How to use Java to develop a stream processing application based on Apache Kafka and KSQL

How to use Java to develop a stream processing application based on Apache Kafka and KSQL

Stream processing is a technology that processes real-time data streams and can process data as soon as it arrives. Analyze and process it. Apache Kafka is a distributed stream processing platform that can be used to efficiently build scalable stream processing applications. KSQL is an open source stream data processing engine that can be used for SQL query and conversion of real-time stream data. In this article, we will introduce how to use Java to develop a stream processing application based on Apache Kafka and KSQL.

1. Environment setup
Before we start, we need to set up a local Kafka and KSQL environment. First, we need to download and install Java JDK, Apache Kafka and Confluent Platform. We can then start Kafka and KSQL using the following command:

1. Start ZooKeeper:
   bin/zookeeper-server-start.sh config/zookeeper.properties
2. Start Kafka Broker :
   bin/kafka-server-start.sh config/server.properties
3. Start KSQL Server:
   bin/ksql-server-start.sh config/ksql-server.properties

2. Create a Kafka topic and KSQL table
Before we start writing Java code, we need to create a Kafka topic and write real-time data into it. We can create a topic named "example-topic" using the following command:

bin/kafka-topics.sh --bootstrap-server localhost:9092 --create --topic example-topic --partitions 1 --replication-factor 1

Next, we need to create a table in KSQL for querying and transforming real-time data. We can create a table named "example-table" in the KSQL terminal using the following command:

CREATE TABLE example_table (key VARCHAR, value VARCHAR) WITH (kafka_topic='example-topic', value_format='json ', key='key');

3. Java code implementation
Before starting to write Java code, we need to add dependencies on Kafka and KSQL. We can add the following dependencies in the Maven or Gradle configuration file:

Maven:

<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>2.5.0</version>

<groupId>io.confluent</groupId>
<artifactId>ksql-serde</artifactId>
<version>0.10.0</version>

Gradle:

implementation 'org.apache.kafka:kafka-clients:2.5.0'
implementation 'io. confluent:ksql-serde:0.10.0'

Next, we can write Java code to implement the stream processing application. The following is a simple sample code:

import org.apache.kafka.clients.consumer.*;
import org.apache.kafka.clients.producer.*;
import org.apache .kafka.common.*;
import org.apache.kafka.connect.json.JsonDeserializer;
import org.apache.kafka.connect.json.JsonSerializer;
import org.apache.kafka.streams .*;
import org.apache.kafka.streams.kstream.*;
import org.apache.kafka.streams.processor.WallclockTimestampExtractor;
import org.apache.kafka.streams.state.* ;
import java.util.*;
import java.util.concurrent.*;

public class StreamProcessingApp {

public static void main(String[] args) {
    Properties props = new Properties();
    props.put(StreamsConfig.APPLICATION_ID_CONFIG, "stream-processing-app");
    props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");

    StreamsBuilder builder = new StreamsBuilder();

    // Step 1: Read from Kafka topic
    KStream<String, String> stream = builder.stream("example-topic");

    // Step 2: Transform and process the data
    stream.mapValues(value -> value.toUpperCase())
          .filter((key, value) -> value.startsWith("A"))
          .to("processed-topic");

    // Step 3: Create a Kafka producer to send data to another topic
    Properties producerProps = new Properties();
    producerProps.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
    producerProps.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, JsonSerializer.class);
    producerProps.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, JsonSerializer.class);

    KafkaProducer<String, String> producer = new KafkaProducer<>(producerProps);

    // Step 4: Consume and process the data from the processed topic
    KStream<String, String> processedStream = builder.stream("processed-topic");
    processedStream.foreach((key, value) -> {
        // Process the data here
        System.out.println("Key: " + key + ", Value: " + value);
    });

    KafkaStreams streams = new KafkaStreams(builder.build(), props);
    streams.start();
}

}

The above code implementation Create a simple stream processing application that reads real-time data from the "example-topic" topic, converts the data to uppercase, and writes data starting with the letter "A" to the "processed-topic" topic. At the same time, it will also consume the data in the "processed-topic" topic and process it.

4. Run the application
After writing the Java code, we can use the following commands to compile and run the application:

javac StreamProcessingApp.java
java StreamProcessingApp

Now, we have successfully developed a stream processing application based on Apache Kafka and KSQL, and implemented data reading, conversion, processing and writing through Java code. You can modify and extend the code according to actual needs to meet your business needs. Hope this article is helpful to you!

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