Shadowing-Übung: Kafka Tutorial for Beginners | Everything you need to get started - Englisch Sprechen Lernen mit Video
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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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Wortschatz und Sprechhinweise zu dieser Lektion
Dieses Video enthält 204 Sätze und 2795 Wörter zum Nachsprechen. Der gesprochene Teil dauert 18:32. Der Sprecher spricht in natürlichem Tempo, etwa 151 Wörter pro Minute, ähnlich wie im Alltagsgespräch. 84 % der Wörter gehören zu den 3.000 häufigsten im Englischen; den Rest solltest du dir vor dem Üben ansehen.
Wichtiger Wortschatz in diesem Video
15 lernenswerte Wörter aus dem Video, mit Aussprache und Bedeutung:
| Wort | Aussprache | Bedeutung |
|---|---|---|
| topic Substantiv | /ˈtɑpɪk/ | Thema |
| inventory Substantiv | /ˈɪn.vən.tɹi/ | Inventar, Lager |
| package Substantiv | /ˈpæk.ɪd͡ʒ/ | Paket, Packung |
| partition Substantiv | /pɑɹˈtɪ.ʃən/ | Teilung |
| database Substantiv | /ˈdeɪtəˌbeɪs/ | Datenbank |
| broker Substantiv | /ˈbɹoʊkɚ/ | Vermittler, Makler |
| producer Substantiv | /pɹəˈdjuːsə/ | Hersteller, Herstellerin |
| notification Substantiv | /ˌnoʊtɪfɪˈkeɪʃn̩/ | Benachrichtigung |
| analytics Substantiv | /ˌæn.əˈlɪt.ɪks/ | Analytik |
| trigger Substantiv | /ˈtɹɪɡɚ/ | Abzug |
| subscribe Verb | /səbˈskɹaɪb/ | abonnieren |
| architecture Substantiv | /ˈɑː.kɪˌtɛk.t͡ʃə/ | Architektur, Baustil |
| queue Substantiv | /kju/ | Schlange, Warteschlange |
| generate Verb | /ˈd͡ʒɛn.ə.ɹeɪt/ | generieren |
| pile Substantiv | /paɪl/ | Haufen, Stoß |
Phrasal Verbs, die du hören wirst
| Wort | Bedeutung |
|---|---|
| drop off Verb | wegtreten, wegdämmern |
| go back Verb | zurückgehen, zurückfahren |
| go down Verb | absteigen, hinabsteigen |
| hand over Verb | übergeben, überreichen |
| help out Verb | aushelfen |
| pick up Verb | aufheben, aufnehmen |
| slow down Verb | verlangsamen |
Aussprache, auf die du achten solltest
Der Sprecher verwendet 14 Kurzformen und abgeschwächte Formen, zum Beispiel don't, we're, can't. Sprich sie in der kurzen Form, so wie du sie hörst.
- Die Laute „sh“ und „zh“: partition /pɑɹˈtɪ.ʃən/, notification /ˌnoʊtɪfɪˈkeɪʃn̩/, calculation /ˌkælkjuˈleɪʃn̩/, confirmation /ˌkɑn.fəɹˈmeɪ.ʃən/, threshold /ˈθɹɛʃ(h)oʊld/
- Lange Wörter – auf die Betonung achten: notification /ˌnoʊtɪfɪˈkeɪʃn̩/, analytics /ˌæn.əˈlɪt.ɪks/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, confirmation /ˌkɑn.fəɹˈmeɪ.ʃən/, recipient /ɹəˈsɪp.i.ənt/
So übst du mit diesem Video
- Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
- Beginne mit 0,75-facher Geschwindigkeit, sprich Satz für Satz nach und wechsle zur normalen Geschwindigkeit, sobald es leichtfällt.
- Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie topic, inventory, package.
Grammatik in diesem Video
Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:
| Struktur | Im Video |
|---|---|
| Bedingungssätze if + Satz, will/would + Verb – eine Bedingung und ihre Folge | if you find it valuable, we would appreciate · if it did, we would add · if you have partitions, you'll have |
| Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut | be updated · be sent · be generated |
| Relativsätze who / which + Satz – eine Zusatzinformation über eine Person oder Sache | services, which means · anyone who needs · order which will |
Was ist die Shadowing-Technik?
Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.