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Explore the transformative power of Apache Kafka in the landscape of computer science in this comprehensive guide. Delve into the architecture and foundations of this powerful open-source event streaming platform, to understand its critical role in simplifying data processing and its profound impact on modern web services. Take a deep dive into stream processing techniques with Kafka, and learn how it's used in real-world scenarios across various top companies. Moreover, the article offers an illuminating comparison between Apache Kafka and Flink, demystifying the key differences, strengths, limitations, and ideal use cases for both. Equip yourself with the requisite knowledge about this influential tool in the computing world.
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Jetzt kostenlos anmeldenExplore the transformative power of Apache Kafka in the landscape of computer science in this comprehensive guide. Delve into the architecture and foundations of this powerful open-source event streaming platform, to understand its critical role in simplifying data processing and its profound impact on modern web services. Take a deep dive into stream processing techniques with Kafka, and learn how it's used in real-world scenarios across various top companies. Moreover, the article offers an illuminating comparison between Apache Kafka and Flink, demystifying the key differences, strengths, limitations, and ideal use cases for both. Equip yourself with the requisite knowledge about this influential tool in the computing world.
Apache Kafka is an open-source stream-processing software developed by LinkedIn. Initially, its creation was to provide a unified, high-throughput, low-latency platform for handling real-time data feeds; however, its applications have widened over time.
For example, let's consider an e-commerce site using Kafka. The "producer" could be the website, generating data (like customer clicks or cart updates), and the "consumer" could be the recommendation system, processing this data to provide personalised suggestions.
In Computer Science, Apache Kafka is an essential tool because it offers a flexible, scalable, and reliable solution to the challenge of processing real-time data.
Real-time stream processing is becoming more crucial than ever before as modern web applications require the ability to handle real-time data for purposes such as personalisation, user engagement, and instant alerting.
Let's take an example of a taxi service wanting to display real-time data to users. With Kafka Streams, you can process data like the real-time position of their assigned cab, estimated time of arrival, and trip fare, and then display them instantly to the user.
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public class StreamApp {
public static void main(String[] args) {
KStreamBuilder builder = new KStreamBuilder();
KStream source = builder.stream("Taxis");
source.mapValues(value -> "ETA: " + value).to("UserApp");
...
}
}
A Topic in Kafka is a category or feed name to which records are published. Topics in Kafka are always multi-subscriber, meaning that a topic can have zero, one, or many consumers that subscribe to the data written to it.
// Producing messages to a Kafka topic ProducerRecordThese techniques reinforce the profound capabilities of Apache Kafka in creating an effective and efficient real-time data processing system, thereby making it an essential tool in the world of computer science.record = new ProducerRecord<>("Topic", "Key", "Value"); producer.send(record); producer.close(); // Consuming messages from a Kafka topic KafkaConsumer consumer = new KafkaConsumer<>(props); consumer.subscribe(Collections.singletonList("Topic")); while (true) { ConsumerRecords records = consumer.poll(100); for (ConsumerRecord record : records) { System.out.println(record.offset() + ": " + record.value()); } }
// Producing messages to a Kafka topic ProducerRecordAt the same time, the analytics system could function as the consumer, reading these updates in real-time and adjusting inventory predictions and analyses accordingly.record = new ProducerRecord<>("Inventory", "ProductID", "NewQuantity"); producer.send(record); producer.close();
// Consuming messages from a Kafka topic
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("group.id", "test");
props.put("enable.auto.commit", "true");
props.put("auto.commit.interval.ms", "1000");
props.put("session.timeout.ms", "30000");
props.put("key.deserializer",
"org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer",
"org.apache.kafka.common.serialization.StringDeserializer");
KafkaConsumer consumer = new KafkaConsumer(props);
consumer.subscribe(Arrays.asList("Inventory"));
while (true) {
ConsumerRecords records = consumer.poll(100);
for (ConsumerRecord record : records)
System.out.printf("offset = %d, key = %s, value = %s\n", record.offset(), record.key(), record.value());
}
Implemented by LinkedIn, Kafka was developed to handle the website's activity stream data and operational metrics. LinkedIn uses Kafka to track activity data and operational metrics, ensuring every event is available in real-time for follow-up processes. By acting as a bridge between data producers and data consumers, Kafka helps LinkedIn transmit each member's action, like viewing a page or sending a message, to LinkedIn's data consumers for real-time monitoring and analysis.
| Apache Kafka | Apache Flink |
| High throughput | Advanced stream processing |
| Built-in fault tolerance | Strong support for event time processing |
| Limited complex analytics capabilities | Not ideal for long-term data storage |
Apache Kafka excels in scenarios where you need a robust, high throughput system to handle real-time data streaming. A key use case is real-time log aggregation, where Apache Kafka collects and aggregates logs from different services and streams them to a central location for processing. Another is stream processing, where constant streams of data are processed and transformed in real-time before being sent to downstream systems.
Flink is ideal for complex analytics over streaming data. Its stream processing capabilities enable it to perform a wide array of transformations and aggregations, even on unbounded data streams. Flink is perfectly suited for Event-driven applications where time and order of events matter. Flink's ability to handle late events and provide exactly-once processing semantics makes it a solid choice for these use-cases.
// A simple Flink Job using the Table API streamEnv.executeSql( "CREATE TABLE Orders (`user` STRING, product STRING, amount INT) WITH (..)"); streamEnv.executeSql( "CREATE TABLE ProductStats (product STRING, amount INT, wstart TIMESTAMP(3), " + "wend TIMESTAMP(3), PRIMARY KEY(product, wstart) NOT ENFORCED) WITH (...)"); streamEnv.executeSql( "INSERT INTO ProductStats SELECT product, SUM(amount) as amount, " + "TUMBLE_START(`time`, INTERVAL '1' HOUR) AS wstart, " + "TUMBLE_END(`time`, INTERVAL '1' HOUR) AS wend FROM Orders GROUP BY product, " + "TUMBLE(`time`, INTERVAL '1' HOUR)");Deciding between Apache Kafka and Flink is not always an 'either-or' decision; these technologies can also work together within the same system, complementing each other's strengths. For instance, Kafka can serve as a reliable, real-time event source for a Flink job, which can then conduct time-windowed, analytical computations.
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Start learningWhat is Apache Kafka and who developed it?
Apache Kafka is a real-time, distributed, publish-subscribe streaming platform capable of handling trillions of events in a day. It was originally developed by LinkedIn and later handed over to the Apache Software Foundation.
What are the essential components of Apache Kafka?
The essential components of Apache Kafka are the Producer (creates the data), Broker (hosts the data), Consumer (uses the data), and Topic (categorised feed stream).
How does Apache Kafka work?
Producers send messages to Kafka brokers. Each message belongs to a specific topic. These messages are divided into 'partitions' for better management and fault tolerance. Consumer applications then read messages from the broker and process them.
What is Apache Kafka predominantly used for?
Apache Kafka is predominantly used for real-time data streaming, serving as the backbone for many services that rely heavily on speedy, reliable data handling.
Why is knowing Apache Kafka valuable for Computer Science students?
Knowing Apache Kafka is valuable as it provides insights into how distributed systems work, broadens understanding of data stream processing - important for fields like FinTech or IoT, and enlightens on complex dual-role technologies.
What role does Apache Kafka play in microservices architecture and big data ecosystems?
In a microservices architecture, Kafka ensures high-speed communication between different services. In big data ecosystems, Kafka can ingest massive real-time data volumes and publish them to multiple systems for timely insights and decision-making.
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