn't return a value, but if it raises an exception (with `RAISE EXCEPTION`), PostgreSQL will propagate that to your C# code.
You can catch it and return a proper error response.

## About SQL Injection (Don't Panic)

You might be looking at those interpolated strings and thinking "wait, isn't this [**SQL injection waiting to happen**](ef-core-raw-sql-queries)?"

**It's not**.

When you write:

```csharp
$"SELECT * FROM users WHERE id = {userId}"
```

EF Core doesn't concatenate strings.
It converts this into:

```sql
SELECT * FROM users WHERE id = @p0
```

**The actual value is sent as a parameter**, completely separate from the SQL text.
This works for all the examples in this article.

The interpolation syntax is just a convenient way to write parameterized queries.

The reason why is we're not actually passing in a `string` to the `SqlQuery` method,
but a [`FormattableString`](https://learn.microsoft.com/en-us/dotnet/api/system.formattablestring).
This is a special type that captures the format and arguments separately, allowing EF Core to handle parameters.

Everything in this article works with [SQL Server](https://learn.microsoft.com/en-us/sql/),
[MySQL](https://dev.mysql.com/),
[SQLite](https://www.sqlite.org/index.html), and other databases EF Core supports.
The differences are mostly syntax.

## A Quick Word on Views

Database views are like functions without parameters.
They're saved queries you can reference by name.

You can query them using `SqlQuery<T>` just like functions:

```csharp
var results = await dbContext.Database
    .SqlQuery<ActiveCustomerDto>(
        $"SELECT * FROM ticketing.active_customers")
    .ToListAsync();
```

Or you can map them to entity types in your `DbContext` for full LINQ support.

Views are great for frequently-used queries that don't need parameters.
Functions give you the flexibility of parameterization.

## Wrapping Up

We've covered how to use PostgreSQL functions and procedures with EF Core, from simple scalar functions to complex procedures with validation and locking.

You learned when to use functions (when you need data back) versus procedures (when you need to modify data).
You saw how EF Core's `SqlQuery<T>` and `ExecuteSqlAsync` give you type safety while letting you write the SQL you need.
And you learned when raw SQL makes sense: complex aggregations, database-specific features, atomic operations with locking, and reducing round trips.

**EF Core** doesn't force you to choose between LINQ and raw SQL.
You can use both.

Use functions when you need to return data, procedures when you need to modify data with complex logic,
and raw SQL queries when LINQ doesn't capture your requirements efficiently.
The combination of EF Core's convenience and the database's power gives you the flexibility to choose the right tool for each scenario.

That's all for today. Hope this was helpful.

---



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Authentication tells you **who** the user is.
Authorization tells you **what** they can do.

Most .NET developers start with simple role-based checks: "Is this user an Admin?"
But as your application grows, you quickly realize that roles alone aren't enough.
You need **granular permissions** that can be combined and assigned flexibly.

That's where **Role-Based Access Control (RBAC)** shines.
Instead of hardcoding role checks everywhere, you define specific permissions and let roles carry those permissions.
A user might be a `Manager` role, but what matters is whether they have the `users:delete` permission.

Let me show you how to build a flexible, **permission-based authorization** system in ASP.NET Core.

## Understanding RBAC Components

[RBAC](https://auth0.com/docs/manage-users/access-control/rbac) has three key components that work together:

**Users** → assigned to → **Roles** → which contain → **Permissions**

Here's how it flows:

- **Users**: Individual people using your system
- **Roles**: Groups of related permissions (Admin, Manager, Editor)
- **Permissions**: Specific actions users can perform (users:read, orders:create, reports:delete)

<Image src="blogs/mnw_161/rbac.png" width={3411} height={1911} />

The beauty is in the flexibility.
You can assign multiple roles to a user, and roles can be modified without touching user assignments.
Need to give all Managers the ability to export reports?
Just add the `reports:export` permission to the `Manager` role.

This is much more maintainable than checking if someone is specifically an `Admin` or `Super Manager` in your code.

It also adds an extra extension point: you can implement custom permissions for some users without creating new roles.

## Building a Custom Authorization Handler

[ASP.NET Core's authorization system](https://learn.microsoft.com/en-us/aspnet/core/security/authorization/introduction) is built around **policies** and **requirements**.
Let's create a custom handler that checks permissions stored in the user's claims:

```csharp {4, 12-14, 18}
public class PermissionAuthorizationRequirement(params string[] allowedPermissions)
    : AuthorizationHandler<PermissionAuthorizationRequirement>, IAuthorizationRequirement
{
    public string[] AllowedPermissions { get; } = allowedPermissions;

    protected override Task HandleRequirementAsync(
        AuthorizationHandlerContext context,
        PermissionAuthorizationRequirement requirement)
    {
        foreach (var permission in requirement.AllowedPermissions)
        {
            bool found = context.User.FindFirst(c =>
                c.Type == CustomClaimTypes.Permission &&
                c.Value == permission) is not null;

            if (found)
            {
                context.Succeed(requirement);
                break;
            }
        }
        return Task.CompletedTask;
    }
}
```

Here's what's happening under the hood:

The class combines both the **requirement** (what permissions are needed) and the **handler** (how to check them).
This keeps related logic together and reduces boilerplate.

The handler looks through the user's claims for any claim with type `Permission` that matches one of the required permissions.
It's an **OR operation** - the user only needs **one** of the specified permissions to proceed.

If a matching permission is found, we call `context.Succeed(requirement)` and break out early.
No need to check the remaining permissions.

Alternatively, you could implement an **AND operation** if your use case requires all permissions to be present.

You'll need to define your custom claim type:

```csharp
public static class CustomClaimTypes
{
    public const string Permission = "permission";
}
```

And then you'll use this when issuing JWT tokens or setting up user claims.

```csharp {1-7, 14}
var permissions = await (
        from role in dbContext.Roles
        join permission in dbContext.RolePermissions on role.Id equals permission.RoleId
        where roles.Contains(role.Name)
        select permission.Name)
    .Distinct()
    .ToArrayAsync();

List<Claim> claims =
[
    new(JwtRegisteredClaimNames.Sub, user.Id),
    new(JwtRegisteredClaimNames.Email, user.Email!),
    ..roles.Select(r => new Claim(ClaimTypes.Role, r)),
    ..permissions.Select(p => new Claim(CustomClaimTypes.Permission, p))
];

var tokenDescriptor = new SecurityTokenDescriptor
{
    Subject = new ClaimsIdentity(claims),
    Expires = DateTime.UtcNow.AddMinutes(configuration.GetValue<int>("Jwt:ExpirationInMinutes")),
    SigningCredentials = credentials,
    Issuer = configuration["Jwt:Issuer"],
    Audience = configuration["Jwt:Audience"]
};

var tokenHandler = new JsonWebTokenHandler();

string accessToken = tokenHandler.CreateToken(tokenDescriptor);
```

## Creating Clean APIs with Extension Methods

Raw authorization policies work, but they're verbose. Let's create extension methods that make the developer experience much cleaner:

```csharp {7}
public static class PermissionExtensions
{
    public static void RequirePermission(
        this AuthorizationPolicyBuilder builder,
        params string[] allowedPermissions)
    {
        builder.AddRequirements(new PermissionAuthorizationRequirement(allowedPermissions));
    }
}
```

Now you can use this with **Minimal APIs**:

```csharp {24}
public static class Permissions
{
    public const string UsersRead = "users:read";
    public const string UsersUpdate = "users:update";
    public const string UsersDelete = "users:delete";
}

app.MapGet("me", (ApplicationDbContext dbContext) =>
{
    var user = await dbContext.Users
        .AsNoTracking()
        .Where(u => u.Id == int.Parse(User.FindFirstValue(JwtRegisteredClaimNames.Sub)!))
        .Select(u => new UserDto
        {
            u.Id,
            u.Email,
            u.FirstName,
            u.LastName
        })
        .SingleOrDefaultAsync();

    return Results.Ok(user);
})
.RequireAuthorization(policy => policy.RequirePermission(Permissions.UsersRead));
```

For **MVC Controllers**, create an attribute:

```csharp
[AttributeUsage(AttributeTargets.Method | AttributeTargets.Class)]
public class RequirePermissionAttribute(params string[] permissions) : AuthorizeAttribute
{
    public RequirePermissionAttribute(params string[] permissions)
        : base(policy: string.Join(",", permissions))
    {
    }
}
```

Then register the policy in your DI container:

```csharp
builder.Services.AddAuthorizationBuilder()
    .AddPolicy("users:read", policy => policy.RequirePermission(Permissions.UsersRead))
    .AddPolicy("users:update", policy => policy.RequirePermission(Permissions.UsersUpdate));
```

Usage becomes clean:

```csharp {1}
[RequirePermission(Permissions.UsersUpdate)]
public async Task<IActionResult> UpdateUser(int id, UpdateUserRequest request)
{
    // Your logic here
}
```

## Extension Points for Production

The basic implementation works great, but we could improve it further.
Here are two key extension points:

### Type-Safe Permissions with Enums

Instead of magic strings, use enums for compile-time safety:

```csharp
public enum Permission
{
    UsersRead,
    UsersUpdate,
    UsersDelete,
    OrdersCreate,
    ReportsExport
}
```

You'll have to convert these to strings when issuing claims and checking permissions.
And also convert from a string to an enum, when reading from claims and validating the permissions.

### Server-Side Permission Resolution

Rather than storing all permissions in JWT tokens (which can get large), fetch them server-side using `IClaimsTransformation`:

```csharp
public class PermissionClaimsTransformation(IPermissionService permissionService)
    : IClaimsTransformation
{
    public async Task<ClaimsPrincipal> TransformAsync(ClaimsPrincipal principal)
    {
        if (principal.Identity?.IsAuthenticated != true)
        {
            return principal;
        }

        var userId = principal.FindFirst(ClaimTypes.NameIdentifier)?.Value;
        if (userId == null)
        {
            return principal;
        }

        // Fetch permissions from database, then cache
        // IMPORTANT: Cache these results to avoid DB hits on every request
        var permissions = await permissionService.GetUserPermissionsAsync(userId);

        var claimsIdentity = (ClaimsIdentity)principal.Identity;
        foreach (var permission in permissions)
        {
            claimsIdentity.AddClaim(new Claim(CustomClaimTypes.Permission, permission));
        }

        return principal;
    }
}
```

Register it in your DI container:

```csharp
builder.Services.AddScoped<IClaimsTransformation, PermissionClaimsTransformation>();
```

This approach keeps your JWTs lightweight while still providing fast authorization checks through claims.

You can learn more about claims transformation in my [**previous article**](master-claims-transformation-for-flexible-aspnetcore-authorization).

## Takeaway

**RBAC** transforms authorization from a maintenance headache into a flexible, scalable system.

**Start with permissions**: Define what actions users can perform, not what roles they have.

**Custom authorization handlers** give you complete control over how permissions are validated.

**Extension methods** make the developer experience clean and consistent across your API.

For production systems, consider **type-safe enums** and **server-side permission resolution** to keep your tokens lean and your code maintainable.

The result? Authorization logic that's easy to understand, test, and modify as your application evolves.

---


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