跟讀練習: System Design Mock Interview: Design Facebook Messenger - 通過影片學習英語口語

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How would you design a real-time messaging app that can support millions of users?

In this video, we'll be talking about how to design and build an application like Facebook Messenger, WhatsApp, Discord, or Slack.

We'll be talking about the high-level architecture of these systems, as well as some specific features like how to build real-time messaging,

group messaging, image and video uploads, as well as push notifications.

Now, we'll be talking about some best practices as well along the way about how to build and scale a reliable system.

Now before we dive into some specific features and components of our system,

let's talk a little bit about some of the high-level features and goals that we want to implement along the way.

Now, I think some important product goals that we want to support obviously are real-time messaging from one individual to another,

as well as group messaging that can support multiple users.

We want to support maybe something like an online status to show whether a user is currently online or offline,

as well as supporting image and video uploads in addition to text messages.

We also may want to support some extra bells and whistles like read receipts or push notifications, so we'll get to those if we have time.

And then we also want to keep in mind some technical goals and constraints here, so thinking about how to build a low latency system to support real-time messaging

and what applications or technologies we need to sort of choose to make that happen.

We also want to build something that can support a really high volume of requests.

So, you know, millions of users potentially writing messages at the same time.

We also want to make sure, of course, that our system is highly reliable and available.

And we also want to make sure that our messaging system is secure and doesn't, you know, result in users being sent messages they shouldn't receive.

Alright, great.

So now that we've laid out some of the features we want to support, let's Let's talk about the overall architecture of this app

and specifically let's talk about how we're going to send messages from one user to another.

So let's pretend we have two users here and we want to send a message between them.

So the reason we need to use a chat server application in this case is

because on the internet it's actually really hard to establish a direct connection from one user to another

and to make that connection reliable.

And in addition we also want to support things like storing and retrieving message histories later.

We need to have our chat server here in order to sort of establish

and broker this connection and to sort of store these messages so they can be retrieved from different devices later in time.

So I'm going to go ahead and draw in our chat API server.

And then we also need to sort of establish a connection from users to this chat server.

Now let's think about what happens when user A sends a message to user B.

What we want to happen is the user sends a message to the server

and the server relays that message instantly to the user that it's intended for.

However, this kind of breaks the model of how HTTP requests work on the internet because they can't actually be server initiated, they have to be client initiated.

So something about this isn't going to work and we're going to have to come up with something else.

Now I've got a few options in mind, so I'm going to talk about those and discuss their trade-offs.

The first one we can do is something called HTTP polling.

And in this model, instead of just sending one request to the server, we're going to sort of repeatedly ask the server if there's any new information available.

And so most of the time the server is going to reply with no, there's no new information available.

And then once in a while it'll say, hey, I received a new message for you, and it will reply with that.

For a variety of reasons, this is probably not the right solution for this problem, because it means we're going to be sending a lot of unnecessary requests to our server

and it also means that we're going to have pretty high latency.

So we're only going to be able to receive messages when we ask for them, not when they're actually received by the server.

Now, the second option we have is something called long polling.

And in this model, we're still using sort of a traditional HTTP request, but instead of resolving immediately with the result, we're actually going to have the server hold on to the request

and wait until data is available before it replies with the result.

So in this way, we sort of maintain an open connection with the server at all times.

And then once data is sent back, we immediately request a new connection and then we keep that open until data is available.

Now, this is a little bit better because it solves our latency problems somewhat

and we don't have to create all these unnecessary requests all the time.

However, we have to maintain this open connection, and

if there's lots of data coming from the server It means

we still have to initiate a new request to get the next piece of data

So while it's good for some systems like notifications and things like

that It's probably not the best for a real-time chat application like we're trying to build here

So the third option we have is something called WebSockets

And this is the solution I'm going to recommend because it was sort of designed for this application Now in WebSockets, we still maintain an open connection with the server,

but instead of being just a one-way connection, it's actually a full duplex connection.

So now we can send up data to the server, and the server can push down data to us, and this connection is maintained and kept open for the duration of the session.

So this architecture presents some unique challenges for us

because there's some practical limitations to how many open connections a server can have at one time.

WebSockets is built on the TCP protocol, which has about 16 bits for the port number.

This means there's a real limitation of about 65,000 connections that any one server can have open at a time.

So instead of having one API server, we're obviously going to need to have a lot of servers to handle all of these WebSocket connections.

And we're going to need a load balancer

or gateway sitting in front of them to help balance these connections and route them to the correct server.

So I'm going to go ahead and replace this with a load balancer, and I'm going to draw in some API servers here instead.

All right, great.

So I'm just sort of visualizing this with about three servers, but if we're trying to support millions or hundreds of millions of users

and we can only support thousands of requests per server, that means we're going to have hundreds or thousands of these servers serving requests and keeping these connections open.

So our system is going to have to reach a pretty massive scale here.

And in addition, we now have a new problem

because before we were able to sort of send a message from one user to another via our chat API server.

However, now we have a distributed system and we need to be able to communicate from one API server to another.

And in fact, we've actually created sort of another messaging problem here, because these API servers need to know how to talk to each other.

So one model we can adopt here, one design pattern we can use, is something like a PubSub message queue.

And so that fits nicely into this problem

because it's sort of a natural solution for a messaging problem between servers in a distributed system.

So I'm going to go ahead and draw in a message service here.

which is going to sort of implement this message queue.

And the idea here is that each API server will publish messages into this centralized queue

and subscribe to updates for the users that it's connected to.

That way when a new message comes in, it can be added to the queue

and any service that's sort of listening for messages for

that user can then receive that update and forward the message onto the user.

So that's how that would work.

Now we still need to think about how we're going to store and persist these messages

in our database and how to sort of model this relationship between messages and users.

So let's go ahead and draw in a database here and think about how that's going to work.

Now when we think about what kind of database to choose for application, let's think back to the beginning where we set out our requirements for the system.

So we know we want to support a really large volume of requests and store a lot of messages.

And we also care a lot about the availability and uptime of our service.

So we want to pick a database that's going to fit these requirements.

And we know from things like the CAP theorem

that there's going to be these sort of universal trade-offs between principles like consistency, availability, and ability to partition or shard our database.

And so we want to focus on this ability to shard and partition and keep our database available

rather than things like consistency, which are less important in a messaging application than they would be in something like a financial application.

So with that in mind, I think I would choose something like a NoSQL database that has sort of built-in replication and sharding ability.

So something like Cassandra or HBase would be great for this application.

100

All right, so now let's talk about how we're going to store and model this data in our database.

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We know we're going to need a few key tables and features like users, messages, and then we're also going to probably need this concept of conversations,

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which will be groups of users who are supposed to receive messages.

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So I'm going to go ahead and draw those tables in.

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So starting with the users table, we're going to have a unique ID for this user.

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We're also going to have something like a username or a name for them to display.

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And then we're probably also going to want to have something like a last active timestamp.

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And the idea here is this, this would allow us to support features like online status

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or being able to see when a user was last online

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and this would just be a simple timestamp that would be the date of their last activity.

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Alright, so next we're going to need a messages table

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and again this is going to have a unique ID

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but it's also going to store a reference to the user who made the message

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and we're also going to have a reference or an ID for the conversation that it belongs to.

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We'll talk more about that in a moment.

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But then obviously at the end, we're also going to need the text of the message as well.

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And if we want to support something like media uploads, like images or videos, we also want to store something like a media URL here in our database as well.

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This won't be the actual data, but it'll be the URL where the user can access this data to download it.

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All right, so next we're going to need this conversations table that I was talking about.

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And the idea here is that this would simply just be an ID and perhaps something like a name.

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So in the case of an application like Slack or Discord, this could be the channel name of the conversation.

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So that's sort of an optional string that we could store here.

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All right, great.

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So the last thing we need is a way to query

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and understand which users are part of a conversation and which conversations a user is part of.

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So for that, I'm going to add one more table here that I'm going to call conversation users.

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And it's just going to sort of mapping from a conversation ID to our user ID.

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So the idea is that there'd be a row here for every user in a conversation.

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And we'd be able to sort of index these in order to do queries like we were just talking about.

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OK, great.

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So now that we've talked about how we're going to store data in our database,

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let's revisit our overall architecture for a moment and think about how we can make the system more scalable and more performant.

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So in particular, one thing I'm thinking about is the cost of going to our database and retrieving messages from it repeatedly.

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So one thing I'd like to add here is some sort of caching service or caching layer, which would be like a read through cache

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that we can store in memory so

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that we don't always have to go to our database and fetch messages from it directly.

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So I'm just going to draw that in here.

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Now, another thing we talked about but didn't really discuss very much is how we're going to store media,

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So images and videos and how we can upload those to the correct place.

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Now we're not going to store those in our database, but instead we're going to choose some sort of other storage platform like an object storage service like Amazon S3.

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So I'm going to go ahead and draw that in up here.

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And the idea is that when our API receives a request to upload some sort of content, we'll actually just forward it onto this object storage system.

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and then we'll store the URL of that object alongside our message as we discussed before.

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On the user side when you receive a message

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that contains some sort of media you'll then go and fetch that URL separately in order to download it.

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Now in order to make

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that more efficient we'll also want to add some sort of caching here

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and in this case we would use something like a CDN

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and the idea is that the user would request the resource from the CDN and if it's cached already, that's great.

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And if it's not, the CDN would request that object from the object storage service in case there's a cache miss.

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All right, great.

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Now the last thing we want to add here is some

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sort of way to notify users who are offline about messages they may have missed.

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So in this case, we might want to have some sort of notification service here

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that's also going to be be contacted by our message service in the event that the user is offline.

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So in this case, our message service will contact our notification service, and our notification service will forward that notification onto the user,

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but probably via some sort of third-party API for iOS devices or Android devices, or perhaps even through some sort of mail service.

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All right, we've covered a lot of ground, and we've talked about everything from choosing the right network protocol

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for our clients to building a distributed messaging queue system in our back end

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and picking the right database and data model to store all these messages.

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I think with that we've covered pretty much everything we need to talk about.

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And while we could go more in depth on each of these topics, I think hopefully you have the big picture

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and the overview of how we would build this application

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and have learned some of the important technical decisions and trade-offs

164

that we need to be thinking about when we implement something like this.

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本課的詞彙與口語要點

這節 C1 級別的口語課以影片“System Design Mock Interview: Design Facebook Messenger”為素材。影片中反覆出現的詞有：user, message, server, connection, request。這段影片共有 164 個句子、2722 個單詞可供跟讀。講話部分時長為 14:39。說話人語速較快，每分鐘約 186 個詞，會出現較多連讀和弱讀。 87% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。

影片中的重點詞彙

影片中最難的 15 個單詞，附發音和釋義：

單詞	發音	釋義
notification 名詞	/ˌnoʊtɪfɪˈkeɪʃn̩/	通知
cache 名詞	/kæʃ/	快取 /快取, 快取
queue 名詞	/kju/	佇列 /佇列
upload 動詞	/ˈʌpˌloʊd/	上載 /上載, 上傳 /上傳
latency 名詞	/ˈleɪ.tən.si/	延遲 /延遲
retrieve 動詞	/ɹɪˈtɹiːv/	重拾
offline 形容詞	/ɔfˈlaɪn/	離線 /離線, 離線 /離線
distribute 動詞	/dɪˈstɹɪbjuːt/	分配
timestamp 名詞	/ˈtaɪmˌstæmp/	時間戳 /時間戳, 時戳 /時戳
query 名詞	/ˈkwɪɹ.i/	問題 /問題, 質問 /質問
fetch 動詞	/fɛt͡ʃ/	取
limitation 名詞	/lɪmɪˈteɪʃən/	限制
constraint 名詞	/kənˈstɹeɪnt/	約束 /約束
replication 名詞	/ɹɛplɪˈkeɪʃən/	複製
revisit 動詞	/ɹiːˈvɪzɪt/	回顧 /回顧

影片中出現的短語動詞

單詞	發音	釋義
go ahead 動詞	/ˌɡoʊ əˈhɛd/	請便 /請便
come up with 動詞		想出, 提出
set out 動詞	/ˈsɛt‿aʊt/	動身 /動身

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	being sent · can be retrieved · is sent
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	we've laid · we've actually created · we've talked
定語從句 who / which + 從句 — 補充說明人或事物	protocol, which has · consistency, which are

需要注意的發音

說話人用了 71 次縮略和弱讀形式，例如 we're, I'm, we'll。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: notification /ˌnoʊtɪfɪˈkeɪʃn̩/, cache /kæʃ/, shard /ˈʃɑːd/, initiate /ɪˈnɪʃ.i.eɪt/, partition /pɑɹˈtɪ.ʃən/
長單詞——注意重音位置: notification /ˌnoʊtɪfɪˈkeɪʃn̩/, initiate /ɪˈnɪʃ.i.eɪt/, limitation /lɪmɪˈteɪʃən/, consistency /kənˈsɪs.tən.si/, replication /ɹɛplɪˈkeɪʃən/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: retrieve /ɹɪˈtɹiːv/, revisit /ɹiːˈvɪzɪt/, visualize /ˈvɪʒuəˌlaɪz/
詞尾子音 — 要讀清楚，後面不要加母音: cache /kæʃ/, upload /ˈʌpˌloʊd/, shard /ˈʃɑːd/, retrieve /ɹɪˈtɹiːv/, initiate /ɪˈnɪʃ.i.eɪt/
/r/ 和 /l/ 的區別: centralize /ˈsɛntɹəlaɪz/, replication /ɹɛplɪˈkeɪʃən/, relay /ˈɹiːleɪ/, separately /ˈsɛp.ɹət.li/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 notification, cache, queue 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: System Design Mock Interview: Design Facebook Messenger - 通過影片學習英語口語

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本課的詞彙與口語要點

影片中的重點詞彙

影片中出現的短語動詞

影片中的語法

需要注意的發音

如何用這段影片練習

什麼是跟讀法？