跟讀練習: Top 5 Redis Use Cases - 通過影片學習英語口語

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In this video, we discover the versatility of Redis.

We'll talk about the top use cases that have been better tested in production at various companies and at various scales.

Get insights into how Redis solves interesting scalability challenges for us

and learn why it is a great tool to know well in our system design toolset.

Let's dive right into it.

First of all, what is Redis and why do people use it?

Redis is an in -memory data structure store.

It is most commonly used as a cache.

It supports many data structures such as strings, hashes, lists, sets, and sorted sets.

Redis is known for its speed.

We made a video to explain why Redis is so fast.

Look for the link in the description if you would like to watch that video.

Now let's dive into the top use cases of Redis.

The number one use case of Redis is caching objects to speed up web applications.

In this use case, Redis stores frequently requested data in memory.

It allows the web servers to return frequently accessed data quickly.

This reduces the load on the database and improves the response time for the application.

At scale, the cache is distributed among a cluster of Redis servers.

Sharding is a common technique to distribute and caching load evenly across the cluster.

Other topics to consider when deploying Redis as a distributed cache include setting a correct TTL

and handling thundering hurt on cold start.

Another common use case is to use Redis as a session store to share session data among stateless servers.

When a user logs into a web application, the session data is stored in Redis,

along with a unique session ID that is returned to the client as a cookie.

When a user makes a request to the application, the session ID is included in the request,

and the stateless web server retrieves the session data from Redis using the ID.

It is important to note that Redis is an in -memory database.

The session data stored in Redis will be lost if the Redis server restarts.

Even though Redis provides persistent options like a snapshot and AOF or append -only file,

they allow session data to be saved to disk and reloaded into memory in the event of a restart.

These options often take too long to load to be practical.

In production, replication is usually used instead.

In this case, data is replicated to a backup instance.

In the event of a crash of the main instance, the backup instance is quickly promoted to take over the traffic.

The next use case is distributed log.

Distributed logs are used when multiple nodes in an application need to coordinate access to some shared resource.

Redis is used as a distributed log with its atomic commands like set -nx or set -if -not -exists.

It allows a caller to set a key only if it does not already exist.

Here's how it works at a high level.

Client 1 tries to acquire the log by setting a key with a unique value

and a timeout using the set -nx command.

If the key was not already set, the set -nx command returns 1, indicating that the log has been acquired by client 1.

Client 1 finishes its work and releases the log by deleting the key.

Now if the key was already set, the set -annex command returns 0, indicating that the log has already been held by another client.

In this case, client 1 waits and retries the set -annex operation until the log is released by the other client.

Note that this simple implementation might be good enough for many use cases, but it is not completely fault -tolerant.

For production use, there are many Redis client libraries that provide high -quality distributed log implementation built right off the box.

Next up is Ray Limiter.

Redis can be used as a Ray Limiter by using its increment command on some counters

and setting expiration times on those counters.

A very basic

Ray Limiting algorithm works this way.

For each incoming request, the request IP or user ID is used as a key.

The number of requests for the key is incremented using the increment command in Redis.

The current count is compared to the allowed rate limit.

If the count is within the rate limit, the request is processed.

If the count is over the limit, the request is rejected.

The keys are set to expire after a specific time window, for example, a minute, to reset the cans for the next time window.

More sophisticated rate limiters, like the leaky bucket algorithm, can also be implemented using Redis.

The last use case we would like to talk about is gaming leaderboard.

For most games that are not super high scale, Redis is a delightful way to implement various types of gaming leaderboards.

Sort of sets are the fundamental data structure that enables this.

A sort of set is a collection of unique elements, each with a score associated with it.

The elements are sorted by score.

This allows for quick retrieval of the elements by score in logarithmic time.

That's it for our top Redis use cases.

Redis is very versatile.

There are many different ways to use it.

Some features are more better tested than others.

Our creativity is the limit.

Use it with care and have fun with it.

You would like our videos.

You may like our weekly system design newsletter as well.

It covers topics and trends in large -scale system design, trusted by 200 ,000 readers.

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本課的詞彙與口語要點

這節口語課以影片“Top 5 Redis Use Cases”為素材。影片中反覆出現的詞有：Redis, log, client, session, key。這段影片共有 75 個句子、937 個單詞可供跟讀。講話部分時長為 6:16。說話人語速平穩，每分鐘約 149 個詞，很適合跟讀。只有 77% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
distribute 動詞	/dɪˈstɹɪbjuːt/	分配
server 名詞	/ˈsɝvɚ/	伺服器 /伺服器, 伺服器
cache 名詞	/kæʃ/	快取 /快取, 快取
element 名詞	/ˈɛləmənt/	要素
topic 名詞	/ˈtɑpɪk/	主題 /主題, 話題 /話題
log 名詞	/lɔɡ/	圓木 /圓木
indicate 動詞	/ˈɪndɪˌkeɪt/	表明, 表示
database 名詞	/ˈdeɪtəˌbeɪs/	資料庫 /資料庫
implementation 名詞	/ˌɪmplɪmənˈteɪʃən/	履行
backup 名詞	/ˈbækˌʌp/	備份 /備份
implement 動詞	/ˈɪmpləmənt/	實施 /實施, 執行 /執行
acquire 動詞	/əˈkwaɪɚ/	獲得 /獲得, 取得
dive 動詞	/daɪv/	潛水 /潛水
algorithm 名詞	/ˈælɡəɹɪðm̩/	演算法, 演算法
cluster 名詞	/ˈklʌstɚ/	串, 叢 /叢

影片中出現的短語動詞

單詞	發音	釋義
speed up 動詞	/spiːdˈʌp/	加速, 加快
take over 動詞		接管, 接手

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	is known · is distributed · is stored
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	have been · has been acquired · has already been held

需要注意的發音

“th” 音: algorithm /ˈælɡəɹɪðm̩/, thunder /ˈθʌndɚ/, logarithmic /ˌlɒɡəˈɹɪðmɪk/
“sh” 和 “zh” 音: cache /kæʃ/, implementation /ˌɪmplɪmənˈteɪʃən/, associate /əˈsoʊʃi.ət/, hash /ˈhæʃ/, expiration /ˌɛk.spɚˈeɪ̯.ʃən/
長單詞——注意重音位置: implementation /ˌɪmplɪmənˈteɪʃən/, associate /əˈsoʊʃi.ət/, fundamental /ˌfʌn.dəˈmɛn.təl/, creativity /kri.eɪˈtɪv.ᵻ.ti/, coordinate /koʊˈɔɹ.dəˌneɪt/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: server /ˈsɝvɚ/, dive /daɪv/, solve /sɒlv/, discover /dɪˈskʌvə/, creativity /kri.eɪˈtɪv.ᵻ.ti/
詞尾子音 — 要讀清楚，後面不要加母音: distribute /dɪˈstɹɪbjuːt/, cache /kæʃ/, element /ˈɛləmənt/, topic /ˈtɑpɪk/, log /lɔɡ/
/r/ 和 /l/ 的區別: algorithm /ˈælɡəɹɪðm̩/, practical /ˈpɹæk.tɪ.kəl/, tolerant /ˈtɑːləɹənt/, replicate /ˈɹɛplɪkeɪt/, logarithmic /ˌlɒɡəˈɹɪðmɪk/

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。
用正常速度逐句跟讀，每句重複到你的節奏與說話人一致為止。
錄下自己的聲音並與原聲對比，特別注意 distribute, server, cache 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: Top 5 Redis Use Cases - 通過影片學習英語口語

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