쉐도잉 연습: 100+ Docker Concepts you Need to Know - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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Welcome to Docker 101.
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If your goal is to ship software in the real world, one of the most powerful concepts to understand is containerization.
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When developing locally, it solves the age-old problem of it works on my machine, and when deploying in the cloud, it solves the age-old problem of this architecture doesn't scale.
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Over the next few minutes, we'll unlock the power inside this container by learning 101 different concepts and terms related to computer science, the cloud, and of course Docker.
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I'm guessing you know what a computer is, right?
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box that has three important components inside.
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A CPU for calculating things, random access memory for the applications you're using right now, and a disk to store things you might use later.
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This is bare metal hardware, but in order to use it, we need an operating system.
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Most importantly, the OS provides a kernel that sits on top of the bare metal, allowing software applications to run on it.
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In the olden days, you would go to the store and buy software to physically install it on your machine.
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But nowadays, most software is delivered via the internet through the magic of networking.
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When you watch a YouTube video, your computer is called the client, but you and billions of other users are getting that data from remote computers called servers.
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When an app starts reaching millions of people, weird things begin to happen.
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The CPU becomes exhausted handling all the incoming requests, disk IO slows down, network bandwidth gets maxed out, and the database becomes too large to query effectively.
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On top of that, you wrote some garbage code that's causing race conditions, memory leaks, and unhandled errors that will eventually grind your server to a halt.
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The big question is how do we scale our infrastructure?
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A server can scale up in two ways, vertically or horizontally.
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To scale vertically, you take your one server and increase its RAM and CPU.
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This can take you pretty far, but eventually you hit a ceiling.
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The other option is to scale horizontally, where you distribute your code to multiple smaller servers, which are often broken down into microservices that can run and scale independently.
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But distributed systems like this aren't very practical when talking about bare metal, because actual resource allocation varies.
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One way engineers address this is with virtual machines, using tools like Hypervisor.
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It can isolate and run multiple operating systems on a single machine.
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That helps, but a VM's allocation of CPU and memory is still fixed.
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And that's where Docker comes in, the sponsor of today's video.
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Applications running on top of the Docker engine all share the same host operating system kernel
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and use resources dynamically based on their needs.
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Under the hood, Docker's running a daemon or persistent process that makes all this magic possible and gives us OS-level virtualization.
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What's awesome is that any developer can easily harness this power by simply installing Docker Desktop.
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It allows you to develop software without having to make massive changes to your local system.
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But here's how Docker works in three easy steps.
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First, you start with a Docker file.
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This is like a blueprint that tells Docker how to configure the environment that runs your application.
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The Docker file is then used to build an image, which contains an OS, your dependencies, and your code, like a template for running your application.
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And we can upload this image to the cloud to places like Docker Hub and share it with the world.
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But an image by itself doesn't do anything.
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You need to run it as a container, which itself is an isolated package running your code that in theory could scale infinitely in the cloud.
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Containers are stateless, which means when they shut down, all the data inside them is lost.
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But that makes them portable, and they can run on every major cloud platform without vendor lock-in.
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Pretty cool, but the best way to learn Docker is to actually run a container.
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Let's do that right now by creating a Docker file.
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A Docker file contains a collection of instructions, which by convention are in all caps.
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From is usually the first instruction you'll see, which points to a base image to get started.
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This will often be a Linux distro, and may be followed by a colon, which is an optional image tag, and in this case specifies the version of the OS.
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Next, we have the working directory instruction, which creates a source directory and CDs into it, and that's where we'll put our source code.
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All commands from here on out will be executed from this working directory.
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Next, we can use the run instruction to use a Linux package manager to install our dependencies.
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Run lets you run any command just like you would from the command line.
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Currently we're running as the root user, but for better security we could also create a non-root user with the user instruction.
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Now we can use copy to copy the code on our local machine over to the image.
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You're halfway there, let's take a brief intermission.
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Now to run this code we have an API key, which we can set as an environment variable with the env instruction.
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We're building a web server that people can connect to, which requires a port for external traffic.
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Use the expose instruction to make that port accessible.
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Finally, that brings us to the command instruction, which is the command you want to run when starting up a container.
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In this case, it will run our web server.
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There can only be one command per container, although you might also add an entry point, allowing you to pass arguments to the command when you run it.
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That's everything we need for the Dockerfile.
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But as an added touch, we could also use label to add some extra metadata, or we could run a health check to make sure it's running properly,
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or if the container needs to store data that's going to be used later or be used by multiple containers, we could mount a volume to it with a persistent disk.
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Okay, we have a Dockerfile, so now what?
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When you install Docker Desktop, that also installed the Docker CLI, which you can run from the terminal.
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Run Docker help to see all the possible commands, but the one we need right now is Docker build, which will turn this Docker file into an image.
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When you run the command, it's a good idea to use the T flag to tag it with a recognizable name.
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Notice how it builds the image in layers.
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Every layer is identified by a SHA-256 hash, which means if you modify your Docker file, each layer will be cached, so it only has to rebuild what has actually changed,
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and that makes your workflow as a developer far more efficient.
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In addition, it's important to point out that sometimes you don't want certain files to end up in a Docker image, in which case you can add them to the Docker ignore file to exclude them from the actual files
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that get copied there.
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Now open Docker Desktop and view the image there.
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Not only does it give us a detailed breakdown, but thanks to Docker Scout, we're able to proactively identify any security vulnerabilities for each layer of the image.
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It works by extracting the software bill of material from the image and compares it to a bunch of security advisory databases.
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When there's a match, it's given a severity rating, so you can prioritize your security efforts.
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But now the time has finally come to run a container.
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We can accomplish that by simply clicking on the run button.
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Under the hood, it executes the docker run command, and we can now access our server on localhost.
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In addition, we can see the running container here in docker desktop, which is the equivalent to the docker psst command,
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which you can run from the terminal to get a breakdown of all the running and stop containers on your machine.
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If we click on it though, we can inspect the logs from this container, or view the file system, and we can even execute commands directly inside the running container.
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Now when it comes time to shut it down, we can use Docker stop to stop it gracefully, or Docker kill to forcefully stop it.
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You can still see the shutdown container here in the UI, or use remove to get rid of it.
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But now you might want to run your container in the cloud.
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Docker push will upload your image to a remote registry, where it can then run on a cloud like AWS with Elastic Container Service,
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or it can be launched on serverless platforms like Google Cloud Run.
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Conversely, you may want to use someone else's Docker image, which can be downloaded from the cloud with Docker Pool.
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And now you can run any developer's code without having to make any changes to your local environment or machine.
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Congratulations, you're now a bona fide and certified Docker expert.
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I hereby grant you permission to print out this certificate and bring it to your next job interview.
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But Docker itself is only the beginning.
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There's a good chance your application has more than one service, in which case you'll want to know about Docker Compose, a tool for managing multi-container applications.
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It allows you to define multiple applications and their Docker images in a single YAML file, like a frontend, a backend, and a database.
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The Docker Compose up command will spin up all the containers simultaneously, while the down command will stop them.
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That works on an individual server, but once you reach massive scale, you'll likely need an orchestration tool like Kubernetes to run and manage containers all over the world.
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It works like this.
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You have a control plane that exposes an API that can manage the cluster.
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Now, the cluster has multiple nodes or machines, each one containing a kubelet and multiple pods.
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A pod is the minimum deployable unit in Kubernetes, which itself has one or more containers inside of it.
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What makes Kubernetes so effective is that you can describe the desired state of the system, and it will automatically scale up or scale down,
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while also providing fault tolerance to automatically heal if one of your servers goes down.
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It gets pretty complicated, but the good news is that you probably don't need Kubernetes.
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It was developed at Google based on its Borg system and is really only necessary for highly complex high-traffic systems.
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If that sounds like you though, you can also use extensions on Docker Desktop to debug your pods.
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And with that, we've looked at 100 concepts related to containerization.
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Big shout out to Docker for making this video possible.
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Thanks for watching, and I will see you in the next one.
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이 레슨의 어휘와 말하기 포인트
이 영상에는 섀도잉할 문장 105개와 단어 1837개가 있습니다. 말하는 구간의 길이는 8:27입니다. 화자는 분당 약 217단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 79%뿐이라 어휘가 어려운 편입니다.
이 영상의 핵심 어휘
영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| container 명사 | /kənˈteɪ.nəɹ/ | 그릇, 용기 |
| server 명사 | /ˈsɝvɚ/ | 서버, 봉사기 |
| cloud 명사 | /ˈklaʊ̯d/ | 구름 |
| desktop 명사 | /ˈdɛsktɒp/ | 탁상 |
| layer 명사 | /ˈleɪ̯ɚ/ | 층 |
| install 동사 | /ɪnˈstɔl/ | 설치하다, 깔다 |
| database 명사 | /ˈdeɪtəˌbeɪs/ | 데이터베이스 |
| disk 명사 | /dɪsk/ | 디스크, 원반 |
| execute 동사 | /ˈɛksɪˌkjuːt/ | 처형하다 |
| directory 명사 | /dɪˈɹɛktəɹi/ | 디렉토리, 폴더 |
| package 명사 | /ˈpæk.ɪd͡ʒ/ | 짐, 꾸러미 |
| solve 동사 | /sɒlv/ | 해결하다 |
| remote 형용사 | /ɹəˈməʊt̞/ | 원격, 먼 |
| terminal 명사 | /ˈtɚmɪnəl/ | 터미널, 공항 터미널 |
| breakdown 명사 | /ˈbɹeɪkˌdaʊn/ | 고장 |
영상에 나오는 구동사
| 단어 | 뜻 |
|---|---|
| go down 동사 | 내려가다 |
| point out 동사 | 가리키다 |
주의할 발음
화자는 we're, you'll, you're 같은 축약형과 약화된 형태를 18번 사용합니다. 들리는 대로 짧게 발음하세요.
- “sh”와 “zh” 소리: instruction /ɪnˈstɹʌkʃən/, allocation /ˌæl.əˈkeɪ.ʃən/, extension /ɪkˈstɛnʃən/, efficient /ɪˈfɪʃənt/, congratulations /kənˌɡɹæt͡ʃəˈleɪʃ(ə)nz/
- 긴 단어 — 강세 위치에 주의: directory /dɪˈɹɛktəɹi/, developer /dɪˈvɛləpɚ/, allocation /ˌæl.əˈkeɪ.ʃən/, dependency /dɪˈpɛndənsi/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/
이 영상으로 연습하는 방법
- 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
- 0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
- 자신의 목소리를 녹음해 원본과 비교하고, container, server, cloud 같은 단어에 특히 주의합니다.
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | is delivered · is called · are often broken |
| 관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보 | servers, which are · stateless, which means · colon, which is |
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | has actually changed · has finally come · we've looked |
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.