# A Switchless State Machine

This blog post is an excerpt from my book, Programming with Types. The code samples are in TypeScript.

## Early Programming with Types

While working on an early draft of the book, I wrote a small script to help me keep the source code in sync with the text. The draft was written in the popular Markdown format. I kept the source code in separate TypeScript files so I could compile them and ensure that, even if I update the code samples, they still work.

I needed a way to ensure that the Markdown text always contains the latest code samples. The code samples always appear between a line containing ts and a line containing . When generating HTML from the Markdown source, ts is interpreted as the beginning of a TypeScript code block, which gets rendered using TypeScript syntax highlighting, while  marks the end of that code block. The contents of these code blocks had to be inlined from actual TypeScript source files which I could compile and validate outside of the text.

The figure shows two TypeScript (.ts) files containing code samples which should be inlined in the Markdown document between ts and  markers. The <!-- ... --> comments annotate the code samples for my script.

To determine which code sample goes where, I relied on a small trick: Markdown allows raw HTML in the document text. I annotated each code sample with an HTML comment, for example <!-- sample1 -->. HTML comments do not get rendered, so when converting Markdown to HTML, these became invisible. On the other hand, my script could use these to determine which code sample to inline where.

Once all code samples were loaded from disk, I had to process each Markdown document of the draft and produce an updated version as follows:

• In text processing mode, simply copy each line of the input text to the output document as-is. Once a marker is encountered (<!-- sample -->), grab the corresponding code sample and switch to marker processing mode.
• In marker processing mode, again copy each line of the input text to the output document until we encounter a code block marker (ts). Once the code marker is encountered, output the latest version of the code sample as loaded from the TypeScript file and switch to code processing mode.
• In code processing mode, we already ensured the latest version of the code is in the output document, so we can skip over the potentially outdated version in the code block. That means we skip each line until we encounter the end of code block marker (). Then we switch back to text processing mode.

With each run, the existing code samples in the document preceded by a <!-- ... --> marker get updated to the latest version of the TypeScript files on disk. Other code blocks that aren’t preceded by <!-- ... --> don’t get updated as they are processed in text processing mode.

As an example, let’s take a helloWorld.ts code sample:

console.log("Hello world!");


We want to embed this in Chapter1.md and make sure it’s kept up to date.

# Chapter 1

Printing "Hello world!".
<!-- helloWorld -->
ts
console.log("Hello");



This is not quite up to date, the string here is "Hello", not matching helloWorld.ts.

This document gets processed line by line as follows:

1. In text processing mode, "# Chapter 1" is copied to the output as-is.
2. "" (blank line) is copied to the output as-is.
3. "Printing "Hello world!"." is copied to the output as-is.
4. "<!-- helloWorld -->" is copied to the output as-is. This is a marker though, so we keep track of the code sample to be inlined (helloWorld.ts) and switch to marker processing mode.
5. "ts" is copied to the output as-is. This is a code block marker, so immediately after copying it to the output we also output the contents of helloWorld.ts. We also switch to code processing mode.
6. "console.log("Hello");" is skipped. We don’t copy lines in code processing mode, as we are replacing them with the latest in the code sample file.
7.  is an end of code block marker. We insert it then switch back to text processing mode.

## State Machines

The behavior of our text processing script is best modelled as a state machine. A state machine has a set of states and a set of transitions between pairs of states. The machine starts in a given state, also known as the start state, then if certain conditions are met, it can transition to another state.

This is exactly what our text processor does, with its three processing modes. Input lines are processed a certain way when in text processing mode. When some condition is met (a <!-- sample --> marker is encountered), our processor transitions to the marker processing mode. Again, when some other condition is met (ts code block marker encountered), it transitions to code processing mode. When the end of the code block marker is encountered (), it transitions back to text processing mode.

The figure shows a text processing state machine with the three states (text processing, marker processing, code processing) and transitions between the states based on input. Text processing is the initial state or start state.

Now that we modeled the solution, let’s look at how we would implement it. One way to implement a state machine is by defining the set of states as an enumeration, keeping track of the current state, and get the desired behavior with a switch statement that covers all possible states. In our case, we can define a TextProcessingMode enum.

Our TextProcessor class will keep track of the current state in a mode property, and implement the switch statement in a processLine() method. Depending on the state, this method will in turn invoke one of the three processing methods, processTextLine(), processMarkerLine(), or processCodeLine(). These functions will implement the text processing then, when appropriate, transition to another state by updating the current state.

Processing a Markdown document consisting of multiple lines of text means processing each line in turn using our state machine then returning the final result to the caller:

enum TextProcessingMode {
Text,
Marker,
Code,
}

class TextProcessor {
private mode: TextProcessingMode = TextProcessingMode.Text;
private result: string[] = [];
private codeSample: string[] = [];

processText(lines: string[]): string[] {
this.result = [];
this.mode = TextProcessingMode.Text;

for (let line of lines) {
this.processLine(line);
}

return this.result;
}

private processLine(line: string): void {
switch (this.mode) {
case TextProcessingMode.Text:
this.processTextLine(line);
break;
case TextProcessingMode.Marker:
this.processMarkerLine(line);
break;
case TextProcessingMode.Code:
this.processCodeLine(line);
break;
}
}

private processTextLine(line: string): void {
this.result.push(line);

if (line.startsWith("<!--")) {

this.mode = TextProcessingMode.Marker;
}
}

private processMarkerLine(line: string): void {
this.result.push(line);

if (line.startsWith("ts")) {
this.result = this.result.concat(this.codeSample);

this.mode = TextProcessingMode.Code;
}
}

private processCodeLine(line: string): void {
if (line.startsWith("")) {
this.result.push(line);

this.mode = TextProcessingMode.Text;
}
}

/* Load sample based on marker, store in this.codeSample  */
}
}


We omitted the code to actually load a sample from an external file as it isn’t particularly relevant to our state machine discussion. This implementation works but it can be simplified if we use a pluggable function.

Note that all our text processing functions have the same signature: they take a line of text as a string argument and return void. What if, instead of having processLine() implement a big switch statement and forward to the appropriate function, we make processLine() be one of those functions?

Instead of implementing processLine() as a method, we can define it as a property of the class with type (line: string) => void and initialize it with processTextLine(). Then, in each of the three text processing methods, instead of setting mode to a different enum value, we set processLine() to a different method. In fact, we no longer need to externally keep track of our state. We don’t even need an enum:

class TextProcessor {
private result: string[] = [];
private processLine: (line: string) => void = this.processTextLine;
private codeSample: string[] = [];

processText(lines: string[]): string[] {
this.result = [];
this.processLine = this.processTextLine;

for (let line of lines) {
this.processLine(line);
}

return this.result;
}

private processTextLine(line: string): void {
this.result.push(line);

if (line.startsWith("<!--")) {

this.processLine = this.processMarkerLine;
}
}

private processMarkerLine(line: string): void {
this.result.push(line);

if (line.startsWith("ts")) {
this.result = this.result.concat(this.codeSample);

this.processLine = this.processCodeLine;
}
}

private processCodeLine(line: string): void {
if (line.startsWith("")) {
this.result.push(line);

this.processLine = this.processTextLine;
}
}

/* Load sample based on marker, store in this.codeSample  */
}
}


The second implementation gets rid of the TextProcessingMode enum, the mode property, and the switch statement which forwarded processing to the appropriate method. Instead of handling forwarding, processLine() now is the appropriate processing method.

This implementation removes the need to keep track of states separately and keep that in sync with the processing logic. If we ever wanted to introduce a new state, the old implementation would’ve forced us to update the code in several places. Besides implementing the new processing logic and state transitions, we would’ve had to update the enum and add another case to the switch statement. Our alternative implementation removes the need for that: a state is represented purely by a function.

## State Machines with Sum Types

One caveat to this is that, for state machines with many states, capturing states and even transitions explicitly might make the code easier to understand. Even so, instead of using enums and switch statements, another possible implementation is to represent each state as a separate type and the whole state machine as a sum type of the possible states. This allows us to break it apart into type-safe components. Below is an example of how we would implement the above state machine using a sum type. The code is a bit more verbose, so if possible, we should try the implementation we discussed so far, but this is another alternative to a switch-based state machine.

Using a sum type, each state is represented by a different type, so we have a TextLineProcessor, a MarkerLineProcessor, and a CodeLineProcessor. Each of them keeps track of the processed lines so far in a result member and provides a process() method to handle a line of text:

class TextLineProcessor {
result: string[];

constructor(result: string[]) {
this.result = result;
}

process(line: string): TextLineProcessor | MarkerLineProcessor {
this.result.push(line);

if (line.startsWith("<!--")) {
return new MarkerLineProcessor(
} else {
return this;
}
}

/* Load sample based on marker, store in this.codeSample */
}
}

class MarkerLineProcessor {
result: string[];
codeSample: string[]

constructor(result: string[], codeSample: string[]) {
this.result = result;
this.codeSample = codeSample;
}

process(line: string): MarkerLineProcessor | CodeLineProcessor {
this.result.push(line);

if (line.startsWith("ts")) {
this.result = this.result.concat(this.codeSample);

return new CodeLineProcessor(this.result);
} else {
return this;
}
}
}

class CodeLineProcessor {
result: string[];

constructor(result: string[]) {
this.result = result;
}

process(line: string): CodeLineProcessor | TextLineProcessor {
if (line.startsWith("")) {
this.result.push(line);

return new TextLineProcessor(this.result);
} else {
return this;
}
}
}

function processText(lines: string): string[] {
let processor: TextLineProcessor | MarkerLineProcessor
| CodeLineProcessor = new TextLineProcessor([]);

for (let line of lines) {
processor = processor.process(line);
}

return processor.result;
}


All our processors return a processor instance. Either this, if there is no state change, or a new processor as state changes. The processText() runs the state machine by calling process() on each line of text and updating processor as state changes by reassigning it to the result of the method call.

Now the set of states is explicitly spelled out in the signature of the processor variable, which can be a TextLineProcessor or a MarkerLineProcessor, or a CodeLineProcessor.

The possible transitions are captured in the signatures of the process() methods – for example TextLineProcessor.process() returns TextLineProcessor | MarkerLineProcessor, meaning it can either stay in the same state (TextLineProcessor) or transition to the MarkerLineProcessor state. These state classes can have more properties and members if needed. This implementation is slightly longer than the one relying on functions, so if we don’t need the extra features, we are better off using the simpler solution.

## State Machine Implementation Recap

Let’s quickly review the alternative implementations discussed.

• The “classical” implementation of a state machine uses an enum to define all the possible states, a variable of that enum type to keep track of the current state, and a big switch statement to determine which processing should be performed based on the current state. State transitions are implemented by updating the current state variable. The drawback of this implementation is that states are completely removed from the processing we want to run during each state, so the compiler can’t prevent mistakes when we run the wrong processing while in a given state. For example, nothing stops us from calling processCodeLine() even when we’re in TextProcessingCode.Text. We also have to maintain state and transitions as a separate enum, with the risk of running out of sync (for example we might add a new value to the enum but forget to add a case for it in the switch statement).
• The functional implementation represents each processing state as a function and relies on a function property to track the current state. State transitions are implemented by assigning the function property to another state. This is a lightweight implementation which should work for many cases. The drawbacks are sometimes we need to associate more information with each state, and sometimes we might want to be explicit when declaring what the possible states and transitions are.
• The sum type implementation represents each processing state as a class and relies on a variable representing the sum type of all the possible states to keep track of the current state. State transitions are implemented by re-assigning the variable to another state. This allows us to add additional properties and members to each state and keep them grouped together. The drawback is that the code is more verbose than the functional alternative.