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.
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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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אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “How Uber Scaled from One Server to 150 Million Reads Per Second”. המילים שחוזרות הכי הרבה: application, database, server, request, Uber. בסרטון הזה יש 134 משפטים ו-1507 מילים לתרגול Shadowing. הדיבור נמשך 9:46. הדובר מדבר בקצב טבעי של כ-154 מילים בדקה, קרוב לשיחה יומיומית. רק 79% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית, כך שאוצר המילים מאתגר.
אוצר מילים מרכזי בסרטון הזה
15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:
| מילה | הגייה | משמעות |
|---|---|---|
| cache שם עצם | /kæʃ/ | מִצְבּוֹר, מַחְבּוֹא |
| shard שם עצם | /ˈʃɑːd/ | רְסִיס |
| distribute פועל | /dɪˈstɹɪbjuːt/ | חילק |
| token שם עצם | /ˈtoʊkən/ | אֲסִימוֹן, זִ׳יטוֹן |
| fare שם עצם | /ˈfɛə/ | דמי נסיעה, תעריף |
| organize פועל | /ˈɔɹɡənaɪz/ | ארגן |
| monolith שם עצם | /ˈmɑn.əˌlɪθ/ | מונולית |
| mullet שם עצם | /ˈmʌlɪt/ | בורי |
| hash שם עצם | /ˈhæʃ/ | סוּלָּמִית |
| fetch פועל | /fɛt͡ʃ/ | הביא, השיג |
| dependence שם עצם | /dɪˈpɛndəns/ | תְּלוּת |
| calculation שם עצם | /ˌkælkjuˈleɪʃn̩/ | חִשׁוּב \ חישוב |
| calculate פועל | /ˈkælkjʊleɪt/ | חישב \ חִשֵּׁב |
| horizontal שם תואר | /ˌhoɹəˈzɑn.təl/ | אפקי |
| boo | /buː/ | בּוּז |
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | is called · are logged · was subbed |
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | We've added · has built |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-6 קיצורים וצורות מקוצרות, כמו don't, we've. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “th”: monolith /ˈmɑn.əˌlɪθ/, unhealthy /ʌnˈhɛl.θi/, algorithm /ˈælɡəɹɪðm̩/
- הצלילים של “sh” ו-“zh”: cache /kæʃ/, shard /ˈʃɑːd/, replication /ɹɛplɪˈkeɪʃən/, hash /ˈhæʃ/, partition /pɑɹˈtɪ.ʃən/
- מילים ארוכות — שימו לב להטעמה: replication /ɹɛplɪˈkeɪʃən/, unavailable /ˌʌnəˈveɪləbəl/, independently /ˌɪndɪˈpɛndəntli/, illustrative /ɪˈlʌs.tɹə.tɪv/, resilience /ɹɪˈzɪl.ɪ.əns/
צלילים שדוברי עברית מתקשים בהם:
- /w/ — שפתיים מעוגלות, לא /v/: query /ˈkwɪɹ.i/, straightforward /ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: cache /kæʃ/, hash /ˈhæʃ/, calculation /ˌkælkjuˈleɪʃn̩/, calculate /ˈkælkjʊleɪt/, algorithm /ˈælɡəɹɪðm̩/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: replica /ˈɹɛplɪkə/, distribute /dɪˈstɹɪbjuːt/, replication /ɹɛplɪˈkeɪʃən/, query /ˈkwɪɹ.i/, retry /riˈtɹaɪ/
איך לתרגל עם הסרטון הזה
- הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
- התחילו במהירות 0.75×, תרגלו Shadowing משפט אחר משפט, וחזרו למהירות רגילה כשזה מרגיש קל.
- הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו cache, shard, distribute.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.