シャドーイング練習: How Uber Scaled from One Server to 150 Million Reads Per Second - 動画で英語スピーキングを学ぶ

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In 2009, Uber was starting with a Python mullet.

One application, one growing business.

By 2019, Uber reported more than 15 million trips a day.

And in 2025, its caching infrastructure reported over 150 million cache database row reads per second at peak.

So how does a system grow at that scale?

Let's build it step by step.

Imagine you are building Uber you start with one application

and a postgres database and this is called monolith

and all your main application features live inside one application

and early days that's not a bad thing it's easier to build test

and deploy code but now more rides are booing trips more drivers are sending updates

and your application has work to do if the application and database share one machine they compete for the CPU and RAM.

The database run queries, the application handle request and both need resources.

One solution is to move the database onto its own server.

Now the application and database have separate resources.

You can scale them independently but there is a catch.

They now communicate over a network and the database can still become a bottleneck.

Meanwhile your application is also getting more traffic.

So let's scale your application first the simplest option is to give your application server more cpu

and memory and this is called vertical scaling it can work well

when you are just starting out you don't need to redesign everything just

because traffic increases but one machine has limits and

if it fails your application may become unavailable

so instead of making one server bigger you can add more servers

and that is horizontal scaling now you have multiple servers handling request

but there's a new question when the request arrives

which server should handle it

so you put a load of answer in front of replication server

it distributes incoming requests across them it can use strategies like round robin

or lease connection

and health checks can help it avoid sending traffic to unhealthy server

now one server failing does not necessarily take down the whole application

but there is a other problem imagine a user logs in replication store their session on the first server

then the user's next request reaches a different server

that server may not have the session so how does it know

that you are logged in one option is to store session in a shared system such as redis

now any application server can retrieve the session

and other option is to use a token such as gwd

that carries signed claims that can reduce the need to look up a session on every request.

But remember, tokens don't eliminate every kind of server -side state.

The key idea is simple.

Don't make one application server the only place where information needed by the next request exists.

We've added application servers, but all of them still depend on database.

And every request does not necessarily need to read fresh data from it.

Some data gets requested repeatedly, and that is where caching comes in.

Imagine a request needs a piece of data.

First, the application checks the cache.

If the data is there, it returns it and that is cache hit.

If it is missing, the application fetches it from database

and can put it in the cache and that is a cache miss.

The next request may get the data without querying the database again.

But caching introduces a problem.

What happens when the original data changes?

The cached copy might become stale.

You can use techniques such as time to live or TTL to expire entries.

You can also use invalid cache data when the underlying data changes.

Strategies like write -through or write -around handle writes differently.

And there is no single strategy that's best for every workload.

Uber has built caching infrastructure called CacheFront.

In August 1995, Uber reported that it handled more than 150 million cache row reads per second at peak.

Notice what that number means.

It's cache database row reads handled by that infrastructure.

And it does not mean every database read across

Uber was subbed from the cache.

Caching can remove a lot of repeated work, but it does not solve every database problem.

Some applications read data far more often than updated.

For those workloads, you can introduce read replicas.

The primary database handle writes, replica receive copies of the data then application can send suitable read queries to those replicas

that take some read traffic away from the primary

but there is an important catch replication can take time the primary might have the latest update

while the replica is still catching up and this is called replication lack

so if a request need the fresh data you may need a read from the primary.

Replica help with re -capacity but they don't automatically solve right bottlenecks or storage limits.

Now imagine your trip data keeps growing.

Eventually one database may struggle to store and serve everything efficiently.

Uber ran into storage challenges with the Postgres -packed trip data in its early growth period.

It developed a system called SchemaLess on top of MySQL.

This brings us to another scaling technique.

It is called Sharding.

Instead of storing all the data on one database, you split it across multiple database groups.

For example, imagine dividing trips into groups based on hash of the trip identifier.

One group stores some trips, another group stores others.

The application needs a way to work out which group contains the requested trip.

And that is the basic idea.

This example explains sharding.

It is not a claim about Uber's exact partitioning algorithm okay

so sharding lets you distribute data and workload

but now your application need to know where each record live

and the workload might not be evenly distributed imagine one shard receiving far more requests than the others

that shard can become a bottleneck even when the rest are healthy

and this is also called a hot shard so sluting the data helps but it also makes data management more complicated.

Okay, so there is one more challenge.

As the developer team grow, more developers work on the same application.

A change in one area might affect other.

Deployment can become much harder to coordinate.

One response is to split the application into small services.

And Uber documented its move toward a microservice architecture in 2015.

So imagine this.

Payments, pricing, and trips matching as separate services.

100

These are illustrative examples of how an application can be divided.

101

Each service can have its own code base and deployment process.

102

Teams can work more independently, but now one user request might depend on several services working together.

103

And that creates a different kind of problem.

104

Okay, inside one application, calling a function is usually straightforward.

105

For example, a fare calculation function might take the trip details as input and return the calculated fare.

106

But when one service calls another service over a network, things change.

107

The other service might be slow or it might be unavailable or the network request might time out.

108

Now you need to handle failure across service boundaries.

109

Okay, so when an application has many services, finding the cause of slow request gets hard.

110

Was the delay in pricing, was it payments or was one service waiting on another?

111

So Uber developed Yaeger, a distributed tracing system to help engineer follow requests across services.

112

Tracing helps you to see where time is spent and where request is failing.

113

Without that visibility, debugging a distributed system becomes much harder.

114

Ok, as the services multiply, service discovery and communication also becomes more complicated.

115

A service needs to find the service it wants to call.

116

And the system need to handle those connections reliably.

117

So Uber developed Hyperband as a part of its service communication infrastructure.

118

So engineers also used general resilience patterns such as timeouts, retries and circuit breakers.

119

But those patterns need careful configuration.

120

Retries for example can make an overload service even busier.

121

So by July 2020, Uber had described an architecture with around 2200 critical microservices organized into 70 domains.

122

These 70 domains were not 70 services.

123

They were broader grouping used to organize services.

124

And that is important lesson here.

125

Microservices can be more independent but also create more dependence, more network calls, more operational complexity.

126

So just look at the progression.

127

You start with one application, then you separate the database, you add application servers, you share session state, you introduce caching and read applicas,

128

you shard the data, and eventually you split the application into services.

129

Each decision solves a problem, but each decision also introduces a new trade -off.

130

That's how production systems grow.

131

Not by adding every technology from the beginning, but by identifying the bottleneck, choosing a solution, and understanding the new problem that solution create.

132

When you are designing a system, don't just ask, what technology should I use?

133

Ask, what problem am I solving and what trade -off am I accepting?

134

Save this for your next systemline interview and follow for more.

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このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「How Uber Scaled from One Server to 150 Million Reads Per Second」を教材にしています。繰り返し出てくる語は次のとおりです：application, database, server, request, Uber。この動画には、シャドーイング用の文が134文、単語が1507語あります。音声の長さは9:46です。話す速さは1分あたり約154語で、日常会話に近い自然なペースです。英語の頻出3,000語に含まれる単語は79%だけなので、語彙は難しめです。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
database 名詞	/ˈdeɪtəˌbeɪs/	データベース
server 名詞	/ˈsɝvɚ/	サーバー
cache 名詞	/kæʃ/	キャッシュ
distribute 動詞	/dɪˈstɹɪbjuːt/	分ける
replica 名詞	/ˈɹɛplɪkə/	複製, 写し
infrastructure 名詞	/ˈɪnfɹəˌstɹʌkt͡ʃə/	インフラストラクチャ, インフラ
solve 動詞	/sɒlv/	解決する
introduce 動詞	/ˌɪntɹəˈdus/	紹介する, 引き合わせる
query 名詞	/ˈkwɪɹ.i/	質問
complicated 形容詞	/ˈkɑm.plɪˌkeɪ.tɪd/	複雑な
technique 名詞	/tɛkˈniːk/	技術
architecture 名詞	/ˈɑː.kɪˌtɛk.t͡ʃə/	建築, 建築学
log 名詞	/lɔɡ/	丸太, 丸木
depend 動詞	/dɪˈpɛnd/	依存する
trace 名詞	/tɹeɪs/	跡, 痕跡

動画に出てくる句動詞

単語	発音	意味
look up 動詞	/ˌlʊk ˈʌp/	見上げる

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	is called · are logged · was subbed
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	We've added · has built

注意したい発音

話し手はdon't, we'veなど、短縮形や弱形を6回使っています。聞こえたとおりの短い形で発音しましょう。

「th」の音: algorithm /ˈælɡəɹɪðm̩/, unhealthy /ʌnˈhɛl.θi/, monolith /ˈmɑn.əˌlɪθ/
「sh」と「zh」の音: cache /kæʃ/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, progression /pɹəˈɡɹɛʃən/, calculation /ˌkælkjuˈleɪʃn̩/
長い単語（アクセントの位置に注意）: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, developer /dɪˈvɛləpɚ/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: replica /ˈɹɛplɪkə/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, differently /ˈdɪf.ə.ɹənt.li/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, repeatedly /ɹɪˈpiːtɪdli/
/v/ — /b/ にならないように、上の歯を下唇に当てる: server /ˈsɝvɚ/, solve /sɒlv/, developer /dɪˈvɛləpɚ/, divide /dɪˈvaɪd/, unavailable /ˌʌnəˈveɪləbəl/
/f/ — 「フ」ではなく、上の歯と下唇で出す: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, fare /ˈfɛə/, differently /ˈdɪf.ə.ɹənt.li/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, straightforward /ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。database, server, cacheなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: How Uber Scaled from One Server to 150 Million Reads Per Second - 動画で英語スピーキングを学ぶ

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