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.

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.

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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.

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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.

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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.

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.

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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.

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These 70 domains were not 70 services.

123

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.

126

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,

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.

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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.

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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Vocabulary and speaking notes for this lesson

This speaking lesson is built on the video “How Uber Scaled from One Server to 150 Million Reads Per Second”. The speaker keeps coming back to these words: application, database, server, request, Uber. This video has 134 sentences and 1507 words to shadow. The speech runs for 9:46. The speaker talks at a natural 154 words per minute, close to everyday conversation. Only 79% of the words are among the 3,000 most common in English, so the vocabulary is demanding.

Key vocabulary in this video

15 words from the video worth learning, with pronunciation and meaning:

Word	Pronunciation	Meaning
database noun	/ˈdeɪtəˌbeɪs/	A collection of (usually) organized information in a regular structure, usually but not necessarily in a machine-readable format accessible by a computer.
server noun	/ˈsɝvɚ/	A program that provides services to other programs or devices, either in the same computer or over a computer network.
cache noun	/kæʃ/	Such a store of physical supplies, placed by humans or other animals for practical reasons.
distribute verb	/dɪˈstɹɪbjuːt/	To divide into portions and dispense.
replica noun	/ˈɹɛplɪkə/	An exact copy.
infrastructure noun	/ˈɪnfɹəˌstɹʌkt͡ʃə/	An underlying base or foundation for a building, organization, or system.
solve verb	/sɒlv/	To find an answer or solution to a problem or question; to work out.
introduce verb	/ˌɪntɹəˈdus/	To cause (someone) to be acquainted (with someone else).
query noun	/ˈkwɪɹ.i/	A question, an inquiry (US), an enquiry (UK).
necessarily adverb	/ˌnɛs.əˈsɛɹ.ə.li/	Inevitably; of necessity.
complicated adjective	/ˈkɑm.plɪˌkeɪ.tɪd/	Difficult or convoluted.
technique noun	/tɛkˈniːk/	The practical aspects of a given art, occupation etc.; formal requirements.
architecture noun	/ˈɑː.kɪˌtɛk.t͡ʃə/	The art and science of designing and managing the construction of buildings and other structures, particularly if they are well proportioned and decorated.
log noun	/lɔɡ/	The trunk of a dead tree, cleared of branches.
domain noun	/ˌdəʊˈmeɪ̯n/	A geographic area owned or controlled by a single person or organization.

Phrasal verbs you will hear

Word	Pronunciation	Meaning
catch up verb		To pick up suddenly.
look up verb	/ˌlʊk ˈʌp/	To have better prospects.
read across verb		To draw a conclusion about one phenomenon based on another phenomenon.
start out verb		To emerge suddenly; to jump out.
take down verb		To remove something from a wall or similar vertical surface to which it is fixed.
work out verb	/ˌwɝk ˈaʊt/	To calculate.
work together verb		To be coworkers with someone; to share a workspace with someone.

Grammar in this video

The structures the speaker uses most, with the exact words from the video:

Structure	In the video
Passive voice be + past participle — the focus is on what happens, not who does it	is called · are logged · was subbed
Present perfect have/has + past participle — a past action that still matters now	We've added · has built

Pronunciation to watch

The speaker uses 6 contractions and reduced forms, such as don't, we've. Say them the short way, as you hear them.

The “th” sounds: algorithm /ˈælɡəɹɪðm̩/, unhealthy /ʌnˈhɛl.θi/, monolith /ˈmɑn.əˌlɪθ/
The “sh” and “zh” sounds: cache /kæʃ/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, progression /pɹəˈɡɹɛʃən/, calculation /ˌkælkjuˈleɪʃn̩/
Long words — get the stress right: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, developer /dɪˈvɛləpɚ/

How to practise with this video

Listen to the whole video once without speaking and note the words you do not know.
Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
Record yourself and compare with the original, paying attention to words like database, server, cache.

What is the Shadowing Technique?

Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.

Shadowing technique: read the full step-by-step guide →

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

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Vocabulary and speaking notes for this lesson

Key vocabulary in this video

Phrasal verbs you will hear

Grammar in this video

Pronunciation to watch

How to practise with this video

What is the Shadowing Technique?