Shadowing Practice: How Uber Scaled from One Server to 150 Million Reads Per Second - Learn English Speaking with Video

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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.

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

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

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

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And that creates a different kind of problem.

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

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

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

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

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Now you need to handle failure across service boundaries.

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

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

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

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

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

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

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

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And the system need to handle those connections reliably.

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

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

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But those patterns need careful configuration.

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Retries for example can make an overload service even busier.

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

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These 70 domains were not 70 services.

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They were broader grouping used to organize services.

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And that is important lesson here.

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

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So just look at the progression.

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

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

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

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That's how production systems grow.

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

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

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

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

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AI TelaffuzHer cümleyi puanla

IPA AlıştırmasıHer sesi ustalaştır

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Bu dersin kelimeleri ve konuşma notları

Bu konuşma dersi “How Uber Scaled from One Server to 150 Million Reads Per Second” videosuna dayanıyor. En çok tekrarlanan kelimeler: application, database, server, request, Uber. Bu videoda gölgeleme çalışması için 134 cümle ve 1507 kelime var. Konuşma bölümü 9:46 sürüyor. Konuşmacı dakikada yaklaşık 154 kelimeyle, günlük konuşmaya yakın doğal bir hızda konuşuyor. Kelimelerin yalnızca %79’i İngilizcede en sık kullanılan 3.000 kelime arasında, bu yüzden kelime dağarcığı zorlayıcı.

Bu videodaki önemli kelimeler

Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:

Kelime	Telaffuz	Anlam
database isim	/ˈdeɪtəˌbeɪs/	veritabanı, veri tabanı
server isim	/ˈsɝvɚ/	sunucu, server
cache isim	/kæʃ/	önbellek
distribute fiil	/dɪˈstɹɪbjuːt/	dağıtmak, paylaştırmak
replica isim	/ˈɹɛplɪkə/	kopya, replika
infrastructure isim	/ˈɪnfɹəˌstɹʌkt͡ʃə/	altyapı, enfrastrüktür
solve fiil	/sɒlv/	çözmek, halletmek
introduce fiil	/ˌɪntɹəˈdus/	tanıştırmak
query isim	/ˈkwɪɹ.i/	sorgulama
complicated sıfat	/ˈkɑm.plɪˌkeɪ.tɪd/	karışık, komplike
technique isim	/tɛkˈniːk/	teknik
architecture isim	/ˈɑː.kɪˌtɛk.t͡ʃə/	mimarlık
log isim	/lɔɡ/	kütük
developer isim	/dɪˈvɛləpɚ/	geliştirici
trace isim	/tɹeɪs/	bulmak, iz sürmek

Duyacağınız deyimsel fiiller

Kelime	Telaffuz	Anlam
catch up fiil		yakalamak
look up fiil	/ˌlʊk ˈʌp/	yukarı bakmak

Bu videodaki dil bilgisi

Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:

Yapı	Videoda
Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli	is called · are logged · was subbed
Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem	We've added · has built

Dikkat edilecek telaffuzlar

Konuşmacı don't, we've gibi kısaltılmış biçimleri 6 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.

“th” sesleri: algorithm /ˈælɡəɹɪðm̩/, unhealthy /ʌnˈhɛl.θi/, monolith /ˈmɑn.əˌlɪθ/
“sh” ve “zh” sesleri: cache /kæʃ/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, progression /pɹəˈɡɹɛʃən/, calculation /ˌkælkjuˈleɪʃn̩/
Uzun kelimeler — vurguyu doğru yere koyun: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, developer /dɪˈvɛləpɚ/

Türkçe konuşanların zorlandığı sesler:

/w/ — dudaklar yuvarlak, /v/ değil: query /ˈkwɪɹ.i/, meanwhile /ˈmiːnwaɪl/, straightforward /ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/
Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: query /ˈkwɪɹ.i/, trace /tɹeɪs/, suitable [ˈsjuː.tə.bɫ̩], progression /pɹəˈɡɹɛʃən/, straightforward /ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/
/æ/ — “e”den daha açık: cache /kæʃ/, algorithm /ˈælɡəɹɪðm̩/, calculate /ˈkælkjʊleɪt/, calculation /ˌkælkjuˈleɪʃn̩/, hash /ˈhæʃ/

Bu videoyla nasıl çalışılır

Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
Kendinizi kaydedin ve orijinaliyle karşılaştırın; database, server, cache gibi kelimelere özellikle dikkat edin.

Gölgeleme Tekniği Nedir?

Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.

Shadowing tekniği: adım adım eksiksiz rehberi okuyun →

Shadowing Practice: How Uber Scaled from One Server to 150 Million Reads Per Second - Learn English Speaking with Video

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