Prática de Shadowing: How Uber Scaled from One Server to 150 Million Reads Per Second - Aprenda a falar inglês com vídeo
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In 2009, Uber was starting with a Python mullet.
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One application, one growing business.
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By 2019, Uber reported more than 15 million trips a day.
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And in 2025, its caching infrastructure reported over 150 million cache database row reads per second at peak.
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So how does a system grow at that scale?
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Let's build it step by step.
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Imagine you are building Uber you start with one application
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and a postgres database and this is called monolith
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and all your main application features live inside one application
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and early days that's not a bad thing it's easier to build test
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and deploy code but now more rides are booing trips more drivers are sending updates
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and your application has work to do if the application and database share one machine they compete for the CPU and RAM.
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The database run queries, the application handle request and both need resources.
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One solution is to move the database onto its own server.
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Now the application and database have separate resources.
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You can scale them independently but there is a catch.
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They now communicate over a network and the database can still become a bottleneck.
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Meanwhile your application is also getting more traffic.
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So let's scale your application first the simplest option is to give your application server more cpu
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and memory and this is called vertical scaling it can work well
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when you are just starting out you don't need to redesign everything just
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because traffic increases but one machine has limits and
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if it fails your application may become unavailable
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so instead of making one server bigger you can add more servers
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and that is horizontal scaling now you have multiple servers handling request
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but there's a new question when the request arrives
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which server should handle it
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so you put a load of answer in front of replication server
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it distributes incoming requests across them it can use strategies like round robin
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or lease connection
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and health checks can help it avoid sending traffic to unhealthy server
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now one server failing does not necessarily take down the whole application
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but there is a other problem imagine a user logs in replication store their session on the first server
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then the user's next request reaches a different server
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that server may not have the session so how does it know
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that you are logged in one option is to store session in a shared system such as redis
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now any application server can retrieve the session
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and other option is to use a token such as gwd
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that carries signed claims that can reduce the need to look up a session on every request.
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But remember, tokens don't eliminate every kind of server -side state.
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The key idea is simple.
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Don't make one application server the only place where information needed by the next request exists.
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We've added application servers, but all of them still depend on database.
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And every request does not necessarily need to read fresh data from it.
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Some data gets requested repeatedly, and that is where caching comes in.
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Imagine a request needs a piece of data.
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First, the application checks the cache.
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If the data is there, it returns it and that is cache hit.
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If it is missing, the application fetches it from database
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and can put it in the cache and that is a cache miss.
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The next request may get the data without querying the database again.
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But caching introduces a problem.
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What happens when the original data changes?
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The cached copy might become stale.
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You can use techniques such as time to live or TTL to expire entries.
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You can also use invalid cache data when the underlying data changes.
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Strategies like write -through or write -around handle writes differently.
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And there is no single strategy that's best for every workload.
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Uber has built caching infrastructure called CacheFront.
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In August 1995, Uber reported that it handled more than 150 million cache row reads per second at peak.
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Notice what that number means.
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It's cache database row reads handled by that infrastructure.
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And it does not mean every database read across
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Uber was subbed from the cache.
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Caching can remove a lot of repeated work, but it does not solve every database problem.
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Some applications read data far more often than updated.
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For those workloads, you can introduce read replicas.
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The primary database handle writes, replica receive copies of the data then application can send suitable read queries to those replicas
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that take some read traffic away from the primary
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but there is an important catch replication can take time the primary might have the latest update
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while the replica is still catching up and this is called replication lack
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so if a request need the fresh data you may need a read from the primary.
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Replica help with re -capacity but they don't automatically solve right bottlenecks or storage limits.
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Now imagine your trip data keeps growing.
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Eventually one database may struggle to store and serve everything efficiently.
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Uber ran into storage challenges with the Postgres -packed trip data in its early growth period.
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It developed a system called SchemaLess on top of MySQL.
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This brings us to another scaling technique.
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It is called Sharding.
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Instead of storing all the data on one database, you split it across multiple database groups.
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For example, imagine dividing trips into groups based on hash of the trip identifier.
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One group stores some trips, another group stores others.
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The application needs a way to work out which group contains the requested trip.
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And that is the basic idea.
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This example explains sharding.
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It is not a claim about Uber's exact partitioning algorithm okay
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so sharding lets you distribute data and workload
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but now your application need to know where each record live
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and the workload might not be evenly distributed imagine one shard receiving far more requests than the others
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that shard can become a bottleneck even when the rest are healthy
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and this is also called a hot shard so sluting the data helps but it also makes data management more complicated.
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Okay, so there is one more challenge.
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As the developer team grow, more developers work on the same application.
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A change in one area might affect other.
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Deployment can become much harder to coordinate.
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One response is to split the application into small services.
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And Uber documented its move toward a microservice architecture in 2015.
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So imagine this.
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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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Vocabulário e dicas de fala para esta lição
Esta aula de conversação usa o vídeo “How Uber Scaled from One Server to 150 Million Reads Per Second”. As palavras que mais se repetem: application, database, server, request, Uber. Este vídeo tem 134 frases e 1507 palavras para praticar shadowing. A fala dura 9:46. O falante fala em ritmo natural, cerca de 154 palavras por minuto, próximo de uma conversa do dia a dia. Apenas 79% das palavras estão entre as 3.000 mais comuns do inglês, por isso o vocabulário é exigente.
Vocabulário principal deste vídeo
15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| database substantivo | /ˈdeɪtəˌbeɪs/ | banco de dados, base de dados |
| server substantivo | /ˈsɝvɚ/ | servidor, server |
| cache substantivo | /kæʃ/ | esconderijo |
| distribute verbo | /dɪˈstɹɪbjuːt/ | distribuir, parcelar |
| replica substantivo | /ˈɹɛplɪkə/ | réplica, cópia exata |
| infrastructure substantivo | /ˈɪnfɹəˌstɹʌkt͡ʃə/ | infraestrutura |
| solve verbo | /sɒlv/ | resolver, solucionar |
| introduce verbo | /ˌɪntɹəˈdus/ | apresentar |
| query substantivo | /ˈkwɪɹ.i/ | pergunta |
| necessarily advérbio | /ˌnɛs.əˈsɛɹ.ə.li/ | necessariamente |
| complicated adjetivo | /ˈkɑm.plɪˌkeɪ.tɪd/ | complicado |
| technique substantivo | /tɛkˈniːk/ | técnica |
| architecture substantivo | /ˈɑː.kɪˌtɛk.t͡ʃə/ | arquitetura |
| log substantivo | /lɔɡ/ | tora |
| domain substantivo | /ˌdəʊˈmeɪ̯n/ | domínio |
Phrasal verbs que você vai ouvir
| Palavra | Pronúncia | Significado |
|---|---|---|
| work out verbo | /ˌwɝk ˈaʊt/ | calcular |
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | is called · are logged · was subbed |
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | We've added · has built |
Pronúncia para ficar de olho
O falante usa 6 contrações e formas reduzidas, como don't, we've. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “th”: algorithm /ˈælɡəɹɪðm̩/, unhealthy /ʌnˈhɛl.θi/, monolith /ˈmɑn.əˌlɪθ/
- Os sons de “sh” e “zh”: cache /kæʃ/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, progression /pɹəˈɡɹɛʃən/, calculation /ˌkælkjuˈleɪʃn̩/
- Palavras longas — acerte a sílaba tônica: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, developer /dɪˈvɛləpɚ/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: distribute /dɪˈstɹɪbjuːt/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, technique /tɛkˈniːk/, log /lɔɡ/, depend /dɪˈpɛnd/
- /l/ final — a língua toca o céu da boca, não vira “u”: unavailable /ˌʌnəˈveɪləbəl/, meanwhile /ˈmiːnwaɪl/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, vertical /ˈvɝ.tɪ.kəl/, horizontal /ˌhoɹəˈzɑn.təl/
- /h/ — um sopro suave, diferente do “r”: horizontal /ˌhoɹəˈzɑn.təl/, unhealthy /ʌnˈhɛl.θi/, hash /ˈhæʃ/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
- Grave a sua voz e compare com o original, prestando atenção a palavras como database, server, cache.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.