Pratique du Shadowing: Kafka Tutorial for Beginners | Everything you need to get started - Apprendre l'anglais à l'oral avec la vidéo
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If you've been hearing about Kafka, but you don't understand what it is and why all the hype,
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let me clarify it using real-life examples that will make everything finally click for you.
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Imagine we are building an e-commerce application called StreamStore, and we have some microservices handling payments, orders, inventory, and so on.
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And when something happens in our application, like customer, places, and order, it's like dominoes where a chain reaction of updates
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and events by other services get triggered like stock needs to be updated in the database now
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that we sold some of it and notification
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or confirmation email needs to be sent to the customer an
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invoice needs to be generated with the right sales tax
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and sent per email to the customer maybe revenue
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and sales data needs to be updated on our sales dashboard and so on.
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Now we are a small startup
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so we are starting with the simplest straightforward microservices architecture where
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the microservices just call each other like the order service would
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say hey all you guys we just closed an order go update your stuff accordingly
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and it all worked great at first but suddenly we become a hit and people are loving our store
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or we just announced Black Friday sales and our store is getting hundreds of thousands of customers,
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which is amazing, but suddenly our application starts crashing,
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everything is slowing down, users are sitting in front of loading screens because our architecture cannot handle this load.
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We get in panic because we are losing sales every minute.
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Our architecture that looked pretty clean and straightforward on the whiteboard becomes a nightmare.
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So here is what's happening in the background.
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First of all, we have what's called tight coupling between the services, which means when the payment service goes down,
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for example, because some API in the background isn't responsive or the service itself just crashes under load.
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And when that happens, our entire order process freezes.
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We have synchronous communication.
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So each order feels like a game of dominoes.
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One slow service and everything backs up.
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And as I said, during peak times, customers are literally staring at loading screens.
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And we also have lots of single points of failure,
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which means a 10 minute inventory service outage meant two hours of order backlogs and countless lost sales.
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And we're also losing a lot of analytics data.
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When the analytics service goes down for an hour, we're losing important black friday sales data after another hectic
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and chaotic week we thought what if we redesign the system so
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that the orders flow through the system like items on a
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conveyor belt instead of our current game of hot potato and instead of apps calling each other directly and waiting for reply,
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we remove that type coupling.
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We basically make space between them and introduce a tool that sits in the middle and acts as a broker.
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Think of it as post office.
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When you order something online, the sellers don't come knocking on your door to deliver package themselves.
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They hand it over to the post office
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or some middleman to deliver your package or
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if you are returning your purchase
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or sending a package to someone you don't fly to their
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place to give them the package in person post office has this infrastructure
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and handles the processing so kafka is like the mail delivery service
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or post office which sits in the middle
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so now the order service goes to kafka and hands over a package called event that says hey order
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was made for this customer for these products
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and here are all the details please make this information available for anyone who needs it to update
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and do stuff in the background bye and it just basically goes back
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and continues its work
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and an event looks like this with very simple structure with key value pair
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and metadata information so the order service does not need need to wait there to make sure
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that the others actually got the information.
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It can trust this broker that it will be delivered to the right services.
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And all this will happen in the background.
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Like in the post office, you just drop off your package and go home.
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You don't wait there sitting and checking whether they actually ship the package
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or not because you know that will take care of the rest.
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And the order service that gives that information to Kafka
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or basically a service that produces this event and hands it over to Kafka is called a producer because it produces events.
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And in code, this is how it would look like using Kafka producer API.
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So in JavaScript or Java code, you basically use that API to create an event and give it to Kafka.
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Now, where does this information or these events get saved when producers of those events give them to Kafka?
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Because we have a bunch of other services like inventory, payment, and so on that also produce certain events
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and hand it over to Kafka with all the information
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that other services may need when inventory gets updated or the payment service says that the payment just failed and so on.
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So do all these events from different producers get dumped into a giant bucket in Kafka or are they organized somehow?
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If we had one big bucket handling all the writes and reads it will not be very performant, right?
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It's like having one single queue in the post office.
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So if whether sending a letter or package or picking up your delivery, everyone would be standing in the same queue.
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Instead, imagine that post office will add sections with their own queues, like a section for letters,
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another one for large packages, and so on.
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So Kafka has what's called topics to group the same type of events.
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So for example, order service will write events to orders topic.
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The payment service may update payments topic and so on.
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Now, how do those topics get created or who defines them?
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Well, just like you define a SQL schema for your database based on what your application needs and what objects you have,
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you as an engineer decide how to group these events in Kafka in what topics.
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So now that the order service added an event to the order's topic, what happens next?
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That event may trigger other actions like updating stock in the database
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because we just sold something or sending notification to customer or updating invoice and sales status.
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Plus what other topics may exist
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that would need an event data entry as a result of an order which will in turn trigger other actions.
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So how does all that get handled?
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Well, on the other side of events, we have consumers, basically microservices who are subscribed to these different topics.
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And whenever a new event happens and gets added to this topic, all consumers who are subscribed get notified by Kafka,
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and they then do their stuff.
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In this case, we have three microservices that subscribe to the order event.
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Notification service will see that a new order event was added, which means an order was placed in our application.
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And based on the payload of that event, it will send a confirmation email to the customer and maybe a purchase notification to your email.
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Then an inventory service may update the database by updating the stocks of every product that was sold in that order.
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And maybe in addition to the database update, we'll generate a new event and write it into an inventory topic.
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And then finally, the payment service may generate invoice and send it to the user.
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Now, I hope you're learning a lot and the topic of Kafka is becoming clear for you.
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It takes us on average two or three weeks to produce one such video.
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So if you find it valuable, we would appreciate if you left your feedback or liked the video.
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And we'd be happy to have you as our subscriber as well.
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Now, you may be asking, is Kafka a replacement of a database somehow,
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since we are saving all this data as events and basically updating the status of things?
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So is it kind of a new way of saving things?
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A simple answer is no, it's not a replacement database.
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Let's explain by following our story.
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So when the inventory service updates the stock for each product in the database,
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why does it produce an event and write it to the inventory topic?
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What kind of event that may be and why would we have it in addition to the data in the database?
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Well, that's another use case of Kafka where one event basically creates this chain reaction of events
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when multiple things need to happen as a result of one event happening, which we saw.
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An example, you may have another service that is subscribed to the inventory topic
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and calculates whether any of the products just gone below the inventory threshold and produce a low inventory alert,
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which maybe as a chain reaction will trigger another service
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that may trigger an inventory restock service that will order more inventory of that specific product.
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Another very important use case of Kafka is real-time analytics.
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For example, again, when sales happen in your application, you may have a sales dashboard where your service is updating real-time sales numbers.
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Another such use case is driver location updates in an application like Uber, where the driver location changes get sent constantly to the application,
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which then updates the UI of the user to display those changes.
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And for all these use cases, Kafka actually uses what's called stream APIs.
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So on one side, you have these regular consumers that will process one event at a time,
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for example, a notification service that will read an order event and based on that, will send an email or notification to the customer.
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Streams on the other hand will process continuous flow of data with aggregations
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and joins and so on in order to do real-time processing and analytics on that.
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So for example low inventory validations to check constantly with every event
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and do the calculation to see whether inventory just dropped below the threshold or get the location changes from the drivers.
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So these analytic services will stream the events continuously doing various analytics and calculations on them.
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And in code, you would have a streams API
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that will read the orders and do all these kinds of calculations on them.
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Now, as I mentioned, these are streams of constant data saved as events in Kafka,
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because if you have an application like Uber with millions of users
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and tens of thousands of drivers with their locations getting updated constantly, that's a lot of data and events that are being produced, right?
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And all consumers need to read from it.
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So millions of writes and reads in different Kafka topics, which can, of course, affect performance.
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So we need to scale.
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And that's where Kafka's partition concept comes in, which is kind of a core of Kafka's ability to scale and become really performant.
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So partitions are basically what make processing large amounts of data easy to handle and process without compromising the performance.
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So how does it work exactly?
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With our post office example, remember, we edit sections for letters, large packages, small packages, and so on.
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Partitions are like adding more workers per section to help out.
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So suddenly before Christmas, the letters section get overloaded because everyone's sending letters to Santa.
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Well, sadly, that doesn't happen.
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But if it did, we would add more workers in that section, but not just randomly.
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Instead, you say Anna processes letters going to Europe, Steve handles letters to US,
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Jay handles ones to Asia, and so on.
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Same way in Kafka, in the orders topic, you may create EU orders partition, US orders, Asia orders and so on.
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And again, you would decide how to partition your topic as part of your schema design.
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Now, let's think about the consumer side.
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Let's say suddenly millions of orders are coming in and we said we can scale this with partitions.
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So producers can write into multiple partitions at the same time.
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But what about the consumers?
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How can they consume so much data at once?
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Because even if you have partitions, you'll have one consumer, let's say,
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inventory service, trying to process all the events that it's subscribed to,
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which is like all the parcels going to one person recipient
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like thousands of letters going to santa those post office workers are being super quick
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and are delivering them to the recipient but he's getting buried under the pile
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but we need some people helping him sort through this
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and that's where consumer groups come in so
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when you start additional instances of
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that microservice like replicas in kubernetes they can all consume from kafka partitions
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and process events faster in parallel now how does kafka know
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which consumers form a group and how to divide and
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which ones belong together simple they are grouped by the group id attribute
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when they register as consumers with kafka
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so replicas of the same application will have the same group IDs and will automatically be grouped together.
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And when you start replicas, Kafka distributes the load automatically by assigning partitions to consumers.
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So Kafka says, oh, we have a new helper.
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Now you can process this pile of letters here.
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And when that helper stops working, it will take the pile and give it to another active one.
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Now the final question is, where is this data physically saved?
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Data in topics is saved on Kafka servers called brokers.
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And you can think of each broker like a post office branch that stores the actual messages on disk,
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handles requests from producers and consumers, and replicates the data for fault tolerance.
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Even if something happens with the disk, the data is stored somewhere else as a backup.
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And this is actually what makes Kafka different from standard message brokers.
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So while regular message queues would delete messages after consumption, so as soon as consumers see that message and do something with it,
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that message is gone.
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Kafka, however, persists every event or message as long as you need.
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And you can configure how long you want to store them with a retention policy.
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So think of it like our post office keeping a log of all package deliveries, but not just for record keeping,
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but for analyzing patterns and improving service.
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So that unique feature of Kafka for real-time data processing
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and general analytics means that Kafka needs to store those events long-term so the consumers can read those events anytime they want,
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even multiple times if they need to.
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And as I said, this capability to process streams of data in real time
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while keeping the original data for later analysis is what really differentiates Kafka from simple message brokers.
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So that's the main difference.
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And for even clearer comparison, think of this as difference between watching Netflix and watching TV.
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Netflix is on demand.
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So consumers or people who are viewers can decide themselves what they want to watch, when they want to watch it, and at what pace.
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So they can stop and pause anytime and continue whenever they want.
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Or they can replay or start from the beginning. With TV, you have predefined programs
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and people who want to view those programs need to tune in at specific take time to watch specific stuff.
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So everyone watches the same thing at the same time at the same pace.
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You can't pause and continue later.
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If you miss a movie or show, you just miss it.
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And it's not automatically saved to watch later.
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And that's exactly the difference between Kafka architecture versus other traditional message brokers.
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And finally, Kafka needs a way to keep track of which brokers are alive, elect leaders to coordinate, manage all the configuration.
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And traditionally Kafka used an external tool called Zookeeper for this type of coordination.
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So it was like a central management for all the Kafka brokers.
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However, important to note that the newer versions of Kafka from version 3.0 introduced K-raft or Kafka raft,
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which removes the need for Zookeeper as this external dependency with centralized control by building that coordination directly into Kafka.
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Now, I hope I made Kafka finally clear for you.
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Share it with one colleague who you think will benefit from it.
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And with that, thanks for watching and see you in the next video.
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Vocabulaire et conseils d’expression pour cette leçon
Cette vidéo contient 204 phrases et 2795 mots à répéter en shadowing. La partie parlée dure 18:32. Le locuteur parle à un rythme naturel d’environ 151 mots par minute, proche d’une conversation courante. 84 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.
Vocabulaire clé de cette vidéo
15 mots de la vidéo à retenir, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| topic nom | /ˈtɑpɪk/ | sujet, thème |
| inventory nom | /ˈɪn.vən.tɹi/ | inventaire |
| package nom | /ˈpæk.ɪd͡ʒ/ | paquet, paquetage |
| database nom | /ˈdeɪtəˌbeɪs/ | base de données, banque de données |
| broker nom | /ˈbɹoʊkɚ/ | courtier, représentant |
| producer nom | /pɹəˈdjuːsə/ | producteur, productrice |
| notification nom | /ˌnoʊtɪfɪˈkeɪʃn̩/ | notification |
| analytics nom | /ˌæn.əˈlɪt.ɪks/ | analytique |
| trigger nom | /ˈtɹɪɡɚ/ | détente, gâchette |
| subscribe verbe | /səbˈskɹaɪb/ | abonner, s'abonner |
| architecture nom | /ˈɑː.kɪˌtɛk.t͡ʃə/ | architecture |
| queue nom | /kju/ | file, queue |
| generate verbe | /ˈd͡ʒɛn.ə.ɹeɪt/ | générer |
| pile nom | /paɪl/ | monceau, tas |
| calculation nom | /ˌkælkjuˈleɪʃn̩/ | calcul |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| back up verbe | reculer |
| drop off verbe | s’endormir, tomber |
| go back verbe | rentrer, retourner |
| go down verbe | descendre |
| hand over verbe | remettre, donner |
| help out verbe | dépanner |
| pick up verbe | soulever |
| slow down verbe | ralentir, décélérer |
Prononciation à surveiller
Le locuteur utilise 14 contractions et formes réduites, comme don't, we're, can't. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: partition /pɑɹˈtɪ.ʃən/, notification /ˌnoʊtɪfɪˈkeɪʃn̩/, calculation /ˌkælkjuˈleɪʃn̩/, confirmation /ˌkɑn.fəɹˈmeɪ.ʃən/, threshold /ˈθɹɛʃ(h)oʊld/
- Mots longs — placez bien l’accent: notification /ˌnoʊtɪfɪˈkeɪʃn̩/, analytics /ˌæn.əˈlɪt.ɪks/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, confirmation /ˌkɑn.fəɹˈmeɪ.ʃən/, recipient /ɹəˈsɪp.i.ənt/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme topic, inventory, package.
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Phrases conditionnelles if + proposition, will/would + verbe — une condition et son résultat | if you find it valuable, we would appreciate · if it did, we would add · if you have partitions, you'll have |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | be updated · be sent · be generated |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | services, which means · anyone who needs · order which will |
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.