At some point in an application’s development, usually, fairly early on, you realize the application is slow. After some research, the culprit is, unnecessarily retrieving the same data, and a light goes off, and you think: “I need some caching.”

Caching is an invaluable pattern for eliminating redundant calls to a database or a third party API. Microsoft provides IMemoryCache for time-based caching, however sometimes time-based caching isn’t what you need. In this article, we look at Request Scoped caching and how it can benefit us.

What is Request caching? Request caching is a mechanism to cache data for the life of a web request. In dot-net, we’ve had this ability in some capacity with the HttpContext.Items collection, however, HttpContext is not known for its injectability.

Request Scoped caching has a few benefits: First, it eliminates the concern of stale data. In most scenarios, a request executes in less than a second and which typically isn’t long enough for data to become stale. And secondly, expiration isn’t a concern because the data dies when the request ends.

Out of the box, Asp.Net Core doesn’t have injectable caching. As mentioned earlier, HttpContext.Items is an option, but it’s not an elegant solution.

Luckily for us, ASP.Net Core gives us the tools to create an injectable Request Caching implementation by using the built-in dependency injection (DI) framework.

The built-in DI framework has three lifetimes for dependencies: Singleton, Scoped, and Transient. Singleton is for the life of the application, Scoped is for the life of the request and Transient is a new instance with each request.

I’ve created an interface modeled after the IMemoryCache interface to keep things consistent.

Interface

public interface IRequestCache
{
    /// <summary>
    /// Add the value into request cache. If the key already exists, the value is overwritten.
    /// </summary>
    /// <param name="key"></param>
    /// <param name="value"></param>
    /// <typeparam name="TValue"></typeparam>
    void Add<TValue>(string key, TValue value);

    /// <summary>
    /// Remove the key from the request cache
    /// </summary>
    /// <param name="key"></param>
    void Remove(string key);

    /// <summary>
    /// Retrieve the value by key, if the key is not in the cache then the add func is called
    /// adding the value to cache and returning the added value.
    /// </summary>
    /// <param name="key"></param>
    /// <param name="add"></param>
    /// <typeparam name="TValue"></typeparam>
    /// <returns></returns>
    TValue RetrieveOrAdd<TValue>(string key, Func<TValue> add);

    /// <summary>
    /// Retrieves the value by key. When the key does not exist the default value for the type is returned.
    /// </summary>
    /// <param name="key"></param>
    /// <typeparam name="TValue"></typeparam>
    /// <returns></returns>
    TValue Retrieve<TValue>(string key);
}

Implementation

public class RequestCache : IRequestCache
{
    IDictionary<string, object> _cache = new Dictionary<string, object>();

    /// <summary>
    /// Add the value into request cache. If the key already exists, the value is overwritten.
    /// </summary>
    /// <param name="key"></param>
    /// <param name="value"></param>
    /// <typeparam name="TValue"></typeparam>
    public void Add<TValue>(string key, TValue value)
    {
        _cache[key] = value;
    }

    /// <summary>
    /// Remove the key from the request cache
    /// </summary>
    /// <param name="key"></param>
    public void Remove(string key)
    {
        if (_cache.ContainsKey(key))
        {
            _cache.Remove(key);
        }
    }

    /// <summary>
    /// Retrieve the value by key, if the key is not in the cache then the add func is called
    /// adding the value to cache and returning the added value.
    /// </summary>
    /// <param name="key"></param>
    /// <param name="add"></param>
    /// <typeparam name="TValue"></typeparam>
    /// <returns></returns>
    public TValue RetrieveOrAdd<TValue>(string key, Func<TValue> add)
    {
        if (_cache.ContainsKey(key))
        {
            return (TValue)_cache[key];
        }

        var value = add();

        _cache[key] = value;

        return value;
    }

    /// <summary>
    /// Retrieves the value by key. When the key does not exist the default value for the type is returned.
    /// </summary>
    /// <param name="key"></param>
    /// <typeparam name="TValue"></typeparam>
    /// <returns></returns>
    public TValue Retrieve<TValue>(string key)
    {
        if (_cache.ContainsKey(key))
        {
            return (TValue)_cache[key];
        }

        return default(TValue);
    }
}

Using ASP.Net Core’s DI framework we’ll wire it up as Scoped.

services.AddScoped<IRequestCache, RequestCache>();

Usage

public class UserService
{
    private readonly IRequestCache _cache;
    private readonly IUserRepository _userRepository;

    public UserService(IRequestCache cache, IUserRepository userRepository)
    {
        _cache = cache;
        _userRepository = userRepository;
    }

    public User RetrieveUserById(int userId)
    {
        var buildCacheKey = UserService.BuildCacheKey(userId);

        return _cache.RetrieveOrAdd(BuildCacheKey, () => { return _userRepository.RetrieveUserBy(userId); });
    }

    public void Delete(int userId)
    {
        var buildCacheKey = UserService.BuildCacheKey(userId);

        _userRepository.Delete(userId);
        _cache.Remove(BuildCacheKey(userId));
    }

    private static string BuildCacheKey(int userId)
    {
        return $"user_{userId}";
    }
}

That’s it! Request Caching is now injectable in any place you need it.

Visit the Git Repository and feel free to take the code for a spin.

One of the selling points of the Single Page Application (SPA) was offloading work traditionally performed on the server onto the client. I feel the SPA has delivered on this promise.

However, it’s not all roses. It’s easy to get overzealous and push too much work to the client. We often forget that we can’t control the client’s environment — the client can be anything from a ten-year-old machine to the latest-and-greatest smartphone with access to varying internet speeds. The only thing we can count on is the user viewing our site in a browser.

Processing

In my experience, intensive processing and data needing consistency between the server and the client, such as date conversions or data requiring precise calculations, such as money, are prime candidates for server-side rendering.

Paging

A common task performed on the client is paging. With small datasets this works great; however, small datasets never stay small. As data grows, the application slows and eventually becomes unresponsive. Unfortunately, you don’t know it’s happening because it’s on the client and even worse it doesn’t occur on all clients making it hard to troubleshoot.

Moving paging from the client to the server will alleviate paging related performance issues on the client. You might be thinking, “but now I am making a ton of API calls. Making so many API calls doesn’t seem optimal.” True, it does seem that way, but you’ll be surprised how fast your data returns from the server. And the best part? You control the server and can increase capacity as needed.

At the end of the day, you want to provide the user a rich responsive experience and sometimes that’s letting the server do the heavy lifting.

Summary

Summarizing, when possible we want to offload work to the client, but by doing so, we can quickly overburden the client. Keeping arduous tasks such as paging and intensive processing on the server can protect us from overwhelming the client.

For nearly 50 years, the switch statement (also known as the case statement) has been an integral part of programming. In recent years, however, some are claiming that the switch statement has outlived its usefulness. Others go even further by labeling the switch statement as a code-smell.

In 1952, Stephen Kleene conceived the switch statement in his paper, Introduction to Metamathematics. The first notable implementation was in ALGOL 58 in 1958. Later, the switch statement was included in the indelible C programming language, which, as we know, has influenced most modern programming languages.

Fast forward to the present day and virtually every language has a switch statement. However, a few languages have omitted the switch statement. The most notable being Smalltalk.

This piqued my curiosity, why was the switch statement excluded from Smalltalk?

Andy Bower, one of the creators/proponents behind Dolphin Smalltalk shared his thoughts on why Smalltalk excluded the switch statement:

When I first came to Smalltalk from C++, I couldn’t understand how a supposedly fully fledged language didn’t support a switch/case construct. After all when I first moved up to “structured programming” from BASIC I thought that switch was one of the best things since sliced bread. However, because Smalltalk didn’t support a switch I had to look for and understand, how to overcome this deficiency. The correct answer is, of course, to use polymorphism and to make the objects themselves dispatch to the correct piece of code. Then I realized that it wasn’t a “deficiency” at all, but Smalltalk was forcing me into much finer grained OOP design than I had got(ten) used to in C++. If there had been a switch statement available it would have taken me a lot longer to learn this or, worse, I might still be programming C++/Java pseudo-object style in Smalltalk.
I would contend that in normal OOP there is no real need for a switch statement. Sometimes, when interfacing to a non-OOP world (like receiving and dispatching WM_XXXX Windows messages that are not objects but just integers), then a switch statement would be useful. In these situations, there are alternatives (like dispatching from a Dictionary) and the number of times they crop up doesn’t warrant the inclusion of additional syntax.

Was Andy right? Are we better off without the switch statement? Would other languages also benefit from excluding the switch statement?

To shed some light on this question, I’ve put together a comparison between a switch statement, a dictionary, and polymorphism. Let’s call it a smackdown. May the best implementation win!

Each implementation has a method taking one parameter, an integer, and returns a string. We’ll use cyclomatic complexity and maintainability index to examine each implementation. We’ll then take a holistic view of all three implementations.

The code.

Switch Statement

Maintainability Index	72
Cyclomatic Complexity	6

    public class SwitchWithFourCases
    {
        public string SwitchStatment(int color)
        {
            var colorString = "Red";

            switch (color)
            {
                case 1:
                    colorString = "Green";
                    break;

                case 2:
                    colorString = "Blue";
                    break;

                case 3:
                    colorString = "Violet";
                    break;

                case 4:
                    colorString = "Orange";
                    break;

            }

            return colorString;
        }
    }

Dictionary

Maintainability Index	73
Cyclomatic Complexity	3

public class DictionaryWithFourItems
{ 
    public string Dictionary(int color)
    {
        var colorString = "Red";
        var colors = new Dictionary<int, string> {{1, "Green"}, {2, "Blue"}, {3, "Violet"}, {4, "Orange"}};
        var containsKey = colors.ContainsKey(color);
        if (containsKey)
        {
            colorString = colors[color];
        }

        return colorString;
    }
}

Polymorphism

Total Maintainability Index	94
Total Cyclomatic Complexity	15

Interface

Maintainability Index	100
Cyclomatic Complexity	1

public interface IColor
{
    string ColorName { get; }
}

Factory

Maintainability Index	76
Cyclomatic Complexity	4

public class ColorFactory
{
    public string GetColor(int color)
    {
        IColor defaultColor = new RedColor();
        var colors = GetColors();
        var containsKey = colors.ContainsKey(color);
        if (containsKey)
        {
            var c = colors[color];
            return c.ColorName;
        }

        return defaultColor.ColorName;
    }

    private static IDictionary<int, IColor> GetColors()
    {
        return new Dictionary<int, IColor>
        {
            {1, new GreenColor()}, 
            {2, new BlueColor()}, 
            {3, new VioletColor()}, 
            {4, new OrangeColor()}, 
            {5, new MagentaColor()}
        };
    }
}

Implementation

Maintainability Index	97
Cyclomatic Complexity	2

public class BlueColor : IColor
{
    public string ColorName => "Blue";
}

public class RedColor : IColor
{
    public string ColorName => "Red";
}

public class GreenColor : IColor
{
    public string ColorName => "Green";
}

public class MagentaColor : IColor
{
    public string ColorName => "Magenta";
}

public class VioletColor : IColor
{
    public string ColorName => "Violet";
}

The Results

Before I dive into the results, let’s define Cyclomatic Complexity and Maintainability Index:

• Cyclomatic Complexity is the measure of logic branching. The lower the number, the better.  
• Maintainability Index measures maintainability of the code. It’s on a scale between 0 and 100. The higher the number, the better.

	Cyclomatic Complexity	Maintainability Index
Switch Statement	6	72
Dictionary	3	73
Polymorphism	15	94

We will examine cyclomatic complexity first.

The results for cyclomatic complexity are straightforward. The dictionary implementation is the simplest. Does this mean it’s the best solution? No, as we’ll see when we evaluate the maintainability index.

Most would think as I did, the implementation with the lowest cyclomatic complexity is the most maintainable — how could it be any other way?

In our scenario, the implementation with the lowest cyclomatic complexity isn’t the most maintainable. In fact in our scenario, it’s the opposite. The most complex implementation is the most maintainable! Mind blown!

If you recall, the higher the maintainability index score, the better. Cutting to the chase, polymorphism has the best maintainability index score — but it also has the highest cyclomatic complexity. What gives? That doesn’t seem right.

Why is the most complex implementation the most maintainable? To answer this, we must understand the maintainability index.

The maintainability index consists of 4 metrics: cyclomatic complexity, lines of code, the number of comments and the Halstead volume. The first three metrics are relatively well known, but the last one, the Halstead Volume, is relatively unknown. Like, cyclomatic complexity, the Halstead Volume attempts objectively measure code complexity.

In simple terms, Halstead Volume measures the number of moving parts (variables, system calls, arithmetic, coding constructs, etc.) in code. The higher the number of moving parts the more complexity. The lower the number of moving parts, the lower the complexity. This explains why the polymorphic implementation scores high on the maintainability index; the classes have little to no moving parts. Another way to look at the Halstead Volume is it measures “moving parts” density.

What is software, if it’s not to change? To reflect the real world, we are introducing change. I’ve added a new color to each implementation.

Below are the revised results.

	Cycolmatic Complexity	Maintainability Index
Switch Statement	7	70
Dictionary	3	73
Polymorphism	17	95

The switch statement and the polymorphic approaches both increased in cyclomatic complexity by one unit, but interestingly, the dictionary didn’t increase. At first I was puzzled by this, but then I realized the dictionary considers the colors as data and the other two implementations treat the colors as code.I’ll get down to the brass tacks.

Turing our attention to the maintainability index, only one, the switch statement, decreased in maintainability. Polymorphism’s maintainability score improved and yet the complexity also increases (we’d prefer it to decrease). As I mentioned above, this is counter-intuitive.

Our comparison shows that dictionaries can, from a complexity standpoint, scale infinitely. The polymorphic approach is by far the most maintainable and seems to increase in maintainability as more scenarios are added. The switch statement increases in complexity and decreases in maintainability when the new scenario was added. Even before we added the new scenario, it had the worst cyclomatic complexity and maintainability index measures.

Jem Finch from Google shared his thoughts on the switch statements shortcomings:

1. Polymorphic method implementations are lexically isolated from one another. Variables can be added, removed, modified, and so on without any risk of impacting unrelated code in another branch of the switch statement.  

2. Polymorphic method implementations are guaranteed to return to the correct place, assuming they terminate. Switch statements in a fall through language like C/C++/Java require an error-prone “break” statement to ensure that they return to the statement after the switch rather than the next case block.

3. The existence of a polymorphic method implementation can be enforced by the compiler, which will refuse to compile the program if a polymorphic method implementation is missing. Switch statements provide no such exhaustiveness checking.

4. Polymorphic method dispatching is extensible without access to (or recompiling of) other source code. Adding another case to a switch statement requires access to the original dispatching code, not only in one place but in every place the relevant enum is being switched on.

5. … you can test polymorphic methods independent of the switching apparatus. Most functions that switch like the example the author gave will contain other code that cannot then be separately tested; virtual method calls, on the other hand, can.

6. Polymorphic method calls guarantee constant time dispatch. No sufficiently smart compiler is necessary to convert what is naturally a linear time construct (the switch statement with fall through) into a constant time construct.

Unfortunately, or fortunately, depending on your camp, most languages have a switch statement, and they aren’t going anywhere anytime soon. With this in mind, it’s good to know what’s happening under the hood when compiling switch statements.

There are three switch statement optimizations that can occur:

1. If-elseif statements – When a switch statement has a small number of cases or sparse cases (non-incremental values, such as 10, 250, 1000) it’s converted to an if-elseif statement.  
2. Jump Table – In larger sets of adjacent cases (1, 2, 3, 4, 5) the compiler converts the switch statement to a jump table. A Jump Table is essentially a Hashtable with a pointer (think goto statement) to the function in memory.
3. Binary Search – For large sets of sparse cases the compiler can implement a binary search to identify the case quickly, similar to how an index works in a database. In extraordinary cases where cases are a large number of sparse and adjacent cases, the compiler will use a combination of the three optimizations.

Summary

In an object oriented world the switch statement, conceived in 1952, is a mainstay of the software engineer. A notable exception is Smalltalk where the designers opted to exclude the switch statement.

When compared to alternative equivalent implementations, the dictionary, and polymorphism, the switch statement did not fare as well.

The switch statement is here to stay, but as our comparison has shown there are better alternatives to the switch statement.

The implementations are available on Github.