Tuples as Syntactic Sugar

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Programming Languages, Part A

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University of Washington

Programming Languages, Part A

934 оценки

This course is an introduction to the basic concepts of programming languages, with a strong emphasis on functional programming. The course uses the languages ML, Racket, and Ruby as vehicles for teaching the concepts, but the real intent is to teach enough about how any language “fits together” to make you more effective programming in any language -- and in learning new ones. This course is neither particularly theoretical nor just about programming specifics -- it will give you a framework for understanding how to use language constructs effectively and how to design correct and elegant programs. By using different languages, you will learn to think more deeply than in terms of the particular syntax of one language. The emphasis on functional programming is essential for learning how to write robust, reusable, composable, and elegant programs. Indeed, many of the most important ideas in modern languages have their roots in functional programming. Get ready to learn a fresh and beautiful way to look at software and how to have fun building it. The course assumes some prior experience with programming, as described in more detail in the first module. The course is divided into three Coursera courses: Part A, Part B, and Part C. As explained in more detail in the first module of Part A, the overall course is a substantial amount of challenging material, so the three-part format provides two intermediate milestones and opportunities for a pause before continuing. The three parts are designed to be completed in order and set up to motivate you to continue through to the end of Part C. The three parts are not quite equal in length: Part A is almost as substantial as Part B and Part C combined. Week 1 of Part A has a more detailed list of topics for all three parts of the course, but it is expected that most course participants will not (yet!) know what all these topics mean.

Из урока

Section 2 and Homework 2

This section is a particularly rewarding one where a lot of ideas come together to reveal a surprisingly elegant underlying structure in ML. As usual, start with the welcome reading, dive into the material, and leave plenty of time to approach the programming assignment methodically.

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Dan Grossman
Professor
Computer Science & Engineering

.

Alright in this lecture I want to show you something kind of neat about tuples,

and to use that to introduce this really important concept in programming

languages called syntactic sugar. So let me just show this to you in the

repple. we have already seen how to build pairs.

Here's a pair. Three plus one, four plus two creates the

pair (4,6), no problem. And, similarly, we could create a record,

maybe with a field second of four2, plus two and a field first of three1.

plus one. Alright?

So nothing unusual there. We see the tuples and records are

similar, but different. But now, suppose I did something kind of

strange, and I made a record. But I made the field names, say, one and

two, in whatever order I want, because the

order never matters. Something like this.

Watch this. Oh isn't that weird.

I made a record with field names one and two and the repple printed it out as a

pair, (6,5), and it printed the type as int * int.

Its like it turned my record into a tuple or something.

Of course this is no problem, right? If I wanted to say, take a hash one of a pair,

and add to that hash two of another pair. Oop.

underscore not plus, there we go.

I get nine of type int and you know for hash one and hash two it doesn't really

matter. Hash two can mean get the second

component of a pair or it can say get field two of a record.

Right? So it doesn't seem to matter.

and just to show that you really can give numeric field names to records if I had a

record like this with three and one, alright,

well that prints as a normal record. it has type one:bool three:string.

But if I had something with three equals hi, one equals true, two equals, say,

three plus two. Now I have a triple, (true, five, hi,)

bool * int * string. And what I want to convince you is that

this is because there is no such thing as tuples in ML, there are only records and

tuples are just a way, a different way of writing records that you as a programmer

can use whenever you want. And the repple always chooses to use of a

[INAUDIBLE] and this is because tuples are just "syntactic sugar" for records.

So lets unpack that idea little bit here. Okay.

Previously, I taught you tuples as though they're in ML.

We gave syntax, we gave type-checking rules, and we gave

evaluation rules. But we could've done something else

instead. I could've taught you records first, even

though they're a little more complicated, and then told you that there's some

special syntax for writing certain kinds of records.

That whenever you write the syntax for building a tuple, e1 comma up to en, in

parentheses, that that's just another way of writing the record with field names

one up to n, and with the corresponding expressions.

And whenever you write the type for a tuple type with those stars in between

the component pieces, that's just another way of writing the

record type where, again, the field names are one through N.

In other words, tuples are nothing more and nothing less than records with

particular field names. And that is how the ML Language

definition actually works. That all there is about tuples are

special syntax that you can use when writing programs, and that we print when

we write the results of things in the rupple.

So you really can write things like the record one equals four, two equals seven,

three equals nine. It's terrible style.

I don't know why you would do this. The tuple syntax is easier to read, it's

what people expect. I just showed you this for the purpose of

teaching you this idea. But it really is that in ML we have

records, tuples are just a particular way of writing particular records.

So, what we say in programming language

speak, is that tuples are just syntactic sugar for records with fields named one,

two, up to N. So, I'm going to use this phrase a lot in

the course, so let's understand exactly what it means.

Syntactic because we can describe the semantics,

the evaluation rules and typing rules for tuples, completely in terms of something

else. Right?

We do this. Just you wrote this.

It's like you wrote that. And then we can use all the semantic

rules we have for a different language construct.

So that's why it's called syntactic when we do this.

We call it sugar because it makes the language sweeter.

It's a pleasant thing to use and see. So we call it syntactic sugar.

We're going to see many more examples of this. there,

syntacic sugar is great because it simplifies our understanding of the

language. Once you understand how tuples are really

a form of records, you understand everything there is to know about tuples,

as long as you understand records. They also simplify implementing the

language. The ML implementation just has to convert

tuples into the corresponding records and there isn't as much duplicative work to

do. We only have to implement one kind of

each of type, instead of two. Okay.

So this is far from the only example we'll see.

there are other examples of syntactic sugar and actu, we, we've already seen

one other. You can think of e1 and also e2 as being

syntactic sugar for, if e1, then e2, else false.

Right? We can explain everything there is to

explain about and also, in terms of that syntactic transformation in terms of an

if-then-else. And also is better styled, but we can

define its semantics in terms of another concept, we already understand.

So that's syntactic sugar. And now we understand the truth about

tuples

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