跟读练习: Top 5 Redis Use Cases - 通过视频学习英语口语
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In this video, we discover the versatility of Redis.
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We'll talk about the top use cases that have been better tested in production at various companies and at various scales.
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Get insights into how Redis solves interesting scalability challenges for us
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and learn why it is a great tool to know well in our system design toolset.
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Let's dive right into it.
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First of all, what is Redis and why do people use it?
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Redis is an in -memory data structure store.
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It is most commonly used as a cache.
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It supports many data structures such as strings, hashes, lists, sets, and sorted sets.
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Redis is known for its speed.
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We made a video to explain why Redis is so fast.
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Look for the link in the description if you would like to watch that video.
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Now let's dive into the top use cases of Redis.
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The number one use case of Redis is caching objects to speed up web applications.
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In this use case, Redis stores frequently requested data in memory.
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It allows the web servers to return frequently accessed data quickly.
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This reduces the load on the database and improves the response time for the application.
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At scale, the cache is distributed among a cluster of Redis servers.
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Sharding is a common technique to distribute and caching load evenly across the cluster.
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Other topics to consider when deploying Redis as a distributed cache include setting a correct TTL
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and handling thundering hurt on cold start.
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Another common use case is to use Redis as a session store to share session data among stateless servers.
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When a user logs into a web application, the session data is stored in Redis,
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along with a unique session ID that is returned to the client as a cookie.
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When a user makes a request to the application, the session ID is included in the request,
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and the stateless web server retrieves the session data from Redis using the ID.
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It is important to note that Redis is an in -memory database.
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The session data stored in Redis will be lost if the Redis server restarts.
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Even though Redis provides persistent options like a snapshot and AOF or append -only file,
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they allow session data to be saved to disk and reloaded into memory in the event of a restart.
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These options often take too long to load to be practical.
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In production, replication is usually used instead.
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In this case, data is replicated to a backup instance.
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In the event of a crash of the main instance, the backup instance is quickly promoted to take over the traffic.
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The next use case is distributed log.
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Distributed logs are used when multiple nodes in an application need to coordinate access to some shared resource.
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Redis is used as a distributed log with its atomic commands like set -nx or set -if -not -exists.
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It allows a caller to set a key only if it does not already exist.
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Here's how it works at a high level.
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Client 1 tries to acquire the log by setting a key with a unique value
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and a timeout using the set -nx command.
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If the key was not already set, the set -nx command returns 1, indicating that the log has been acquired by client 1.
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Client 1 finishes its work and releases the log by deleting the key.
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Now if the key was already set, the set -annex command returns 0, indicating that the log has already been held by another client.
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In this case, client 1 waits and retries the set -annex operation until the log is released by the other client.
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Note that this simple implementation might be good enough for many use cases, but it is not completely fault -tolerant.
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For production use, there are many Redis client libraries that provide high -quality distributed log implementation built right off the box.
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Next up is Ray Limiter.
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Redis can be used as a Ray Limiter by using its increment command on some counters
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and setting expiration times on those counters.
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A very basic
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Ray Limiting algorithm works this way.
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For each incoming request, the request IP or user ID is used as a key.
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The number of requests for the key is incremented using the increment command in Redis.
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The current count is compared to the allowed rate limit.
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If the count is within the rate limit, the request is processed.
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If the count is over the limit, the request is rejected.
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The keys are set to expire after a specific time window, for example, a minute, to reset the cans for the next time window.
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More sophisticated rate limiters, like the leaky bucket algorithm, can also be implemented using Redis.
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The last use case we would like to talk about is gaming leaderboard.
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For most games that are not super high scale, Redis is a delightful way to implement various types of gaming leaderboards.
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Sort of sets are the fundamental data structure that enables this.
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A sort of set is a collection of unique elements, each with a score associated with it.
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The elements are sorted by score.
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This allows for quick retrieval of the elements by score in logarithmic time.
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That's it for our top Redis use cases.
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Redis is very versatile.
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There are many different ways to use it.
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Some features are more better tested than others.
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Our creativity is the limit.
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Use it with care and have fun with it.
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You would like our videos.
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You may like our weekly system design newsletter as well.
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It covers topics and trends in large -scale system design, trusted by 200 ,000 readers.
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本课的词汇与口语要点
这节口语课以视频“Top 5 Redis Use Cases”为素材。 视频中反复出现的词有：Redis, log, client, session, key。 这段视频共有 75 个句子、937 个单词可供跟读。 讲话部分时长为 6:16。 说话人语速平稳，每分钟约 149 个词，很适合跟读。 只有 77% 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 |
|---|---|---|
| distribute 动词 | /dɪˈstɹɪbjuːt/ | 分配 |
| server 名词 | /ˈsɝvɚ/ | 服務器 /服务器, 伺服器 |
| cache 名词 | /kæʃ/ | 緩存 /缓存, 快取 |
| element 名词 | /ˈɛləmənt/ | 要素 |
| topic 名词 | /ˈtɑpɪk/ | 主題 /主题, 話題 /话题 |
| log 名词 | /lɔɡ/ | 圓木 /圆木 |
| indicate 动词 | /ˈɪndɪˌkeɪt/ | 表明, 表示 |
| database 名词 | /ˈdeɪtəˌbeɪs/ | 數據庫 /数据库 |
| implementation 名词 | /ˌɪmplɪmənˈteɪʃən/ | 履行 |
| backup 名词 | /ˈbækˌʌp/ | 備份 /备份 |
| implement 动词 | /ˈɪmpləmənt/ | 實施 /实施, 執行 /执行 |
| acquire 动词 | /əˈkwaɪɚ/ | 獲得 /获得, 取得 |
| dive 动词 | /daɪv/ | 潛水 /潜水 |
| algorithm 名词 | /ˈælɡəɹɪðm̩/ | 算法, 演算法 |
| cluster 名词 | /ˈklʌstɚ/ | 串, 叢 /丛 |
视频中出现的短语动词
| 单词 | 发音 | 释义 |
|---|---|---|
| speed up 动词 | /spiːdˈʌp/ | 加速, 加快 |
| take over 动词 | 接管, 接手 |
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 known · is distributed · is stored |
| 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 | have been · has been acquired · has already been held |
需要注意的发音
- “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/
- 长单词——注意重音位置: implementation /ˌɪmplɪmənˈteɪʃən/, associate /əˈsoʊʃi.ət/, fundamental /ˌfʌn.dəˈmɛn.təl/, creativity /kri.eɪˈtɪv.ᵻ.ti/, coordinate /koʊˈɔɹ.dəˌneɪt/
中文母语者容易读错的音：
- /v/ — 不要读成 /w/，上齿轻触下唇: server /ˈsɝvɚ/, dive /daɪv/, solve /sɒlv/, discover /dɪˈskʌvə/, creativity /kri.eɪˈtɪv.ᵻ.ti/
- 词尾辅音 — 要读清楚，后面不要加元音: distribute /dɪˈstɹɪbjuːt/, cache /kæʃ/, element /ˈɛləmənt/, topic /ˈtɑpɪk/, log /lɔɡ/
- /r/ 和 /l/ 的区别: algorithm /ˈælɡəɹɪðm̩/, practical /ˈpɹæk.tɪ.kəl/, tolerant /ˈtɑːləɹənt/, replicate /ˈɹɛplɪkeɪt/, logarithmic /ˌlɒɡəˈɹɪðmɪk/
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。
- 用正常速度逐句跟读，每句重复到你的节奏与说话人一致为止。
- 录下自己的声音并与原声对比，特别注意 distribute, server, cache 这类单词。
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。