Skip to main content

API Design in ASP.NET Core Part I

Today I had the honor to give a training to some of the newly started young professionals in our organisation. The topic of the training was API design in ASP.NET Core. During this training we discussed multiple topics and a lot of interesting questions were raised. I'll try to tackle some of them with a blog post.

Let's start with a first question and immediatelly a controversial one...

What is REST?

Let's first have a look at what Wikipedia has to say about REST:

Representational state transfer (REST) is a software architectural style that describes a uniform interface between physically separate components, often across the Internet in a Client-Server architecture.

It was originally introduced in a dissertation by Roy Fielding titled 'Architectural Styles and the Design of Network-based Software Architectures’. Here is the link if you like to read it: https://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm.

What is most important to understand is that REST is an architecture style, NOT a standard.

Let’s walk through the six constraints:

Client–server architecture

In the REST architectural style, the implementation of the client and the implementation of the server can be done independently without each knowing about the other. This means that the code on the client side can be changed at any time without affecting the operation of the server, and the code on the server side can be changed without affecting the operation of the client.

As long as each side knows what format of messages to send to the other, they can be kept separate.

Statelessness

Systems that follow the REST paradigm are stateless, meaning that the server does not need to know anything about what state the client is in and vice versa. In this way, both the server and the client can understand any message received, even without seeing previous messages. This constraint of statelessness is enforced through the use of resources, rather than commands. Resources are the nouns of the Web - they describe any object, document, or thing that you may need to store or send to other services.

Cacheability

When possible, resources should be cacheable on the client or server side. Server responses also need to contain information about whether caching is allowed for the delivered resource. The goal is to improve performance on the client side, while increasing scalability on the server side.

Layered system

In REST APIs, the calls and responses go through different layers. As a rule of thumb, don’t assume that the client and server applications connect directly to each other. There may be a number of different intermediaries in the communication loop (firewalls, routers, reverse proxies, …). REST APIs need to be designed so that neither the client nor the server can tell whether it communicates with the end application or an intermediary.

Uniform interface

All API requests for the same resource should look the same, no matter where the request comes from. The REST API should ensure that the same piece of data, such as the address of a user, belongs to only one uniform resource identifier (URI).

Code on demand (optional)

REST allows client functionality to be extended by downloading and executing code in the form of applets or scripts. This simplifies clients by reducing the number of features required to be pre-implemented. Allowing features to be downloaded after deployment improves system extensibility. However, it also reduces visibility, and thus is only an optional constraint within REST.

Truly RESTful

Although 5 of them are necessary to call a system truly RESTful, typically any Web API that uses HTTP methods and URL’s to point to resources REST is called a REST API (hence all the controversy).

Tomorrow I’ll introduce the Richardson Maturity Model as a way to identify how REST compliant your Web API is.

Popular posts from this blog

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME         ...

Cache stampede: when our cache turned against us

While investigating some performance issues, we ran into an ASP.NET Core API that cached a fairly expensive aggregation query for 60 seconds. Under normal load, that was fine: one request rebuilds the cache, everyone else reads from it. Under peak load, dozens of requests would arrive in that same expiry window, all see a cache miss, and all fire the same expensive query in parallel. The database didn't like that. That was the moment when our caching layer stopped helping and started hurting. A burst of requests comes in at the same time, all miss the cache, and all go hammer the database or the downstream API at once. That's a cache stampede . The cache was supposed to protect our backend, and for a few hundred milliseconds it did the opposite. Why this happens IMemoryCache.GetOrCreate (and its async sibling) looks like it protects you, but it doesn't add any locking on its own. Look at the naive version: public async Task<Report> GetReportAsync(string key) ...

A complex system designed from scratch never works

A few years ago, I worked as an architect on a big mainframe rewrite. I still count it as one of my failures. Not because the technology was wrong, but because I couldn't convince the management team to simplify the approach. Years later, the organization is still struggling to get the new system up and running. I left the project at the time, because I couldn't put my name behind an approach that would take very long and cost a lot of money without a working system to show for it along the way. Gall’s Law That memory keeps coming back to me, because it's a textbook case of Gall's Law playing out in real life. Gall's Law , from John Gall's Systemantics , states it plainly: A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works, and it cannot be patched to make it work. You have to start over with a simple system that works. What does that mean in practice,...