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Building distributed applications might seem simple at first. It's just servers talking to each other. Right?

However, it opens a set of potential problems you must consider.
What if the network has a hiccup?
A service unexpectedly crashes?
You try to scale, and everything crumbles under the load?
This is where the way your distributed system communicates becomes critical.

Traditional synchronous communication, where services call each other directly, is inherently fragile.
It creates tight coupling, making your whole application vulnerable to single points of failure.

To combat this, we can turn to distributed messaging
(and introduce an entirely different set of problems, but that's a story for another issue).

One powerful tool for achieving this in the .NET world is MassTransit.

In this week's issue, we'll explore MassTransit's implementation of the request-response pattern.

## Request-Response Messaging Pattern Introduction

Let's start by explaining how the request-response messaging pattern works.

The request-response pattern is just like making a traditional function call but over the network.
One service, the requester, sends a request message and waits for a corresponding response message.
This is a [**synchronous communication approach**](modular-monolith-communication-patterns) from the requester's side.

The good parts:

- **Loose Coupling**: Services don't need direct knowledge of each other, only of the message contracts.
  This makes changes and scaling easier.
- **Location Transparency**: The requester doesn't need to know _where_ the responder is located, leading to improved flexibility.

The bad parts:

- **Latency**: The overhead of messaging adds some additional latency.
- **Complexity**: Introducing a messaging system and managing the additional infrastructure can increase project complexity.

<Image
  src="blogs/mnw_087/request_response.png"
  width={1617}
  height={848}
  alt="Request response messaging pattern diagram."
/>

## Request-Response Messaging With MassTransit

[**MassTransit**](using-masstransit-with-rabbitmq-and-azure-service-bus)
supports the [request-response](https://masstransit.io/documentation/concepts/requests) messaging pattern out of the box.
We can use a **request client** to send requests and wait for a response.
The request client is asynchronous and supports the `await` keyword.
The request will also have a timeout of 30 seconds by default, to prevent waiting for the response for too long.

Let's imagine a scenario where you have an order processing system that needs to fetch an order's latest status.
We can fetch the status from an Order Management service.
With MassTransit, you'll create a request client to initiate the process.
This client will send a `GetOrderStatusRequest` message onto the bus.

```csharp
public record GetOrderStatusRequest
{
    public string OrderId { get; init; }
}
```

On the Order Management side, a responder (or consumer) will be listening for `GetOrderStatusRequest` messages.
It receives the request, potentially queries a database to get the status,
and then sends a `GetOrderStatusResponse` message back onto the bus.
The original request client will be waiting for this response and can then process it accordingly.

```csharp
public class GetOrderStatusRequestConsumer : IConsumer<GetOrderStatusRequest>
{
    public async Task Consume(ConsumeContext<GetOrderStatusRequest> context)
    {
        // Get the order status from a database.

        await context.ResponseAsync<GetOrderStatusResponse>(new
        {
            // Set the respective response properties.
        });
    }
}
```

## Getting User Permissions In a Modular Monolith

Here's a real-world scenario where my team decided to implement this pattern.
We were building a [**modular monolith**](/modular-monolith-architecture),
and one of the modules was responsible for managing user permissions.
The other modules could call out to the Users module to get the user's permissions.
And this works great while we are still inside a monolith system.

However, at one point we needed to extract one module into a separate service.
This meant that the communication with the Users module using simple method calls would no longer work.

Luckily, we were already using MassTransit and RabbitMQ for messaging inside the system.

So, we decided to use the MassTransit request-response feature to implement this.

The new service will inject an `IRequestClient<GetUserPermissions>`.
We can use it to send a `GetUserPermissions` message and await a response.

A very powerful feature of MassTransit is that you can await more than one response message.
In this example, we're waiting for a `PermissionsResponse` or an `Error` response.
This is great, because we also have a way to handle failures in the consumer.

```csharp {11,13,18}
internal sealed class PermissionService(
    IRequestClient<GetUserPermissions> client)
    : IPermissionService
{
    public async Task<Result<PermissionsResponse>> GetUserPermissionsAsync(
        string identityId)
    {
        var request = new GetUserPermissions(identityId);

        Response<PermissionsResponse, Error> response =
            await client.GetResponse<PermissionsResponse, Error>(request);

        if (response.Is(out Response<Error> errorResponse))
        {
            return Result.Failure<PermissionsResponse>(errorResponse.Message);
        }

        if (response.Is(out Response<PermissionsResponse> permissionResponse))
        {
            return permissionResponse.Message;
        }

        return Result.Failure<PermissionsResponse>(NotFound);
    }
}
```

In the Users module, we can easily implement the `GetUserPermissionsConsumer`.
It will respond with a `PermissionsResponse` if the permissions are found or an `Error` in case of a failure.

```csharp
public sealed class GetUserPermissionsConsumer(
    IPermissionService permissionService)
    : IConsumer<GetUserPermissions>
{
    public async Task Consume(ConsumeContext<GetUserPermissions> context)
    {
        Result<PermissionsResponse> result =
            await permissionService.GetUserPermissionsAsync(
                context.Message.IdentityId);

        if (result.IsSuccess)
        {
            await context.RespondAsync(result.Value);
        }
        else
        {
            await context.RespondAsync(result.Error);
        }
    }
}
```

## Closing Thoughts

By embracing messaging patterns with MassTransit, you're building on a much sturdier foundation.
Your .NET services are now less tightly coupled, giving you the flexibility to evolve them independently
and weather those inevitable network glitches or service outages.

The [request-response pattern](https://youtu.be/NjsoykEOkrk) is a powerful tool in your messaging arsenal.
MassTransit makes it remarkably easy to implement, ensuring that requests and responses are delivered reliably.

We can use request-response to implement communication between modules in a [**modular monolith**](/modular-monolith-architecture).
However, don't take this to the extreme, or your system might suffer from increased latency.

Start small, experiment, and see how the reliability and flexibility of messaging can transform your development experience.

That's all for this week. Stay awesome!

---



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If you're building or maintaining distributed .NET applications, understanding how they behave is key to ensuring reliability and performance.

Distributed systems offer flexibility but introduce complexity, making troubleshooting a headache.
Understanding how requests flow through your system is crucial for debugging and performance optimization.

OpenTelemetry is an open-source observability framework that makes this possible.

In this article, we'll dive into what OpenTelemetry is, how to use it in your .NET projects, and the powerful insights it provides.

## OpenTelemetry Introduction

[OpenTelemetry](https://opentelemetry.io/) (OTel) is a vendor-neutral, open-source standard for instrumenting applications to generate telemetry data.
OpenTelemetry contains APIs, SDKs, tools, and integrations for creating and managing this telemetry data (traces, metrics, and logs).

Telemetry data includes:

- **Traces**: Represent the flow of requests through distributed systems, showing timings and relationships between services.
- **Metrics**: Numerical measurements of system behavior over time (e.g., request counts, error rates, memory usage).
- **Logs**: Textual records of events with rich contextual information. Structured logs.

<figure>
  <Image
    src="blogs/mnw_086/otel.png"
    width={1500}
    height={996}
    alt="OpenTelemetry Reference Architecture."
  />
  <figcaption>
    Source: [https://opentelemetry.io/docs/](https://opentelemetry.io/docs/)
  </figcaption>
</figure>

OpenTelemetry provides a unified way to collect this data, making it easier to understand the behavior and health of complex distributed applications.

We can export the telemetry data we are collecting to a service capable of processing it and providing us with an interface to analyze it.

We're going to configure OpenTelemetry to export traces directly to [Jaeger](https://www.jaegertracing.io/).

## Adding OpenTelemetry to .NET Applications

OpenTelemetry provides libraries and SDKs to add code (instrumentation) into your .NET applications.
These instrumentations automatically capture the traces, metrics, and logs we are interested in.

We're going to install the following NuGet packages:

```powershell
# Automatic tracing, metrics
Install-Package OpenTelemetry.Extensions.Hosting

# Telemetry data exporter
Install-Package OpenTelemetry.Exporter.OpenTelemetryProtocol

# Instrumentation packages
Install-Package OpenTelemetry.Instrumentation.Http
Install-Package OpenTelemetry.Instrumentation.AspNetCore
Install-Package OpenTelemetry.Instrumentation.EntityFrameworkCore
Install-Package OpenTelemetry.Instrumentation.StackExchangeRedis
Install-Package Npgsql.OpenTelemetry
```

Once we have these NuGet packages installed, it's time to configure some services.

```csharp
services
    .AddOpenTelemetry()
    .ConfigureResource(resource => resource.AddService(serviceName))
    .WithTracing(tracing =>
    {
        tracing
            .AddAspNetCoreInstrumentation()
            .AddHttpClientInstrumentation()
            .AddEntityFrameworkCoreInstrumentation()
            .AddRedisInstrumentation()
            .AddNpgsql();

        tracing.AddOtlpExporter();
    });
```

- `AddAspNetCoreInstrumentation` - This enables ASP.NET Core instrumentation.
- `AddHttpClientInstrumentation` - This enables `HttpClient` instrumentation for outgoing requests.
- `AddEntityFrameworkCoreInstrumentation` - This enables EF Core instrumentation.
- `AddRedisInstrumentation` - This enables Redis instrumentation.
- `AddNpgsql` - This enables PostgreSQL instrumentation.

With all of these instrumentations configured, our application will start collecting a lot of valuable traces at runtime.

We also need to configure an environment variable for the exporter added with `AddOtlpExporter` to work correctly.
We can set `OTEL_EXPORTER_OTLP_ENDPOINT` through application settings.
The address specified here will point to a local Jaeger instance.

```
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
```

## Running Jaeger Locally

[Jaeger](https://www.jaegertracing.io/) is an open source, distributed tracing platform.
Jaeger maps the flow of requests and data as they travel through a distributed system.
These requests could be calling out to multiple services, and Jaeger knows how to piece all of this information together.

Here's how to run Jaeger inside a Docker container:

```
docker run -d -p 4317:4317 -p 16686:16686 jaegertracing/all-in-one:latest
```

We're using the `jaegertracing/all-in-one:latest` image, and exposing the `4317` to accept telemetry data.
The Jaeger user interface will be exposed on the `16686` port.

## Distributed Tracing

After installing the OpenTelemetry libraries and configuring tracing in our applications, we can send some requests to generate telemetry data.
We can then access Jaeger to start analyzing our distributed traces.

**Registering a new user**

Here's an example of registering a new user with the system.
We're accessing the API gateway (`Evently.Gateway`) service, which proxies the request to the `Evently.Api` service.
And you can see that the `Evently.Api` service makes a few HTTP requests before persisting a new record in the database.

<Image
  src="blogs/mnw_086/trace_1.png"
  width={1920}
  height={650}
  alt="Distributed trace."
/>

**Publishing a message with MassTransit**

Here's another distributed trace where we publish the `UserRegisteredIntegrationEvent` over a message bus.
You can see that it's being consumed by two different services that write some data to the database.

<Image
  src="blogs/mnw_086/trace_2.png"
  width={1920}
  height={596}
  alt="Distributed trace."
/>

**Examining additional trace information**

Distributed traces can include some useful contextual information.
Here's an example trace representing a database command.
This comes from the PostgreSQL instrumentation, and we can see the SQL query that we are executing.

<Image
  src="blogs/mnw_086/trace_3.png"
  width={1920}
  height={500}
  alt="Distributed trace."
/>

**Complex distributed traces**

Here's a more complex distributed trace, which includes:

- Three .NET applications
- PostgreSQL database
- Redis distributed cache

We're sending a request to get the customer's cart.
The request will first hit the API gateway, which proxies it to the `Evently.Ticketing.Api` service that owns the data.
However, the `Evently.Ticketing.Api` service needs to reach out to the `Evently.Api` service to get the authorization information.
And all of this leads to the distributed trace you can see below.

<Image
  src="blogs/mnw_086/trace_4.png"
  width={1920}
  height={850}
  alt="Distributed trace."
/>

## Summary

Understanding modern applications, especially distributed ones, can be a real mind-bender.
OpenTelemetry is like having X-ray vision into your system.

While adding OpenTelemetry takes some upfront work, consider it an investment.
That investment pays off big time when problems pop up.
Instead of frantic guesswork, you have precise data to zero in on issues fast.

Is OpenTelemetry a magic bullet for all your problems? Nope.

But it's an excellent tool to add to your troubleshooting arsenal, especially as your .NET applications grow and get more complex.

If you're curious where the distributed traces come from, it's from the application we're building in my [**Modular Monolith course**](/modular-monolith-architecture).

That's all for today. Stay awesome, and I'll see you next week.

---



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Sometimes, especially in high-traffic scenarios, you absolutely need to ensure that only one process can modify a piece of data at a time.

Imagine you're building the ticket sales system for a wildly popular concert.
Customers are eagerly grabbing tickets, and the last few could sell out simultaneously.
If you're not careful, multiple customers might think they've secured the final seat, leading to overbooking and disappointment!

Entity Framework Core is a fantastic tool, but it doesn't have a direct mechanism for pessimistic locking.
[Optimistic locking](solving-race-conditions-with-ef-core-optimistic-locking) (using versions) can work, but in high-contention scenarios, it can lead to a lot of retries.

So, how can we solve this problem with EF Core?

## The Scenario in More Detail

Here's a simplified code snippet to illustrate our ticketing challenge:

```csharp
public async Task Handle(CreateOrderCommand request)
{
    await using DbTransaction transaction = await unitOfWork
        .BeginTransactionAsync();

    Customer customer = await customerRepository.GetAsync(request.CustomerId);

    Order order = Order.Create(customer);
    Cart cart = await cartService.GetAsync(customer.Id);

    foreach (CartItem cartItem in cart.Items)
    {
        // Uh-oh... what if two requests hit this at the same time?
        TicketType ticketType = await ticketTypeRepository.GetAsync(
            cartItem.TicketTypeId);

        ticketType.UpdateQuantity(cartItem.Quantity);

        order.AddItem(ticketType, cartItem.Quantity, cartItem.Price);
    }

    orderRepository.Insert(order);

    await unitOfWork.SaveChangesAsync();

    await transaction.CommitAsync();

    await cartService.ClearAsync(customer.Id);
}
```

The example above is contrived, but it should be enough to explain the problem.
During checkout, we verify the `AvailableQuantity` for each ticket.

What will happen if we get concurrent requests trying to purchase the same ticket?

The worst-case scenario is we end up "overselling" the tickets.
Concurrent requests could see available tickets for sale and complete the checkout.

So, how do we solve this?

## Raw SQL to the Rescue!

Since EF Core doesn't offer pessimistic locking directly, we'll dip into a bit of good old-fashioned SQL.
We will replace the `GetAsync` call to fetch the ticket with `GetWithLockAsync`.

Thankfully, EF Core makes this easy with [raw SQL queries](ef-core-raw-sql-queries):

```csharp {10}
public async Task<TicketType> GetWithLockAsync(Guid id)
{
    return await context
        .TicketTypes
        .FromSql(
            $@"
            SELECT id, event_id, name, price, currency, quantity
            FROM ticketing.ticket_types
            WHERE id = {id}
            FOR UPDATE NOWAIT") // PostgreSQL: Lock or fail immediately
        .SingleAsync();
}
```

Understanding the magic:

- `FOR UPDATE NOWAIT`: This is the heart of pessimistic locking in PostgreSQL.
  It tells the database "Grab this row, lock it for me, and if it's already locked, raise an error right now."
- **Error Handling**: We'd wrap our `GetWithLockAsync` call in a `try-catch` block to gracefully handle locking failures, either retrying or notifying the user.

Since there isn't a built-in way in EF Core to add query hints, we have to write raw SQL queries.
We can use the PostgreSQL `SELECT FOR UPDATE` statement to acquire a row-level lock on the selected rows.
Any competing transactions will be blocked until the current transaction releases the lock.
This is a very simple way to implement pessimistic locking.

## Flavors of Locking and When to Use Them

To prevent the operation from waiting for other transactions to release any locked rows, you can combine `FOR UPDATE` with:

- `NO WAIT` - Reports an error if the row can't be locked instead of waiting.
- `SKIP LOCKED` - Skips any selected rows that cannot be locked.

Skipping locked rows comes with a caveat - you will get inconsistent results from the database.
However, this can be useful to avoid lock contention when multiple consumers access a queue-like table.
Implementing the [Outbox pattern](outbox-pattern-for-reliable-microservices-messaging) is a great example of this.

**SQL Server**: You'd use the `WITH (UPDLOCK, READPAST)` query hint for a similar effect.

## Pessimistic Locking vs. Serializable Transactions

**Serializable** transactions offer the highest level of data consistency.
They guarantee that all transactions are executed as if they happened in a strict, sequential order, even if they occur simultaneously.
This eliminates the possibility of anomalies like dirty reads (seeing uncommitted data) or non-repeatable reads (data changing between reads).

Here's how it works:

- When a transaction starts under the Serializable isolation level, the database locks all the data the transaction might access.
- These locks are held until the entire transaction is committed or rolled back.
- Any other transaction attempting to access the locked data will be blocked until the first transaction releases its locks.

While Serializable transactions provide the ultimate isolation, they come with a significant cost:

- **Performance Overhead**: Locking a large chunk of data can severely impact performance,
  especially in high-concurrency scenarios.
- **Deadlocks**: With so much locking happening, there's a higher risk of deadlocks.
  These occur when two or more transactions are waiting for locks held by each other, creating a stalemate.

Pessimistic locking with `SELECT FOR UPDATE` offers a more targeted approach to data isolation.
You explicitly lock the specific rows you need to modify.
Other transactions attempting to access the locked rows are blocked until the lock is released.

By locking only the necessary data, pessimistic locking avoids the performance overhead associated with locking everything.
Since you're locking fewer resources, the chances of deadlocks are lower.

## When to Use Each Approach

The best approach depends on your specific needs:

- **Serializable Transactions**: Ideal for scenarios involving highly sensitive data where even the slightest inconsistency is unacceptable.
  Examples include financial transactions and medical record updates.
- **Pessimistic Locking**: A great choice for most use cases, especially in high-traffic applications.
  It provides strong consistency while maintaining good performance and reducing deadlock risks.

## Takeaway

I hope this exploration of pessimistic locking has been helpful.
It's a powerful tool to have in your arsenal if you have scenarios where absolute data consistency is paramount.

Both **Serializable transactions** and pessimistic locking with `SELECT FOR UPDATE` are excellent options for ensuring data consistency.
Consider the level of isolation required, potential performance impact, and the likelihood of deadlocks when making your choice.

That's all for today. Stay awesome, and I'll see you next week.

---



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[Claims-based authorization](https://learn.microsoft.com/en-us/aspnet/core/security/authorization/claims) mechanisms are central to modern authorization in ASP.NET Core.
However, the access tokens issued by your Identity Provider (IDP) might not always perfectly align with your application's internal authorization needs.

External IDPs like [Microsoft Entra ID](https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id) (previously Azure AD) or
[Auth0](https://auth0.com) might have their own schema for claims or might not directly issue all the claims your application needs for its authorization logic.

The solution? Claims transformation.

Claims transformation allows you to modify the claims before the application uses them for authorization.

In today's issue, we will:

- Explore the concept of claims transformation in ASP.NET Core
- Explore the `IClaimsTransformation` interface with practical examples
- Address considerations for security and RBAC (Role-Based Access Control)

## How Does Claims Transformation Work?

They say a picture is worth a thousand words.
In software engineering, we have something called [UML](https://en.wikipedia.org/wiki/Unified_Modeling_Language) diagrams that we can use to paint a picture.

Here's a [sequence diagram](https://en.wikipedia.org/wiki/Sequence_diagram) showing the claims transformation flow:

1. The user authenticates with the Identity Provider
2. The user calls the backend API and provides an access token
3. The backend API performs claims transformation and authorization
4. If the user is correctly authorized, the backend API returns a response

<Image
  src="blogs/mnw_084/claims_transformation_sequence_diagram.png"
  width={1614}
  height={1357}
  alt="Claims transformation sequence diagram."
/>

Let's see how to implement this in ASP.NET Core.

## Simple Claims Transformation

Claims can be created from any user or identity data issued by a trusted identity provider.
A claim is a name-value pair that represents the subject's identity, not what the subject can do.

The core of [claims transformation](https://learn.microsoft.com/en-us/aspnet/core/security/authentication/claims#extend-or-add-custom-claims-using-iclaimstransformation)
in ASP.NET Core is the [`IClaimsTransformation`](https://learn.microsoft.com/en-us/dotnet/api/microsoft.aspnetcore.authentication.iclaimstransformation) interface.

It exposes a single method to transform claims:

```csharp
public interface IClaimsTransformation
{
    Task<ClaimsPrincipal> TransformAsync(ClaimsPrincipal principal);
}
```

Here's a simple example of using `IClaimsTransformation` to add a custom claim:

```csharp
internal static class CustomClaims
{
    internal const string CardType = "card_type";
}

internal sealed class CustomClaimsTransformation : IClaimsTransformation
{
    public Task<ClaimsPrincipal> TransformAsync(ClaimsPrincipal principal)
    {
        if (principal.HasClaim(claim => claim.Type == CustomClaims.CardType))
        {
            return Task.FromResult(principal);
        }

        ClaimsIdentity claimsIdentity = new ClaimsIdentity();

        claimsIdentity.AddClaim(new Claim(CustomClaims.CardType, "platinum"));

        principal.AddIdentity(claimsIdentity);

        return Task.FromResult(principal);
    }
}
```

The `CustomClaimsTransformation` class should be registered as a service:

```csharp
builder.Services
    .AddTransient<IClaimsTransformation, CustomClaimsTransformation>();
```

Finally, you can define a custom authorization policy that uses this claim:

```csharp
builder.Services.AddAuthorization(options =>
{
    options.AddPolicy(
        "HasPlatinumCard",
        builder => builder
            .RequireAuthenticatedUser()
            .RequireClaim(CustomClaims.CardType, "platinum"));
});
```

There are a few caveats with using `IClaimsTransformation` you should be aware of:

- **Might execute multiple times**: The `TransformAsync` method might get called multiple times.
  Claims transformation should be idempotent to avoid adding the same claim multiple times to the `ClaimsPrincipal`.
- **Potential performance impact**: Since it's executed on authentication requests, be mindful of your transformation logic's performance,
  especially if it involves external calls (database, APIs). Consider caching where appropriate.

## Implementing RBAC With Claims Transformation

[Role-Based Access Control (RBAC)](https://auth0.com/docs/manage-users/access-control/rbac) is an authorization model where permissions are assigned to roles,
and users are granted roles.
Claims transformation helps implement RBAC smoothly.
By adding role claims and potentially permission claims, authorization logic throughout your application can be simplified.
Another benefit is that you can keep the access token smaller and free of any role or permission claims.

Let's consider a scenario where your application manages resources at a granular level,
but your identity provider only provides coarse-grained roles like `Registered` or `Member`.
You could use claims transformation to map the `Member` role to specific fine-grained permissions like `SubmitOrder` and `PurchaseTicket`.

Here's a more complex `CustomClaimsTransformation` implementation.
We send a database query using `GetUserPermissionsQuery` and get the `PermissionsResponse` back.
The `PermissionsResponse` contains the user's permissions, which are added as custom claims.

```csharp
internal sealed class CustomClaimsTransformation(
    IServiceProvider serviceProvider)
    : IClaimsTransformation
{
    public async Task<ClaimsPrincipal> TransformAsync(
        ClaimsPrincipal principal)
    {
        if (principal.HasClaim(c => c.Type == CustomClaims.Sub ||
                                    c.Type == CustomClaims.Permission))
        {
            return principal;
        }

        using IServiceScope scope = serviceProvider.CreateScope();

        ISender sender = scope.ServiceProvider.GetRequiredService<ISender>();

        string identityId = principal.GetIdentityId();

        Result<PermissionsResponse> result = await sender.Send(
            new GetUserPermissionsQuery(identityId));

        if (result.IsFailure)
        {
            throw new ClaimsAuthorizationException(
                nameof(GetUserPermissionsQuery), result.Error);
        }

        var claimsIdentity = new ClaimsIdentity();

        claimsIdentity.AddClaim(
            new Claim(CustomClaims.Sub, result.Value.UserId.ToString()));

        foreach (string permission in result.Value.Permissions)
        {
            claimsIdentity.AddClaim(
                new Claim(CustomClaims.Permission, permission));
        }

        principal.AddIdentity(claimsIdentity);

        return principal;
    }
}
```

Now that the `ClaimsPrincipal` contains the permissions as custom claims, you can do some interesting things.
For example, you can implement a permission-based `AuthorizationHandler`:

```csharp {8}
internal sealed class PermissionAuthorizationHandler
    : AuthorizationHandler<PermissionRequirement>
{
    protected override Task HandleRequirementAsync(
        AuthorizationHandlerContext context,
        PermissionRequirement requirement)
    {
        HashSet<string> permissions = context.User.GetPermissions();

        if (permissions.Contains(requirement.Permission))
        {
            context.Succeed(requirement);
        }

        return Task.CompletedTask;
    }
}
```

## Takeaway

Claims transformation is an elegant way to bridge the gap between claims provided by identity providers and the needs of your ASP.NET Core application.
The `IClaimsTransformation` interface enables you to customize the claims of the current `ClaimsPrincipal`.
Whether you need to add roles, map external groups to internal permissions, or extract additional information from a user profile,
claims transformation offers the flexibility to do so.

However, it's important to use claims transformation with a few key considerations in mind:

- Claims transformations are executed on each request.
- The `IClaimsTransformation` should be idempotent. It should not add existing claims to the `ClaimsPrincipal` if executed multiple times.
- Design your transformations efficiently, and consider caching the results if you're fetching external data to enrich your claims.

If you want to see a complete implementation of RBAC in ASP.NET Core, check out this [Authentication & Authorization playlist](https://www.youtube.com/playlist?list=PLYpjLpq5ZDGtJOHUbv7KHuxtYLk1nJPw5).

Hope this was helpful.

See you next week.

---



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Modern web applications need to serve increasing numbers of users and handle surges in traffic.
When a single server reaches its limits, performance degrades, leading to slow response times, errors, or complete downtime.

Load balancing is a key technique to address these challenges and improve the scalability of your application.

In this article, we will explore:

- How to use [YARP (Yet Another Reverse Proxy)](implementing-an-api-gateway-for-microservices-with-yarp) to implement load balancing
- How to leverage horizontal scaling for performance gains
- How to utilize K6 as a load testing tool

We'll dive into load balancing, why it matters, and how YARP simplifies the process for .NET applications.

## Types of Software Scalability

Before exploring YARP and load balancing further, let's cover the fundamentals of scaling.

There are two main approaches:

- **Vertical Scaling**: Involves upgrading individual servers with more powerful hardware - more CPU cores, RAM, and faster storage.
  However, this has a few limitations: costs escalate quickly, and you'll still hit a performance ceiling.
- **Horizontal Scaling**: Involves adding more servers to your infrastructure and distributing the load intelligently among them.
  This approach offers greater scalability potential, as you can continue adding servers to handle more traffic.

Horizontal scaling is where load balancing comes in, and YARP shines bright in this approach.

## Adding a Reverse Proxy

[YARP](https://microsoft.github.io/reverse-proxy/index.html) is a high-performance reverse proxy library from Microsoft.
It's designed with modern microservice architectures in mind.
A reverse proxy sits in front of your backend servers, acting as a traffic director.

Setting up YARP is quite straightforward.
You'll install the YARP NuGet package, create a basic configuration to define your backend destinations, and then activate the YARP middleware.
YARP allows you to perform routing and transformation tasks on incoming requests before they reach your backend servers.

First, let's install the `Yarp.ReverseProxy` NuGet package:

```powershell
Install-Package Yarp.ReverseProxy
```

Then, we're going to configure the required application services and introduce the YARP middleware to the request pipeline:

```csharp {3,4,8}
var builder = WebApplication.CreateBuilder(args);

builder.Services.AddReverseProxy()
    .LoadFromConfig(builder.Configuration.GetSection("ReverseProxy"));

var app = builder.Build();

app.MapReverseProxy();

app.Run();
```

All that's left is adding the YARP configuration to our `appsettings.json` file.
YARP uses `Routes` to represent incoming requests to the reverse proxy and `Clusters` to define the downstream services.
The `{**catch-all}` pattern allows us to easily route all incoming requests.

```json
{
  "ReverseProxy": {
    "Routes": {
      "api-route": {
        "ClusterId": "api-cluster",
        "Match": {
          "Path": "{**catch-all}"
        },
        "Transforms": [{ "PathPattern": "{**catch-all}" }]
      }
    },
    "Clusters": {
      "api-cluster": {
        "Destinations": {
          "destination1": {
            "Address": "http://api:8080"
          }
        }
      }
    }
  }
}
```

This configures YARP as a pass-through proxy, but let's update it to support horizontal scaling.

## Scaling Out With YARP Load Balancing

The core of horizontal scaling with YARP lies in its various [load balancing strategies](https://microsoft.github.io/reverse-proxy/articles/load-balancing.html):

- `PowerOfTwoChoices`: Selects two random destinations and selects the one with the least assigned requests.
- `FirstAlphabetical`: Selects the alphabetically first available destination server.
- `LeastRequests`: Sends requests to servers with the least assigned requests.
- `RoundRobin`: Distributes requests evenly across backend servers.
- `Random`: Randomly selects a backend server for each request.

You configure these strategies within YARP's configuration file.
The load balancing strategy can be configured using the `LoadBalancingPolicy` property on the cluster.

Here's what the updated YARP configuration looks like with `RoundRobin` load balancing:

```json {14,16-23}
{
  "ReverseProxy": {
    "Routes": {
      "api-route": {
        "ClusterId": "api-cluster",
        "Match": {
          "Path": "{**catch-all}"
        },
        "Transforms": [{ "PathPattern": "{**catch-all}" }]
      }
    },
    "Clusters": {
      "api-cluster": {
        "LoadBalancingPolicy": "RoundRobin",
        "Destinations": {
          "destination1": {
            "Address": "http://api-1:8080"
          },
          "destination2": {
            "Address": "http://api-2:8080"
          },
          "destination3": {
            "Address": "http://api-3:8080"
          }
        }
      }
    }
  }
}
```

Here's a diagram of what our system could look like with a YARP load balancer and horizontally scaled application servers.

The incoming API requests will first hit YARP, distributing the traffic to the application servers based on the load balancing strategy.
In this example, there's one database serving multiple application instances.

<Image
  src="blogs/mnw_083/horizontal_scaling.png"
  width={1615}
  height={919}
  alt="Horizontal scaling with a YARP load balancer."
/>

And now, let's do some performance testing.

## Performance Testing with K6

To see the impact of our horizontal scaling efforts, we need to do some load testing.
[K6](https://k6.io/) is a modern, developer-friendly load testing tool.
We'll write K6 scripts to simulate user traffic on our application and compare metrics like average response time and the number of successful requests per second.

The application we're going to scale horizontally has two API endpoints.
The `POST /users` endpoint creates a new user, saves the user to a [PostgreSQL](https://www.postgresql.org/) database, and returns the user's identifier.
The `GET /users/id` endpoint returns a user with the given identifer if it exists.

Here's a k6 performance test that will:

- Ramp up to **20 virtual users**
- Send a `POST` request to the `/users` endpoint
- Check that the response is `201 Created`
- Send a `GET` request to the `/users/{id}` endpoint
- Check that the response is `200 OK`

Note that all API requests go through the YARP load balancer.

```js
import { check } from 'k6';
import http from 'k6/http';

export const options = {
  stages: [
    { duration: '10s', target: 20 },
    { duration: '1m40s', target: 20 },
    { duration: '10s', target: 0 }
  ]
};

export default function () {
  const proxyUrl = 'http://localhost:3000';

  const response = http.post(`${proxyUrl}/users`);

  check(response, {
    'response code was 201': (res) => res.status == 201
  });

  const userResponse = http.get(`${proxyUrl}/users/${response.body}`);

  check(userResponse, {
    'response code was 200': (res) => res.status == 200
  });
}
```

To make the performance testing results more consistent, we can limit the available resources on the [Docker](https://www.docker.com) containers to `1 CPU` and `0.5G` of RAM.

```yml {4,5}
services:
  api:
    image: ${DOCKER_REGISTRY-}loadbalancingapi
    cpus: 1
    mem_limit: '0.5G'
    ports:
      - 5000:8080
    networks:
      - proxybackend
```

Finally, here are the k6 performance testing results:

```
|    API Instances   |     Request Duration     |    Requests Per Second    |
|------------------- |------------------------- |---------------------------:
|         1          |         9.68 ms          |          2260/s           |
|--------------------|------------------------- |---------------------------|
|         2          |         6.57 ms          |          2764/s           |
|--------------------|------------------------- |---------------------------|
|         3          |         5.62 ms          |          3227/s           |
|--------------------|------------------------- |---------------------------|
|         5          |         4.65 ms          |          3881/s           |
```

## Summary

Horizontal scaling, coupled with effective load balancing, can significantly enhance the performance and scalability of your web applications.
The benefits of horizontal scaling become especially apparent in high-traffic scenarios where a single server can no longer cope with the demand.

YARP is a powerful and user-friendly reverse proxy server for .NET applications.
However, highly complex, large-scale distributed systems might benefit from specialized, standalone load-balancing solutions.
These dedicated solutions can offer more granular control and sophisticated features.

If you want to learn more, here's how to [build an API Gateway using YARP](implementing-an-api-gateway-for-microservices-with-yarp).

You can find the [source code](https://github.com/m-jovanovic/yarp-load-balancing) for this example on GitHub.

That's all for today. I'll see you next week.

---



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Whether you're building a data analytics platform, migrating a legacy system, or onboarding a surge of new users,
there will likely come a time when you'll need to insert a massive amount of data into your database.

Inserting the records one by one feels like watching paint dry in slow motion.
Traditional methods won't cut it.

So, understanding fast bulk insert techniques with C# and EF Core becomes essential.

In today's issue, we'll explore several options for performing bulk inserts in C#:

- Dapper
- EF Core
- EF Core Bulk Extensions
- SQL Bulk Copy

The examples are based on a `User` class with a respective `Users` table in **SQL Server**.

```csharp
public class User
{
    public int Id { get; set; }
    public string Email { get; set; }
    public string FirstName { get; set; }
    public string LastName { get; set; }
    public string PhoneNumber { get; set; }
}
```

This isn't a complete list of bulk insert implementations.
There are a few options I didn't explore, like manully generating SQL statements
and using [Table-Valued parameters](https://learn.microsoft.com/en-us/sql/relational-databases/tables/use-table-valued-parameters-database-engine?view=sql-server-ver16).

## EF Core Simple Approach

Let's start with a simple example using EF Core.
We're creating an `ApplicationDbContext` instance, adding a `User` object, and calling `SaveChangesAsync`.
This will insert each record to the database one by one.
In other words, each record requires one round trip to the database.

```csharp
using var context = new ApplicationDbContext();

foreach (var user in GetUsers())
{
    context.Users.Add(user);

    await context.SaveChangesAsync();
}
```

The results are as poor as you'd expect:

```
EF Core - Add one and save, for 100 users: 20 ms
EF Core - Add one and save, for 1,000 users: 260 ms
EF Core - Add one and save, for 10,000 users: 8,860 ms
```

I omitted the results with `100,000` and `1,000,000` records because they took too long to execute.

We'll use this as a "how not to do bulk inserts" example.

## Dapper Simple Insert

[Dapper](https://github.com/DapperLib/Dapper) is a simple SQL-to-object mapper for .NET.
It allows us to easily insert a collection of objects into the database.

I'm using Dapper's feature to unwrap a collection into a SQL `INSERT` statement.

```csharp
using var connection = new SqlConnection(connectionString);
connection.Open();

const string sql =
    @"
    INSERT INTO Users (Email, FirstName, LastName, PhoneNumber)
    VALUES (@Email, @FirstName, @LastName, @PhoneNumber);
    ";

await connection.ExecuteAsync(sql, GetUsers());
```

The results are much better than the initial example:

```
Dapper - Insert range, for 100 users: 10 ms
Dapper - Insert range, for 1,000 users: 113 ms
Dapper - Insert range, for 10,000 users: 1,028 ms
Dapper - Insert range, for 100,000 users: 10,916 ms
Dapper - Insert range, for 1,000,000 users: 109,065 ms
```

## EF Core Add and Save

However, EF Core still didn't throw in the towel.
The first example was poorly implemented on purpose.
EF Core can batch multiple SQL statements together, so let's use that.

If we make a simple change, we can get significantly better performance.
First, we're adding all the objects to the `ApplicationDbContext`.
Then, we're going to call `SaveChangesAsync` only once.

EF will create a batched SQL statement - group many `INSERT` statements together - and send them to the database together.
This reduces the number of round trips to the database, giving us improved performance.

```csharp
using var context = new ApplicationDbContext();

foreach (var user in GetUsers())
{
    context.Users.Add(user);
}

await context.SaveChangesAsync();
```

Here are the benchmark results of this implementation:

```
EF Core - Add all and save, for 100 users: 2 ms
EF Core - Add all and save, for 1,000 users: 18 ms
EF Core - Add all and save, for 10,000 users: 203 ms
EF Core - Add all and save, for 100,000 users: 2,129 ms
EF Core - Add all and save, for 1,000,000 users: 21,557 ms
```

Remember, it took Dapper **109 seconds** to insert `1,000,000` records.
We can achieve the same with EF Core batched queries in **~21 seconds**.

## EF Core AddRange and Save

This is an alternative to the previous example.
Instead of calling `Add` for all objects, we can call `AddRange` and pass in a collection.

I wanted to show this implementation because I prefer it over the previous one.

```csharp
using var context = new ApplicationDbContext();

context.Users.AddRange(GetUsers());

await context.SaveChangesAsync();
```

The results are very similar to the previous example:

```
EF Core - Add range and save, for 100 users: 2 ms
EF Core - Add range and save, for 1,000 users: 18 ms
EF Core - Add range and save, for 10,000 users: 204 ms
EF Core - Add range and save, for 100,000 users: 2,111 ms
EF Core - Add range and save, for 1,000,000 users: 21,605 ms
```

## EF Core Bulk Extensions

There's an awesome library called [EF Core Bulk Extensions](https://github.com/borisdj/EFCore.BulkExtensions) that we can use to squeeze out more performance.
You can do a lot more than bulk inserts with this library, so I recommend exploring it.
This library is open source, and has a community license if you meet the free usage criteria.
Check the [licensing section](https://github.com/borisdj/EFCore.BulkExtensions?#license) for more details.

For our use case, the `BulkInsertAsync` method is an excellent choice.
We can pass the collection of objects, and it will perform an SQL bulk insert.

```csharp
using var context = new ApplicationDbContext();

await context.BulkInsertAsync(GetUsers());
```

The performance is equally amazing:

```
EF Core - Bulk Extensions, for 100 users: 1.9 ms
EF Core - Bulk Extensions, for 1,000 users: 8 ms
EF Core - Bulk Extensions, for 10,000 users: 76 ms
EF Core - Bulk Extensions, for 100,000 users: 742 ms
EF Core - Bulk Extensions, for 1,000,000 users: 8,333 ms
```

For comparison, we needed **~21 seconds** to insert `1,000,000` records with EF Core batched queries.
We can do the same with the [Bulk Extensions](https://github.com/borisdj/EFCore.BulkExtensions) library in just **8 seconds**.

## SQL Bulk Copy

Lastly, if we can't get the desired performance from EF Core, we can try using [`SqlBulkCopy`](https://learn.microsoft.com/en-us/dotnet/api/system.data.sqlclient.sqlbulkcopy).
SQL Server supports [bulk copy operations](https://learn.microsoft.com/en-us/dotnet/framework/data/adonet/sql/bulk-copy-operations-in-sql-server) natively, so let's use this.

This implementation is slightly more complex than the EF Core examples.
We need to configure the `SqlBulkCopy` instance and create a `DataTable` containing the objects we want to insert.

```csharp
using var bulkCopy = new SqlBulkCopy(ConnectionString);

bulkCopy.DestinationTableName = "dbo.Users";

bulkCopy.ColumnMappings.Add(nameof(User.Email), "Email");
bulkCopy.ColumnMappings.Add(nameof(User.FirstName), "FirstName");
bulkCopy.ColumnMappings.Add(nameof(User.LastName), "LastName");
bulkCopy.ColumnMappings.Add(nameof(User.PhoneNumber), "PhoneNumber");

await bulkCopy.WriteToServerAsync(GetUsersDataTable());
```

However, the performance is blazing fast:

```
SQL Bulk Copy, for 100 users: 1.7 ms
SQL Bulk Copy, for 1,000 users: 7 ms
SQL Bulk Copy, for 10,000 users: 68 ms
SQL Bulk Copy, for 100,000 users: 646 ms
SQL Bulk Copy, for 1,000,000 users: 7,339 ms
```

Here's how you can create a `DataTable` and populate it with a list of objects:

```csharp
DataTable GetUsersDataTable()
{
    var dataTable = new DataTable();

    dataTable.Columns.Add(nameof(User.Email), typeof(string));
    dataTable.Columns.Add(nameof(User.FirstName), typeof(string));
    dataTable.Columns.Add(nameof(User.LastName), typeof(string));
    dataTable.Columns.Add(nameof(User.PhoneNumber), typeof(string));

    foreach (var user in GetUsers())
    {
        dataTable.Rows.Add(
            user.Email, user.FirstName, user.LastName, user.PhoneNumber);
    }

    return dataTable;
}
```

## Results

Here are the results for all the bulk insert implementations:

```
| Method             |   Size     |      Speed
|------------------- |----------- |----------------:
| EF_OneByOne        | 100        |      19.800 ms |
| EF_OneByOne        | 1000       |     259.870 ms |
| EF_OneByOne        | 10000      |   8,860.790 ms |
| EF_OneByOne        | 100000     |            N/A |
| EF_OneByOne        | 1000000    |            N/A |

| Dapper_Insert      | 100        |      10.650 ms |
| Dapper_Insert      | 1000       |     113.137 ms |
| Dapper_Insert      | 10000      |   1,027.979 ms |
| Dapper_Insert      | 100000     |  10,916.628 ms |
| Dapper_Insert      | 1000000    | 109,064.815 ms |

| EF_AddAll          | 100        |       2.064 ms |
| EF_AddAll          | 1000       |      17.906 ms |
| EF_AddAll          | 10000      |     202.975 ms |
| EF_AddAll          | 100000     |   2,129.370 ms |
| EF_AddAll          | 1000000    |  21,557.136 ms |

| EF_AddRange        | 100        |       2.035 ms |
| EF_AddRange        | 1000       |      17.857 ms |
| EF_AddRange        | 10000      |     204.029 ms |
| EF_AddRange        | 100000     |   2,111.106 ms |
| EF_AddRange        | 1000000    |  21,605.668 ms |

| BulkExtensions     | 100        |       1.922 ms |
| BulkExtensions     | 1000       |       7.943 ms |
| BulkExtensions     | 10000      |      76.406 ms |
| BulkExtensions     | 100000     |     742.325 ms |
| BulkExtensions     | 1000000    |   8,333.950 ms |

| BulkCopy           | 100        |       1.721 ms |
| BulkCopy           | 1000       |       7.380 ms |
| BulkCopy           | 10000      |      68.364 ms |
| BulkCopy           | 100000     |     646.219 ms |
| BulkCopy           | 1000000    |   7,339.298 ms |
```

## Takeaway

`SqlBulkCopy` holds the crown for maximum raw speed.
However, [EF Core Bulk Extensions](https://github.com/borisdj/EFCore.BulkExtensions) deliver fantastic performance while maintaining the ease of use that Entity Framework Core is known for.

The best choice hinges on your project's specific demands:

- Performance is all that matters? `SqlBulkCopy` is your solution.
- Need excellent speed and streamlined development? EF Core is a smart choice.

I leave it up to you to decide which option is best for your use case.

Hope this was helpful.

See you next week.

---



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To delete or not to delete, that is the question (pun intended).

The traditional way to remove information in a database is through a "hard delete."
A hard delete permanently erases a record from the database table.
While this seems straightforward, it presents a significant risk: once that data is gone, it's gone for good.

Instead of physically removing a record, a soft delete marks it as deleted, usually by setting a flag like `IsDeleted` to `true`.
The record remains in the database, but it's effectively hidden from regular application queries.

Today, we'll dive into the details of how to implement soft deletes using EF Core.
We'll discuss global query filters, explore efficient ways to handle soft-deleted data, and weigh the trade-offs.

## What Is a Soft Delete?

A soft delete is a data persistence strategy that prevents the permanent deletion of records from your database.
Instead of removing data from the database, a flag is set on the record, indicating it as "deleted."

This approach allows the application to ignore these records during normal queries.
However, you can restore these records if necessary.
Soft delete is also practical if you want to keep foreign key constraints in place.
Soft delete is a "non-destructive" operation in contrast with hard delete, where data is completely removed from the database.

A hard delete uses the SQL `DELETE` statement:

```sql
DELETE FROM bookings.Reviews
WHERE Id = @BookingId;
```

A soft delete, on the other hand, uses an `UPDATE` statement:

```sql
UPDATE bookings.Reviews
SET IsDeleted = 1, DeletedOnUtc = @UtcNow
WHERE Id = @BookingId;
```

The data is still present in the database, and the operation can be undone.

But you need to remember to filter out soft-deleted data when querying the database:

```sql
SELECT *
FROM bookings.Reviews
WHERE IsDeleted = 0;
```

Let's see how we can implement soft delete with [EF Core](https://learn.microsoft.com/en-us/ef/core/).

## Soft Deletes Using EF Core Interceptors

[EF Core interceptors](https://learn.microsoft.com/en-us/ef/core/logging-events-diagnostics/interceptors)
provide a powerful mechanism for intercepting and modifying database operations.
For example, you can intercept the saving changes operation to implement soft delete functionality.

Let's create an `ISoftDeletable` marker interface to represent soft-deletable entities:

```csharp
public interface ISoftDeletable
{
    bool IsDeleted { get; set; }

    DateTime? DeletedOnUtc { get; set; }
}
```

The entities that should support soft delete will implement this interface.
You will need to apply the respective database migration to create these columns.

The next component we need is a `SaveChangesInterceptor`, which allows us to hook into the `SavingChangesAsync` (or `SavingChanges`) method.
We can access the `ChangeTracker` and look for entries that implement `ISoftDeletable` and are flagged for deletion.
We can figure this out by checking if the entity state is `EntityState.Deleted`.

When we find the entities flagged for deletion, we loop through them and update their state to `EntityState.Modified`.
You should also set the respective values for the `IsDeleted` and `DeletedOnUtc` properties.
This will cause EF to generate an `UPDATE` operation instead of a `DELETE` operation.

```csharp
public sealed class SoftDeleteInterceptor : SaveChangesInterceptor
{
    public override ValueTask<InterceptionResult<int>> SavingChangesAsync(
        DbContextEventData eventData,
        InterceptionResult<int> result,
        CancellationToken cancellationToken = default)
    {
        if (eventData.Context is null)
        {
            return base.SavingChangesAsync(
                eventData, result, cancellationToken);
        }

        IEnumerable<EntityEntry<ISoftDeletable>> entries =
            eventData
                .Context
                .ChangeTracker
                .Entries<ISoftDeletable>()
                .Where(e => e.State == EntityState.Deleted);

        foreach (EntityEntry<ISoftDeletable> softDeletable in entries)
        {
            softDeletable.State = EntityState.Modified;
            softDeletable.Entity.IsDeleted = true;
            softDeletable.Entity.DeletedOnUtc = DateTime.UtcNow;
        }

        return base.SavingChangesAsync(eventData, result, cancellationToken);
    }
}
```

This approach ensures that all delete operations across the application respect the soft delete policy.

You'll need to register the `SoftDeleteInterceptor` with dependency injection and configure it with the `ApplicationDbContext`.

```csharp
services.AddSingleton<SoftDeleteInterceptor>();

services.AddDbContext<ApplicationDbContext>(
    (sp, options) => options
        .UseSqlServer(connectionString)
        .AddInterceptors(
            sp.GetRequiredService<SoftDeleteInterceptor>()));
```

If you want to learn more, here's an article with a few practical use cases for [EF Core interceptors](how-to-use-ef-core-interceptors).

## Automatically Filtering Soft-Deleted Data

To ensure that soft-deleted records are automatically excluded from queries, we can use [EF Core global query filters](how-to-use-global-query-filters-in-ef-core).
We can apply query filters to entities using the `OnModelCreating` method to automatically exclude records marked as deleted.
This feature dramatically simplifies writing queries.

Here's how to configure the soft delete query filter:

```csharp {8}
public sealed class ApplicationDbContext(
    DbContextOptions<UsersDbContext> options) : DbContext(options)
{
    public DbSet<Review> Reviews { get; set; }

    protected override void OnModelCreating(ModelBuilder modelBuilder)
    {
        modelBuilder.Entity<Review>().HasQueryFilter(r => !r.IsDeleted);
    }
}
```

A limitation is you can't have more than one query filter configured per entity.

However, it's sometimes useful to explicitly include soft-deleted records.
You can achieve this using the `IgnoreQueryFilters` method.

```csharp {2}
dbContex.Reviews
    .IgnoreQueryFilters()
    .Where(r => r.ApartmentId == apartmentId)
    .ToList();
```

## Faster Queries Using Filtered Index

To enhance query performance, especially in tables with a significant number of soft-deleted records, you can use **filtered indexes**.
A filtered index only includes records that meet the specified criteria.
This reduces the index size and improves query execution times for operations that exclude filtered records.
Most popular databases support filtered indexes.

Here's how you can configure a [filtered index with EF Core](https://learn.microsoft.com/en-us/ef/core/modeling/indexes?tabs=data-annotations#index-filter):

```csharp {10-12}
public sealed class ApplicationDbContext(
    DbContextOptions<UsersDbContext> options) : DbContext(options)
{
    public DbSet<Review> Reviews { get; set; }

    protected override void OnModelCreating(ModelBuilder modelBuilder)
    {
        modelBuilder.Entity<Review>().HasQueryFilter(r => !r.IsDeleted);

        modelBuilder.Entity<Review>()
            .HasIndex(r => r.IsDeleted)
            .HasFilter("IsDeleted = 0");
    }
}
```

The `HasFilter` method accepts the SQL filter for records that will be included in the index.

You can also create a filtered index using SQL:

```sql
CREATE INDEX IX_Reviews_IsDeleted
ON bookings.Reviews (IsDeleted)
WHERE IsDeleted = 0;
```

You can learn more about filtered indexes from the documentation:

- [SQL Server filtered index](https://learn.microsoft.com/en-us/sql/relational-databases/indexes/create-filtered-indexes?view=sql-server-ver16)
- [PostgreSQL partial index](https://www.postgresql.org/docs/16/indexes-partial.html)

## Do You Really Need Soft Deletes?

It's worthwhile to think through if you even need to soft delete records.

In enterprise systems, you're typically not thinking about "deleting" data.
There are business concepts that don't involve deleting data.
A few examples are canceling an order, refunding a payment, or voiding an invoice.
These "destructive" operations return the system to a previous state.
But from a business perspective, you aren't really deleting data.

Soft deletes are helpful if there is a risk of accidental deletion.
They allow you to easily restore soft-deleted records.

In any case, consider if soft deletes make sense from a business perspective.

## Takeaway

Soft deletes offer a valuable safety net for data recovery and can enhance historical data tracking.
However, it's crucial to assess whether they truly align with your application's specific requirements.
Consider factors like the importance of deleted data recovery, any auditing needs, and your industry's regulations.
Creating a filtered index can improve query performance on tables with soft-deleted records.

If you decide that soft deletes are a good fit, EF Core provides the tools necessary for a streamlined implementation.

Thanks for reading, and I'll see you next week!

---



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I've worked with many different software architectures over the years.

There's one that clearly stands out for its benefits: **Modular Monolith architecture**.

Modular monoliths blend the simplicity and robustness of traditional monolithic applications with the flexibility and scalability of microservices.
I'm tempted to say they bring together the best of both worlds.

The modular monolith architecture allows you to work in a unified codebase with clearly defined boundaries and independent modules.
You can have a high development velocity without the complexity of distributed systems.

Today, I'll introduce you to the modular monolith architecture and why you should know about it.

## What is a Modular Monolith?

A **modular monolith** is an architectural pattern that structures the application into independent modules or components with well-defined boundaries.
The modules are split based on logical boundaries, grouping together related functionalities.
This approach significantly improves the cohesion of the system.

The modules are loosely coupled, which further promotes modularity and separation of concerns.
Modules communicate through a public API, and you can learn more about this in my article on [modular monolith communication patterns](modular-monolith-communication-patterns).

But what are the benefits of a modular design?

<Image
  src="blogs/mnw_080/modular_monolith.png"
  width={2934}
  height={1554}
  alt="Modular monolith."
/>

If we take the example of an apartment booking system illustrated above.
During the holiday season, the system is expecting a traffic spike.
The bookings and payments modules need to scale so they can be deployed independently.
At the end of the holiday season, they can be merged back into a single deployment.
Modular monoliths give you this kind of flexibility.

## Modular Architecture

Modular monoliths introduce a few important technical challenges that we will need to solve.

To achieve a modular architecture, the modules:

- Must be independent and interchangeable
- Must be able to provide the required functionality
- Must have a well-defined interface exposed to other modules

Is it possible for a module to be completely independent?
Not really.
That would mean it's not integrated with other modules.
We want loosely coupled modules and to keep the number of dependencies low.
We can use a few techniques to keep the modules independent, and having good [data isolation](modular-monolith-data-isolation) is one example.

Another factor you need to consider is how strong the dependency is.
If two modules are very "chatty", you might have incorrectly defined the boundaries.
You should consider merging these modules together.

Remember, a module is a grouping of related functionalities accessed via a [well-defined interface](modular-monolith-communication-patterns).

Having a modular architecture allows you to easily extract modules into separate services.

## Monolith First

Microservices have become the most popular architectural pattern in recent years, and for good reason.
Microservices offer many benefits like clearly defined service boundaries, independent deployments, independent scalability, and much more.

However, most teams would be better off starting with a monolith application.

A monolith is an architectural pattern where all components are deployed as a single physical deployment unit.

Here's an interesting quote from Martin Fowler:

> You shouldn't start a new project with microservices, even if you're sure your application will be big enough to make it worthwhile.

_&mdash; [Martin Fowler](https://martinfowler.com/bliki/MonolithFirst.html)_

And I wholeheartedly agree with this. Better yet, consider starting with a modular monolith.

Even Google is jumping on board the modular monolith trend in their recent research paper, [Towards Modern Development of Cloud Applications](https://dl.acm.org/doi/pdf/10.1145/3593856.3595909).

Here are the five main challenges Google identified with microservices:

- **Performance** - The overhead of serializing data and sending it across the network has a noticeable impact on performance.
- **Correctness** - It's difficult to reason about the correctness of a distributed system when there are many interactions between components.
- **Management** - We have to manage multiple different applications, each with its release schedule.
- **Frozen APIs** - Once an API is established, it becomes hard to change without breaking any existing API consumers.
- **Development speed** - Making a change in one microservice may affect many other microservices, which requires carefully planning deployments.

When you factor in the complexity of distributed systems, starting with a modular monolith becomes increasingly compelling.
I also recommend reading about the [fallacies of distributed computing](https://en.wikipedia.org/wiki/Fallacies_of_distributed_computing) if you're unfamiliar with them.

Well-defined, in-process components (modules) can be an excellent stepping stone to out-of-process components (services).

## Benefits of a Modular Monolith

Modular monoliths have many benefits. So, I want to highlight a few that I consider important:

- **Simplified deployment** - Unlike microservices, which require complex deployment strategies, a modular monolith can be deployed as a single unit.
- **Improved performance** - Communication between modules occurs in-process. This means that there's no network latency or data serialization/deserialization overhead.
- **Enhanced development velocity** - There's a single codebase to manage, simplifying debugging and the overall development experience.
- **Easier transaction management** - Managing transactions in a distributed system is very challenging. Modular monoliths simplify this since modules can share the same database.
- **Lower operational complexity** - Modular monoliths reduce the operational overhead that comes with managing and deploying a distributed microservices system.
- **Easier transition to Microservices** - A well-structured modular monolith offers a clear path to a microservices architecture.
  You can gradually [extract modules into separate services](monolith-to-microservices-how-a-modular-monolith-helps) when the need arises.

## Modular Monolith vs Microservices

The biggest difference between modular monoliths and microservices is how they're deployed.
Microservices elevate the logical boundaries inside a modular monolith into physical boundaries.

Microservices give you a clear strategy for modularity and decomposing the bounded contexts.
But, you can also achieve this without building a distributed system.
The problem is people end up using microservices to enforce code boundaries.

<Image
  src="blogs/mnw_080/modular_monolith_vs_microservices.png"
  width={2828}
  height={1592}
  alt="Modular monolith vs. microservices."
/>

Instead, you can build a modular monolith to get most of the same benefits.
Modular monoliths give you high cohesion, low coupling, data encapsulation, focus on business functionalities, and more.

Microservices give you all that, plus independent deployments, independent scalability, and the ability to use different technology stacks per service.

> Choose microservices for the benefits, not because your monolithic codebase is a mess.

_&mdash; [Simon Brown](https://twitter.com/simonbrown)_

## Next Steps

Modular monoliths offer a compelling way to structure applications.
They balance the benefits of well-organized code, scalability potential, and a smooth path for transitioning to microservices if needed.
If you want to improve the maintainability and adaptability of your software, consider exploring modular monoliths.

Want to dive deeper into modular monoliths? Check out these resources:

- [Modular Monolith Communication Patterns](modular-monolith-communication-patterns)
- [Modular Monolith Data Isolation](modular-monolith-data-isolation)
- [Monolith to Microservices: How a Modular Monolith Helps](monolith-to-microservices-how-a-modular-monolith-helps)
- [Modular Monoliths: How To Build One & Lessons Learned](https://youtu.be/Xo3rsiZYsJQ)
- [How to Structure a Modular Monolith Project in .NET](https://youtu.be/z3piPJ7x4WU)
- [Getting Started with Modular Monoliths in .NET](https://youtu.be/5dilYMii9T4)
- [Modular Monolith Architecture course](/modular-monolith-architecture)

That's all for today. Stay awesome, and I'll see you next week.

---



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Suppose you're building a modular monolith, a type of software architecture where different components are organized into loosely coupled modules.
Or you might need to process data asynchronously.
You'll need a tool or service that allows you to implement this.

Messaging plays a crucial role in modern software architecture, enabling communication and coordination between loosely coupled components.

An in-memory message bus is particularly useful when high performance and low latency are critical requirements.

In today's issue, we will:

- Create the required messaging abstractions
- Build an in-memory message bus using channels
- Implement an integration event processor background job
- Demonstrate how to publish and consume messages asynchronously

Let's dive in.

## When To Use an In-Memory Message Bus

I have to preface this by saying that an in-memory message bus is far from a silver bullet.
There are many caveats to using it, as you will soon learn.

But first, let's start with the pros of using an in-memory message bus:

- Because it works in memory, you have a very low-latency messaging system
- You can implement asynchronous (non-blocking) communication between components

However, there are a few drawbacks to this approach:

- Potential for losing messages if the application process goes down
- It only works inside of a single process, so it's not useful in distributed systems

A practical use case for an in-memory message bus is when building a modular monolith.
You can implement [communication between modules](modular-monolith-communication-patterns) using integration events.
When you need to extract some modules into a separate service, you can replace the in-memory bus with a distributed one.

## Defining The Messaging Abstractions

We will need a few abstractions to build our simple messaging system.
From the client's perspective, we really only need two things.
One abstraction is to publish messages, and another is to define a message handler.

The `IEventBus` interface exposes the `PublishAsync` method.
This is what we will use to publish messages.
There's also a generic constraint defined that only allows passing in an `IIntegrationEvent` instance.

```csharp
public interface IEventBus
{
    Task PublishAsync<T>(
        T integrationEvent,
        CancellationToken cancellationToken = default)
        where T : class, IIntegrationEvent;
}
```

I want to be practical with the `IIntegrationEvent` abstraction, so I'll use [MediatR](https://github.com/jbogard/MediatR) for the pub-sub support.
The `IIntegrationEvent` interface will inherit from `INotification`.
This allows us to easily define `IIntegrationEvent` handlers using `INotificationHandler<T>`.
Also, the `IIntegrationEvent` has an identifier, so we can track its execution.

The abstract `IntegrationEvent` serves as a base class for concrete implementations.

```csharp
using MediatR;

public interface IIntegrationEvent : INotification
{
    Guid Id { get; init; }
}

public abstract record IntegrationEvent(Guid Id) : IIntegrationEvent;
```

## Simple In-Memory Queue Using Channels

The `System.Threading.Channels` namespace provides data structures for asynchronously passing messages between producers and consumers.
Channels implement the [producer/consumer pattern](https://en.wikipedia.org/wiki/Producer%E2%80%93consumer_problem).
Producers asynchronously produce data, and consumers asynchronously consume that data.
It's an essential pattern for building loosely coupled systems.

One of the primary motivations behind the adoption of [.NET Channels](https://learn.microsoft.com/en-us/dotnet/core/extensions/channels)
is their exceptional performance characteristics.
Unlike traditional message queues, Channels operate entirely in memory.
This has the disadvantage of the potential for message loss if the application crashes.

The `InMemoryMessageQueue` creates an unbounded channel using the `Channel.CreateUnbounded` bounded.
This means the channel can have any number of readers and writers.
It also exposes a `ChannelReader` and `ChannelWriter`, which allow consumers to publish and consume messages.

```csharp
internal sealed class InMemoryMessageQueue
{
    private readonly Channel<IIntegrationEvent> _channel =
        Channel.CreateUnbounded<IIntegrationEvent>();

    public ChannelReader<IIntegrationEvent> Reader => _channel.Reader;

    public ChannelWriter<IIntegrationEvent> Writer => _channel.Writer;
}
```

You also need to register the `InMemoryMessageQueue` as a singleton with dependency injection:

```csharp
builder.Services.AddSingleton<InMemoryMessageQueue>();
```

## Implementing The Event Bus

The `IEventBus` implementation is now straightforward with the use of channels.
The `EventBus` class uses the `InMemoryMessageQueue` to access the `ChannelWriter` and write an event to the channel.

```csharp
internal sealed class EventBus(InMemoryMessageQueue queue) : IEventBus
{
    public async Task PublishAsync<T>(
        T integrationEvent,
        CancellationToken cancellationToken = default)
        where T : class, IIntegrationEvent
    {
        await queue.Writer.WriteAsync(integrationEvent, cancellationToken);
    }
}
```

We will register the `EventBus` as a singleton service with dependency injection because it's stateless:

```csharp
builder.Services.AddSingleton<IEventBus, EventBus>();
```

## Consuming Integration Events

With the `EventBus` implementing the producer, we need a way to consume the published `IIntegrationEvent`.
We can implement a simple [background service](running-background-tasks-in-asp-net-core) using the built-in `IHostedService` abstraction.

The `IntegrationEventProcessorJob` depends on the `InMemoryMessageQueue`, but this time for reading (consuming) messages.
We'll use the `ChannelReader.ReadAllAsync` method to get back an `IAsyncEnumerable`.
This allows us to consume all the messages in the `Channel` asynchronously.

The `IPublisher` from MediatR helps us connect the `IIntegrationEvent` with the respective handlers.
It's important to resolve it from a custom scope if you want to inject scoped services into the event handlers.

```csharp
internal sealed class IntegrationEventProcessorJob(
    InMemoryMessageQueue queue,
    IServiceScopeFactory serviceScopeFactory,
    ILogger<IntegrationEventProcessorJob> logger)
    : BackgroundService
{
    protected override async Task ExecuteAsync(CancellationToken stoppingToken)
    {
        await foreach (IIntegrationEvent integrationEvent in
            queue.Reader.ReadAllAsync(stoppingToken))
        {
            try
            {
                using IServiceScope scope = serviceScopeFactory.CreateScope();

                IPublisher publisher = scope.ServiceProvider
                    .GetRequiredService<IPublisher>();

                await publisher.Publish(integrationEvent, stoppingToken);
            }
            catch (Exception ex)
            {
                logger.LogError(
                    ex,
                    "Something went wrong! {IntegrationEventId}",
                    integrationEvent.Id);
            }
        }
    }
}
```

Don't forget to register the hosted service:

```csharp
builder.Services.AddHostedService<IntegrationEventProcessorJob>();
```

## Using The In-Memory Message Bus

With all of the necessary abstractions in place, we can finally use the in-memory message bus.

The `IEventBus` service will write the message to the `Channel` and immediately return.
This allows you to publish messages in a non-blocking way, which can improve performance.

```csharp {3, 16-19}
internal sealed class RegisterUserCommandHandler(
    IUserRepository userRepository,
    IEventBus eventBus)
    : ICommandHandler<RegisterUserCommand>
{
    public async Task<User> Handle(
        RegisterUserCommand command,
        CancellationToken cancellationToken)
    {
        // First, register the user.
        User user = CreateFromCommand(command);

        userRepository.Insert(user);

        // Now we can publish the event.
        await eventBus.PublishAsync(
            new UserRegisteredIntegrationEvent(user.Id),
            cancellationToken);

        return user;
    }
}
```

This solves the producer side, but we also need to create a consumer for the `UserRegisteredIntegrationEvent` message.
This part is greatly simplified because I'm using MediatR in this implementation.

We need to define an `INotificationHandler` implementation handling the integration event `UserRegisteredIntegrationEvent`.
This will be the `UserRegisteredIntegrationEventHandler`.

When the background job reads the `UserRegisteredIntegrationEvent` from the `Channel`, it will publish the message and execute the handler.

```csharp
internal sealed class UserRegisteredIntegrationEventHandler
    : INotificationHandler<UserRegisteredIntegrationEvent>
{
    public async Task Handle(
        UserRegisteredIntegrationEvent event,
        CancellationToken cancellationToken)
    {
        // Asynchronously handle the event.
    }
}
```

## Improvement Points

While our basic in-memory message bus is functional, there are several areas we can improve:

- **Resilience** - We can introduce retries when we run into exceptions, which will improve the reliability of the message bus.
- **Idempotency** - Ask yourself if you want to handle the same message twice.
  The [idempotent consumer pattern](idempotent-consumer-handling-duplicate-messages) elegantly solves this problem.
- **Dead Letter Queue** - Sometimes, we won't be able to handle a message correctly.
  It's a good idea to introduce a persistent storage for these messages.
  This is called a [Dead Letter Queue](https://aws.amazon.com/what-is/dead-letter-queue/), and it allows for troubleshooting at a later time.

We've covered the key aspects of building an in-memory message bus using .NET Channels.
You can extend this further by implementing the improvements for a more robust solution.

Remember that this implementation only works inside of one process.
Consider using a real message broker if you need a more reliable solution.

That's all for today. I'll see you next week.

---



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In ASP.NET Core applications using [Minimal APIs](https://learn.microsoft.com/en-us/aspnet/core/fundamentals/minimal-apis/overview?view=aspnetcore-8.0),
registering each API endpoint with `app.MapGet`, `app.MapPost`, etc., can introduce repetitive code.
As projects grow, this manual process becomes increasingly time-consuming and prone to maintenance headaches.

You can try grouping the Minimal API endpoints using extension methods so as not to clutter the `Program` file.
This approach scales well as the project grows.
However, it feels like reinventing controllers.

I like to view each Minimal API endpoint as a standalone component.

The vision I have in my mind aligns nicely with the concept of [vertical slices.](vertical-slice-architecture)

Today, I'll show you how to register your Minimal APIs automatically with a simple abstraction.

## The Endpoint Comes First

Automatically registering Minimal APIs significantly reduces boilerplate, streamlining development.
It makes your codebase more concise and improves maintainability by establishing a centralized registration mechanism.

Let's create a simple `IEndpoint` abstraction to represent a single endpoint.

The `MapEndpoint` accepts an `IEndpointRouteBuilder`, which we can use to call `MapGet`, `MapPost`, etc.

```csharp
public interface IEndpoint
{
    void MapEndpoint(IEndpointRouteBuilder app);
}
```

Each `IEndpoint` implementation should contain exactly one Minimal API endpoint definition.

Nothing prevents you from registering multiple endpoints in the `MapEndpoint` method.
But you (really) shouldn't.

Additionally, you could implement a code analyzer or [architecture test](enforcing-software-architecture-with-architecture-tests) to enforce this rule.

```csharp
public class GetFollowerStats : IEndpoint
{
    public void MapEndpoint(IEndpointRouteBuilder app)
    {
        app.MapGet("users/{userId}/followers/stats", async (
            Guid userId,
            ISender sender) =>
        {
            var query = new GetFollowerStatsQuery(userId);

            Result<FollowerStatsResponse> result = await sender.Send(query);

            return result.Match(Results.Ok, CustomResults.Problem);
        })
        .WithTags(Tags.Users);
    }
}
```

## Sprinkle Some Reflection Magic

Reflection allows us to dynamically examine code at runtime.
For Minimal API registration, we'll use reflection to scan our .NET assemblies and find classes that implement `IEndpoint`.
Then, we will configure them as services with dependency injection.

The `Assembly` parameter should be the assembly that contains the `IEndpoint` implementations.
If you want to have endpoints in multiple assemblies (projects), you can easily extend this method to accept a collection.

```csharp
public static IServiceCollection AddEndpoints(
    this IServiceCollection services,
    Assembly assembly)
{
    ServiceDescriptor[] serviceDescriptors = assembly
        .DefinedTypes
        .Where(type => type is { IsAbstract: false, IsInterface: false } &&
                       type.IsAssignableTo(typeof(IEndpoint)))
        .Select(type => ServiceDescriptor.Transient(typeof(IEndpoint), type))
        .ToArray();

    services.TryAddEnumerable(serviceDescriptors);

    return services;
}
```

We only need to call this method once from the `Program` file:

```csharp
builder.Services.AddEndpoints(typeof(Program).Assembly);
```

## Registering Minimal APIs

The final step in our implementation is to register the endpoints automatically.
We can create an extension method on the `WebApplication`, which lets us resolve services using the `IServiceProvider`.

We're looking for all registrations of the `IEndpoint` service.
These will be the endpoint classes we can now register with the application by calling `MapEndpoint`.

I'm also adding an option to pass in a `RouteGroupBuilder` if you want to apply conventions to all endpoints.
A great example is adding a route prefix, authentication, or [API versioning.](api-versioning-in-aspnetcore)

```csharp
public static IApplicationBuilder MapEndpoints(
    this WebApplication app,
    RouteGroupBuilder? routeGroupBuilder = null)
{
    IEnumerable<IEndpoint> endpoints = app.Services
        .GetRequiredService<IEnumerable<IEndpoint>>();

    IEndpointRouteBuilder builder =
        routeGroupBuilder is null ? app : routeGroupBuilder;

    foreach (IEndpoint endpoint in endpoints)
    {
        endpoint.MapEndpoint(builder);
    }

    return app;
}
```

## Putting It All Together

Here's what the `Program` file could look like when we put it all together.

We're calling `AddEndpoints` to register the `IEndpoint` implementations.

Then, we're calling `MapEndpoints` to automatically register the Minimal APIs.

I'm also configuring a route prefix and API Versioning for each endpoint using a `RouteGroupBuilder`.

```csharp {6,19}
WebApplicationBuilder builder = WebApplication.CreateBuilder(args);

builder.Services.AddEndpointsApiExplorer();
builder.Services.AddSwaggerGen();

builder.Services.AddEndpoints(typeof(Program).Assembly);

WebApplication app = builder.Build();

ApiVersionSet apiVersionSet = app.NewApiVersionSet()
    .HasApiVersion(new ApiVersion(1))
    .ReportApiVersions()
    .Build();

RouteGroupBuilder versionedGroup = app
    .MapGroup("api/v{version:apiVersion}")
    .WithApiVersionSet(apiVersionSet);

app.MapEndpoints(versionedGroup);

app.Run();
```

## Takeaway

Automatic Minimal API registration with techniques like reflection can significantly improve developer efficiency and project maintainability.

While highly beneficial, it's important to acknowledge the potential **performance impact of reflection** on application startup.

So, an improvement point could be using source generators for pre-compiled registration logic.

A few alternatives worth exploring:

- [Extension methods](how-to-structure-minimal-apis)
- [FastEndpoints](https://fast-endpoints.com/)
- [Carter](https://github.com/CarterCommunity/Carter)

Hope this was helpful.

See you next week.

**P.S.** Here's the complete [source code](https://github.com/m-jovanovic/minimal-endpoints) for this article.

---


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