Hårde kodningsbegreber forklaret med enkle virkelige analyser

Sådan forklares kodningskoncepter som streams, løfter, fnug og erklærende programmering til en 5-årig

Jeg elsker at tænke på kodning af begreber ved at sammenligne dem med velkendte ting, vi kender i livet. Der er så mange analogier derude om kodningskoncepter. Nogle af dem er gode, mens andre er forvirrende, primært fordi de fokuserer på delvise aspekter af et koncept, mens de ignorerer mange andre. Denne artikel vil opsummere nogle af de analogier, som jeg synes bedst passer til nogle få kodningskoncepter på komplette måder.

Opdatering: Denne artikel er nu en del af min bog "The Professional Programmer". Læs den opdaterede version af dette indhold og mere programmeringsråd på jscomplete.com/pro-programmer .

Jeg starter med enkle begreber og går videre til sværere. Lad os starte med kodning af sig selv. Kodning kan sammenlignes med at skrive madlavningsopskrifter. En opskrift i denne analogi er programmet, og kokken er computeren. En opskrift er en liste over instruktioner, som en kok skal følge, og et program er en liste over instruktioner, som en computer kan udføre.

Dette er en meget enkel analogi i betragtning af, at en opskrift er skrevet på et menneskeligt sprog, og et program er skrevet på et computersprog, og de er meget forskellige sprog (medmindre dine opskrifter lukker og lover!). Der er heller ikke mange uventede ting at planlægge i en opskrift, mens et computerprogram vil have mange. Uanset dens enkelhed er det en god måde at vise, hvordan en computer udfører en liste med instruktioner sekventielt. Det viser også, hvor en instruktionslinie kan bruge ethvert resultat fra udførelse af tidligere instruktionslinjer.

Nogle opskrifter vil endda have if-udsagn: hvis madlavning til 2, 4 eller 8! Nogle opskrifter har løkker: Bliv ved med at slå den blanding indtil ...

Jeg kan også lide denne analogi på grund af alle de klare ting og værktøjer, som du kan bruge i dine opskrifter - som kageblandingen, som du kan bruge til at lave cupcakes, og den specielt formede pande, der gør det så meget lettere at skabe cupcakes.

Brugen af ​​færdige genstande og værktøjer er som at inkludere og bruge en pakke kode skrevet af andre i din egen kode.

// The making of a cupcake// First steps:
$ npm install cake-mix$ npm install cupcake-pan

NPM er pakkehåndtering for Node.js , som er en meget populær ramme til skrivning af JavaScript-applikationer. I denne analogi er Node.js som selve køkkenet. Det giver dig mulighed for at udføre linjer i dine opskrifter ved hjælp af indbyggede moduler som din ovn og vask.

Når vi taler om usund mad, er denne næste analogi at lære at kode og sammenlignes med spisevaner. Jeg ELSKER især denne analogi, og hvad den formidler, fordi den hjælper mig med at holde mig på rette spor i min kodeindlæringsrejse. For mig begyndte dette i gymnasiet og vil fortsætte, indtil min hjerne når sin sidste instruktion: dø ();

At lære at kode

At lære at kode er som at prøve at tabe sig. Denne analogi gælder virkelig at lære noget, men at lære at kode er et specielt match her.

"At tabe sig" er et negativt udtryk. Vi bør virkelig kalde det "Få sundhed." I den forstand er det meget sammenligneligt med "Opnå viden." De uddannelsesmæssige ressourcer, du har til rådighed for dig, er som dine madmuligheder. Nogle er bare okay, andre er gode, og nogle er helt dårlige for dig. At spise sundt og træne er de to vigtigste aktiviteter, der hjælper dig med at få sundhed. Tilsvarende er forbrug af gode uddannelsesressourcer og praksis med kodning i hånden de to vigtigste aktiviteter, der hjælper dig med at få god kodningskendskab.

Så hvordan lærer du "sund"? Når du forpligter dig til at spise sundt, bruger du filtre som organisk , lokal , fedtfattig , græsfodret og ikke-GMO. Det er nøjagtigt det samme med sunde uddannelsesressourcer, bortset fra at disse mærker endnu ikke er så klare. Jeg håber, at uddannelsesmæssige ressourcer en dag vil have kontrollerbare og relevante mærker. Måske mærker som "ikke-sponsoreret", "ingen markedsføring", "godkendt af eksperter", "tæt redigeret" og "drager fremad."

Men i stedet for at filtrere efter indholdet kan du nemt filtrere efter de gode mærker. Det gør jeg også med mad. Jeg kender og stoler på et par mærker, og jeg bruger dem mest. Det er nemmere. Med uddannelsesmæssige ressourcer er der nogle mærker (publikationer og mennesker), som du bare skal følge hele tiden.

Efter at have filtreret dit videnindtag til kun de gode ressourcer, skal du bare udøve! Øv alt, hvad du lærer, men ikke bare ved at genoprette nøjagtigt det, du lærte. Udfordre dig selv også til at gøre noget lidt anderledes omkring de emner, du har lært. Hvis du er heldig, sidder du fast! Så lærer du permanent noget andet, når du bliver løs.

Motion er for både kroppen og sindet.

Variabler

Variabler bruges i computerprogrammer til at indeholde data . Dette er en meget forenklet erklæring, og den er ved mange foranstaltninger simpelthen forkert.

Variabler indeholder ikke data. De peger bare på det. Data gemmes i computerens hukommelse. Du kan sammenligne variabler med de etiketter, du placerer i dine e-mail-meddelelser (eller noter eller filer).

Alle kodeeksempler i denne artikel er skrevet i JavaScript. JavaScript er et meget let at lære computersprog.

I Gmail er en etiket en markør til en e-mail eller en liste over e-mails. Mange etiketter kan pege på den samme e-mail. Dette svarer til at tildele en anden variabel til en eksisterende variabel:

let work = [email1, email2, email3];let important = work;

Både arbejde og vigtigt er nu etiketter, der peger på nøjagtig den samme liste over e-mails.

Nogle variabler repræsenterer konstante referencer . De kan ikke ændres. Dette er som " sendt " -etiketten i Gmail. Mens vi kan ændre arbejdsmærket ovenfor og få det til at pege på en anden liste over e-mails, kan vi ikke ændre den sendte etiket. Du kan ikke pege den sendte etiket på en anden liste over e-mails. Du kan kun få det til at pege på flere e-mails.

const sent = [];
// You cannot change the meaning of sent now// But you can add more values to it:
sent.push(new Email());

Fejl og undtagelser

En programmørs ekspertise handler stort set om, hvordan man håndterer fejl. Ekspertprogrammerere elsker fejl, fordi fejl for dem betyder fremskridt.

Nogle gange forventer vi at se disse vidunderlige røde beskeder, og hvis vi ikke gør det, ved vi, at koden simpelthen er forkert!

Jeg elsker sætningen " lyt til din kode", fordi jeg tror, ​​at kode udvikler sig ved at kommunikere til os ved hjælp af fejl.

Dette er nøjagtigt som at opdrage børn.

Det vigtigste forældrekoncept, som jeg indså med praksis, er hvordan børn kommunikerer ved at opføre sig dårligt. Dette skyldes, at de endnu ikke har en logisk hjerne. Jeg tror, ​​at programmer gør nøjagtigt det samme. De kommunikerer også ved at fejle (producere fejl), fordi programmer ikke er helt logiske. Din opgave som programmør er at tilføje mere logik i koden til at håndtere de sager, der oprindeligt producerede fejl. Dette er ligesom hvordan en forældres opgave er at lære det dårligt opførte barn, hvad der er galt med den dårlige opførsel, og hvad de skal gøre anderledes næste gang.

Some errors are not recoverable and a program encountering those should just exit (and be rebooted). This is like if your heart stops. There is not much that can be done except to reboot it with an electric shock. This is why we monitor our programs and reboot them when they get to that state. Luckily, the process of rebooting a program is not as dramatic.

Most errors that happen during the early development of programs help improve these programs so that the errors never happen. This is how good kids are raised. They do not repeat the misbehaving because now they have good logic to guide them in a good direction.

Some errors evolve to be exceptions. Exceptions are expected errors. Errors that we can plan for and recover from. The best coding example here is a Network Connection error while we make a program, for example, download some data. This is very much expected because we know network connections could be unreliable so we plan for that error. When that error happens, let’s label the task of downloading that data as incomplete. Queue it somewhere, and re-try it at a later time (see below for an analogy for queuing).

What we did with this planned exception is give the computer a different set of instructions (a different recipe) to do when that error happens. We do exactly that with our kids as well. We give them instructions about what to do in certain future scenarios that we expect (or fear in this case).

// Hey kidsif (stranger.offersYou(chocolate)) { doNotAccept(); doNotTalkTo(stranger); walkAway();}
if (stranger.triesToForceYouToDoSomething()) { screamFor(help); runAway(); call(911);}

Reactive Programming and Streams

Reactive programming is a popular method for writing code that is based on reacting to changes. It is inspired by our everyday life and how we take actions and communicate with others. When performing everyday life activities, we try to multitask when we can but the brain cannot multitask no matter how hard we try. The only way we humans can multitask is to switch tasks and split them efficiently during their lifetime. This makes more sense when the tasks that we need to do require some amount of waiting, which is almost always the case. We actually always switch-tasks, even when we are not aware of it.

Reactive programming is simply to program using, and relying on, events instead of the order of lines in the code. Usually, this involves more than one event, and those events happen in a sequence over time. We call this sequence of events a “stream”.

Think of events as anything that might happen in the future. For example, you know that Jane (a store owner) is always tweeting interesting things on Twitter. Every time she tweets something we call that an “event”. If you look at Jane’s Twitter feed, you have a sequence of “events” happening over time (a stream of events). Reactive programming is named so because we get to “react” to those events. For example, imagine that you are waiting for Jane to tweet a promotional code about something cool she sells in her store. You want to “react” to that tweet and buy the cool thing using the promotional code. In a simplified picture, that is exactly what Reactive programming is all about.

To be able to react to an event, we have to be monitoring it. If we do not track the event, we will never know when to react to it. On Twitter, to monitor the events of Jane tweeting, we follow Jane and set our phone to notify us every time she tweets. When she does, we look at the tweet and make a decision on whether we need to further react to it or not.

In reactive programming, the process of monitoring an event is known as listening or subscribing to the event. This is, in fact, very similar to subscribing to a newsletter. When you subscribe to a newsletter on the Web, you supply your email address. Every time there is a new issue of the newsletter your email address will be used as the way for you to get a copy of the issue. Similarly, we subscribe to an event stream with a function. Every time there is a new event, the stream will use the function to enable our code to react to the event. In this analogy, the newsletter platform is the event stream. Every issue of the newsletter is an event and your email is the function you use to subscribe to the event stream.

Now imagine a dynamic newsletter that allows you to select topics and send you only the news items that match your topics. You are basically filtering the newsletter issues to your liking and that is something we can do on event streams as well. Also, imagine that you have subscribed to several newsletters using different email addresses. You later decided that you want all issues of the newsletters to be sent to a new single email address. One easy thing you can do is to set an email rule that forwards any issues from any newsletter to the new email address. You are basically merging multiple newsletter issues into one email address, which is another thing we can do with event streams.

Another way to think about event streams is to compare them to regular arrays. They are actually very similar. Arrays are a sequence of values in space while event streams are a sequence of values over time. In reactive programming, all the functional operations that we can do on an array. Filtering, reducing, mapping, combining, piping can all be done on event streams. We can filter an event stream, reduce the values of an event stream, map an event stream to another, combine streams, and make one stream an input to another. These are all options that yield new streams of values over time.

Callbacks and Promises

Imagine you ask someone to give you something that needs some time to be prepared. They take your order and your name and tell you to wait to be called when your order is ready. After a while, they call your name and give you what you asked for.

The name you originally gave them is the callback function here. They called it with the object that was requested.

This is like when you order a latte from Starbucks (in the store, not in the drive-thru). They synchronously record your order and name and then you wait until your name is called. When that happens, you receive your latte:

starbucks.makeMeALatte({ type: 'Vanilla', size: 'Grande' }, Samer);
// "Samer" here is the callback function.// When the Latte is ready, the barista will call Samer // with the ready object// We define a function Samer to process the ready object
function Samer(readyLatte) { // drink readyLatte}

Now imagine you ask someone to give you something, but they give you something else. Let’s call it a mystery object. They promise you that this mystery object might eventually turn into the thing you originally asked for.

This promise mystery object can turn into one of two possible forms. One form is associated with success and the other with failure.

This is like when we ask a chicken for a chick and the chicken gives us an egg. That egg might successfully turn into a chick or it might die and be useless.

const egg = chicken.makeChick(); // It's a promise!
egg.then(chick => raiseChick()) // Success outcome .catch(badEgg => throwBadEgg()) // Fail outcome

Queues and Stacks

When we work with elements of data, there are two popular data structures to store and use these elements: A LIFO Stack and a FIFO queue.

LIFO stands for Last In First Out and FIFO stands for First In First Out.

The simplest analogy of a data stack is the stack of dirty dishes in your sink. When you are done using a dish, you stack it on top of the existing dirty dishes until you are ready to wash them.

When you are ready to wash them, you take the last dirty dish that you stacked and you wash that. In computer terminologies, we say you “popped” a dish.

The last dish you stacked is the first dish you washed. This is LIFO.

The simplest analogy of a data queue is the line of people that forms in front of a checkout or order station. When you are ready to pay for your groceries and take them home, you might need to queue yourself in a line until it is your turn.

The first person to arrive at that queue will be the first person to be done with it. This is FIFO.

Pair Programming

You can drive your car on your own when you go to familiar places, but when it is time to go somewhere far for the first time you use a GPS. If you have someone else in the car with you, a better option would be to have them navigate by giving you the instructions on where to turn next. If you do not follow the instructions and end up taking a bad turn, they will let you know immediately and advise you on how to correct it.

Having a navigator next to you when you drive is like having a pair-programmer. You are not driving alone. You are a team with the same goal: to arrive at your destination safely, without any problems, and with the least amount of time and effort.

You can probably do it yourself without a human navigator or a fancy GPS by using the old-school way and checking a map before you leave. If needed, you can check the map again. If you check the map while driving, you might accidentally hit a curb or put a dent in the car. If you stop to check the map, you will be losing time. Without that pair navigator, you are not as safe and/or the journey will take a lot longer.

The experience of your pair navigator might also teach you new things. They might know of a new shortcut that you do not and one that is not on the map. You learn from their relevant experience, and this is beyond valuable.

If you need to go to two destinations and you have two cars. You might be tempted to think that it would be faster to drive solo and do the destinations in parallel. This might be faster in the short term, but all things considered, time might not be the most important factor here. When it comes to computer programs, using one car and making sure it is dent-free at the end of both journeys might be a far more important factor. This why we love pair programming.

Linting and Task Automation

If you have to drive alone on that long trip, you can still make your journey safer by relying on tools. A map is a tool. The GPS is a better tool. Cruise control is another tool.

Tools that automatically warn you if you do something wrong while driving are similar to linting tools for coding. In JavaScript, the best linting tool today is ESLint. It will warn you about so many wrong things you should not be doing while coding. Best of all, it can do that even before you run your program.

Examples of tools that warn you while you are driving are evolving in modern cars. Cars can now warn you when you cross a lane line unexpectedly, or when you try to turn or change a lane while not seeing that hidden car in your blind spot. Additionally, they warn you when you drive over the speed limit, or when you are about to hit something while trying to park in a tight spot.

Linting tools also evolve to provide more accurate and helpful warnings. ESlint always surprises me with very accurate warnings. Additionally, its default recommendations are getting better with each upgrade.

Another analogy that I love in modern cars is automation. Any task that you repeat often should be automated once its purpose and value are clear. Instead of restarting that program every time you save the file, have a monitor process that automates that. Rather than running a format command on your code before you share it with others, have a command that automatically does that every time you commit your code to source control.

Modern cars automate so many things as well. The obvious example here is adaptive cruise control, but other subtle examples include automatic windshield wipers and automatic high beams at night (my favorite!).

Imperative vs Declarative Programming

When you need to do something, there is always the what and the how aspects of it. What exactly needs to be done and how do we do it.

Imperative programming is about the how. Declarative programming is about the what.

What? How? And why should you care?

An imperative approach represents a list of steps. Do this first, then do that, and after that do something else. For example: Go over a list of numbers one by one and for every one add its value to a running sum.

A declarative approach represents what we have and what we need. For example: We have a list of numbers and we need the sum of those numbers. The imperative language is closer to the computers of today because they only know how to execute instructions. The declarative language is closer to how we think and command. Get it done, please. Somehow!

The good news is computer languages have evolved. Computer languages offer declarative ways to do the needed imperative computer instructions. Just as cars have evolved from manual stick shift into automatic and self-driving ones!

Imperative programming is like driving a stick shift car. You need to do manual steps (press the clutch, depress it slowly, change gears incrementally, etc). Declarative programming is like driving an automatic car — you just specify the “what”: Park or Drive.

You cannot program declaratively unless you have the tools that enable you to do so. While you can imperatively drive an automatic car (by switching to manual mode) you cannot declaratively drive a stick shift car. If all you have is a stick shift car, imperative programming is your only obvious choice. This is unless you take the time to install an automatic gear shifter, which might be worth it in the long term. If you can afford a new car, you will probably go with an automatic one unless you are that true nerd who still likes to program with Assembly!

Assemblyis the original true imperative low-level computer language with pure instructions that directly translate into machine code.

Note that imperative programming might produce faster programs. Additionally, declarative programming requires less effort from you. In general, it will also require less effort to be maintained. Coding does not have to be one way or the other. Any non-trivial computer program will most likely have a little bit of both approaches. Also, knowing how to code declaratively is great, but it does not mean that you do not need to learn the imperative ways as well. You should simply be confident using both.

Værktøjer, der gør det muligt for dig at programmere erklærende, udvikler sig til bedre og hurtigere måder at bringe dig dit sted, du er på vej hen. Den ultimative erklærende oplevelse med moderne biler er de selvkørende. "Hvad" bliver destinationen, og bilen vil gøre resten. Dette er sandsynligvis også fremtiden for programmering. Vi vil have programmer, der forstår alle mål, og de kan bare bruge deres magi til at generere logik for at få os til disse mål.

Hvad er din foretrukne analogi? Giv mig besked i svaret nedenfor.

Tak for læsningen!