跟讀練習: System Design: How to Build an API Rate Limiter - 通過影片學習英語口語
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42 句
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1
Let's design an API rate limiter for an application that takes thousands of requests per second.
2
User journey is simple.
3
User can make API calls to your server to do something.
4
Each user is allowed to make 100 API calls per minute.
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If a user makes more than 100 calls per minute, then your server will not process those API calls.
6
Most engineers approach this problem with a counter.
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For each user, you store user ID, count of API calls, and current minute.
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Every time a request is made by user, you increase this count, and you will reject the requests once it passes 100 requests.
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This implementation is good enough for internal API calls, but let's take two specific examples why this solution is not good.
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Let's say user Alan is a burst user, and makes 100 API calls in first two seconds.
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Then he will get rejected for the next 58 seconds.
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Alan will be able to make calls when next minute starts.
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And our second user, John, sends 100 API requests, but these requests are sent at the last second of the current minute.
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The next second clock will reset to new minute, and John can send another 100 requests.
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Basically, John sent 200 requests in 2 seconds, because according to Counter, these 100 requests belong to different minutes.
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Basically, users will experience different behavior of APIs based on when they make the API call.
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There is a fundamental issue with the approach we just discussed.
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The Counter is watching the clock instead of watching the user.
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Let's see a better approach.
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So, we will give every user a bucket that holds 100 tokens.
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Every API request from user takes one token out, and we will keep filling the bucket at a rate of 100 tokens per minute, which is approximately 1.67 tokens per second.
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For simplification, let's assume we will fill two tokens per second.
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If user doesn't make any API call, the bucket will remain at 100, and we don't need to fill.
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If the bucket is empty, we will reject the request.
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This token bucket approach is what Stripe, GitHub, and most API gateways actually use for tracking API calls.
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Now let's take a look at Alan and John's requests again, and see how this bucket system will work.
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Alan drains all 100 tokens in 2 seconds.
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Then Alan has to slow down.
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Bucket fills about 2 tokens per second.
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That's why he can now make 2 calls per second.
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John, who is making 100 API calls at the end of the minute, will also need to wait for Bucket to fill, because once he makes 100 requests in a second, Bucket will get empty.
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So he can also make about 2 per second, as Bucket fills with time.
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Next question is, how do we store these buckets?
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We just need to store the token count, and the time of the last refill, so that you can add tokens per second.
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But what if your app works at large scale, and you have 20 different servers accepting API calls.
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Two requests for the same user can land on two servers in the same millisecond.
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Therefore, you keep the bucket outside the servers, in one common shared storage.
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This storage needs to be fast, and that's why we usually use Redis.
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When any of these 20 servers processes any API call, we change the count at this common Redis.
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To make this crystal clear in your mind, you need to remember that request limit is not just one number.
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It's two numbers.
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First, how much a user can burst in one go, and second, how fast they can go after the burst.
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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: How to Build an API Rate Limiter”為素材。 影片中反覆出現的詞有：Api, user, request, bucket, token。 這段影片共有 42 個句子、594 個單詞可供跟讀。 講話部分時長為 2:47。 說話人語速較快，每分鐘約 213 個詞，會出現較多連讀和弱讀。 82% 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 11 個單詞，附發音和釋義：
| 單詞 | 發音 | 釋義 |
|---|---|---|
| bucket 名詞 | /ˈbʌkɪt/ | 水桶, 吊桶 |
| token 名詞 | /ˈtoʊkən/ | 代幣 /代幣, 籌碼 /籌碼 |
| server 名詞 | /ˈsɝvɚ/ | 伺服器 /伺服器, 伺服器 |
| burst 動詞 | /bɜːst/ | 爆裂, 破裂 |
| crystal 名詞 | /ˈkɹɪstəl/ | 結晶 /結晶 |
| belong 動詞 | /bɪˈlɒŋ/ | 屬於 /屬於 |
| fundamental 形容詞 | /ˌfʌn.dəˈmɛn.təl/ | 基本 |
| implementation 名詞 | /ˌɪmplɪmənˈteɪʃən/ | 履行 |
| reset 動詞 | /ɹiːˈsɛt/ | 重置 |
| millisecond 名詞 | /ˈmɪlɪˌsɛkənd/ | 毫秒 |
| simplification 名詞 | /ˌsɪm.plɪ.fɪˈkeɪ.ʃən/ | 簡單化 /簡單化, 簡化 /簡化 |
影片中出現的短語動詞
| 單詞 | 釋義 |
|---|---|
| slow down 動詞 | 減速 /減速, 放慢 |
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 |
|---|---|
| 條件句 if + 從句，will/would + 動詞 — 條件及其結果 | If the bucket is empty, we will reject |
| 被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的 | is allowed · is made · are sent |
需要注意的發音
- “sh” 和 “zh” 音: implementation /ˌɪmplɪmənˈteɪʃən/, simplification /ˌsɪm.plɪ.fɪˈkeɪ.ʃən/
- 長單詞——注意重音位置: fundamental /ˌfʌn.dəˈmɛn.təl/, implementation /ˌɪmplɪmənˈteɪʃən/, millisecond /ˈmɪlɪˌsɛkənd/, simplification /ˌsɪm.plɪ.fɪˈkeɪ.ʃən/
中文母語者容易讀錯的音：
- 詞尾子音 — 要讀清楚，後面不要加母音: bucket /ˈbʌkɪt/, burst /bɜːst/, clock /klɑk/, reset /ɹiːˈsɛt/, millisecond /ˈmɪlɪˌsɛkənd/
- /r/ 和 /l/ 的區別: crystal /ˈkɹɪstəl/, refill /ˈɹiː.fɪl/
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 bucket, token, server 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。