跟读练习: 100+ Docker Concepts you Need to Know - 通过视频学习英语口语
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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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本课的词汇与口语要点
这段视频共有 105 个句子、1837 个单词可供跟读。 讲话部分时长为 8:27。 说话人语速较快，每分钟约 217 个词，会出现较多连读和弱读。 只有 79% 的单词属于英语最常用的 3,000 词，词汇难度较高。
视频中的重点词汇
视频中 15 个值得学习的单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| container 名词 | /kənˈteɪ.nəɹ/ | 容器 |
| server 名词 | /ˈsɝvɚ/ | 服務器 /服务器, 伺服器 |
| cloud 名词 | /ˈklaʊ̯d/ | йүн, 雲 /云 |
| instruction 名词 | /ɪnˈstɹʌkʃən/ | 教導 /教导 |
| desktop 名词 | /ˈdɛsktɒp/ | 桌面 |
| layer 名词 | /ˈleɪ̯ɚ/ | 層 /层 |
| install 动词 | /ɪnˈstɔl/ | 安裝 /安装 |
| database 名词 | /ˈdeɪtəˌbeɪs/ | 數據庫 /数据库 |
| disk 名词 | /dɪsk/ | 圓盤 /圆盘 |
| execute 动词 | /ˈɛksɪˌkjuːt/ | 處決 /处决, 處死 /处死 |
| directory 名词 | /dɪˈɹɛktəɹi/ | 名錄 /名录, 簿 |
| package 名词 | /ˈpæk.ɪd͡ʒ/ | 包裹, 包 |
| solve 动词 | /sɒlv/ | 解決 /解决 |
| remote 形容词 | /ɹəˈməʊt̞/ | 遠 /远, 遙遠 /遥远 |
| terminal 名词 | /ˈtɚmɪnəl/ | 航站, 航廈 /航厦 |
视频中出现的短语动词
| 单词 | 释义 |
|---|---|
| go down 动词 | 下降, 下去 |
| point out 动词 | 指, 指出 |
| slow down 动词 | 減速 /减速, 放慢 |
需要注意的发音
说话人用了 18 次缩略和弱读形式，例如 we're, you'll, you're。请按听到的简短形式来说。
- “sh” 和 “zh” 音: instruction /ɪnˈstɹʌkʃən/, allocation /ˌæl.əˈkeɪ.ʃən/, extension /ɪkˈstɛnʃən/, efficient /ɪˈfɪʃənt/, congratulations /kənˌɡɹæt͡ʃəˈleɪʃ(ə)nz/
- 长单词——注意重音位置: directory /dɪˈɹɛktəɹi/, developer /dɪˈvɛləpɚ/, allocation /ˌæl.əˈkeɪ.ʃən/, dependency /dɪˈpɛndənsi/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 container, server, cloud 这类单词。
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | is delivered · is called · are often broken |
| 定语从句 who / which + 从句 — 补充说明人或事物 | servers, which are · stateless, which means · colon, which is |
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | has actually changed · has finally come · we've looked |
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。