模拟爬虫从网络中爬取数据的过程，并将数据灌输到Kafka中，供下游消费者使用
pom.xml
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.wspark</groupId>
<artifactId>KafkaProducer</artifactId>
<version>1.0-SNAPSHOT</version>
<build>
<plugins>
<plugin>
<groupId>org.scala-tools</groupId>
<artifactId>maven-scala-plugin</artifactId>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<artifactId>maven-assembly-plugin</artifactId>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
</configuration>
<version>2.3</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>2.12.12</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-reflect</artifactId>
<version>2.12.12</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-compiler</artifactId>
<version>2.12.12</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.12</artifactId>
<version>3.1.2</version>
<!--<scope>compile</scope>-->
<exclusions>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-reflect</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-compiler</artifactId>
</exclusion>
</exclusions>
</dependency>
<!--Spark核心依赖包-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.12</artifactId>
<version>3.1.2</version>
<!--<scope>compile</scope>-->
<exclusions>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-reflect</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-compiler</artifactId>
</exclusion>
</exclusions>
</dependency>
<!--SparkStreaming依赖包-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.12</artifactId>
<version>3.1.2</version>
<exclusions>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-reflect</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-compiler</artifactId>
</exclusion>
</exclusions>
</dependency>
<!--Spark Streaming Kafka依赖包 -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_2.12</artifactId>
<version>3.1.2</version>
<exclusions>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-reflect</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-compiler</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-hive_2.12</artifactId>
<version>3.1.2</version>
<exclusions>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-reflect</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-compiler</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-mllib_2.12</artifactId>
<version>3.1.2</version>
<exclusions>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-reflect</artifactId>
</exclusion>
<exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-compiler</artifactId>
</exclusion>
</exclusions>
</dependency>
<!-- <dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-swing</artifactId>
<version>2.12.12</version>
</dependency>-->
<!--<dependency>
<groupId>org.apache.storm</groupId>
<artifactId>storm-kafka</artifactId>
<version>1.1.0</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>2.7.0</version>
</dependency>-->
<!-- https://mvnrepository.com/artifact/org.slf4j/slf4j-log4j12 -->
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.21</version>
<scope>compile</scope>
</dependency>
<!-- https://mvnrepository.com/artifact/log4j/log4j -->
<dependency>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
<version>1.2.17</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-simple</artifactId>
<version>1.7.25</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>1.7.25</version>
</dependency>
</dependencies>
</project>完成jar包引入，创建Producer进行虚拟生产者的开发，连接kafka并向其推送模拟的用户评论数据
import java.util.Properties
import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord}
import scala.util.Random
/*
* 用户生成模拟数据的生产者
* */
object Producer extends App {
@Override
override def main(args: Array[String]): Unit = {
val rnd = new Random()
val events = 100000 // 一万条消息
val props = new Properties()
props.put("bootstrap.servers", "192.168.30.21:9092,192.168.30.22:9092,192.168.30.23:9092") // broker集群地址
props.put("client.id", "KafkaFreqGenerator")
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer") // 可以序列化
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer") // value 序列化
val producer = new KafkaProducer[String, String](props) // 创建kafka对象
val startTime = System.currentTimeMillis(); // 毫秒时间
// 读取汉字字典 /home/hadoop/tools/jsonfile/ 当前工作目录
val source = scala.io.Source.fromFile("D:\\360Downloads\\hanzi.txt") // 从指定的目录读取文本文件
val lines = try source.mkString finally source.close() // try finally 使用技巧
for (nEvents <- Range(0, events)) { // 循环生成一万条用户的评论数据
// 模拟评论数据(user,comment)
val sb = new StringBuilder()
for (ind <- Range(0, rnd.nextInt(200))) {
sb += lines.charAt(rnd.nextInt(lines.length()))
}
val userName = "user_" + rnd.nextInt(100)
//println("<message content> :"+sb.toString())
val data = new ProducerRecord[String, String]("t01", userName, sb.toString()) // userName -- key sb.toString()为值
// 发送评论到kafka
/* val futureCallback = producer.send(data)
val answer = futureCallback.get()
val topic = answer.topic();*/
//println("messege has sended : "+topic)
producer.send(data)
}
System.out.println("sent per second: " + events * 1000 / (System.currentTimeMillis() - startTime))
producer.close()
}
}?
为了验证数据生成器效果，创建ProducerConsumer，创建一个简单的消费者来进行检验
import java.time.Duration
import java.util.concurrent._
import java.util.{Collections, Properties}
import scala.collection.JavaConversions._
import org.apache.kafka.clients.consumer.{ConsumerConfig, KafkaConsumer}
class ProducerConsumer (val brokers:String,
val groupId:String,
val topic:String){
val props = createCunsumerConfig(brokers,groupId)
val consumer = new KafkaConsumer[String,String](props)
def shutdown() = { // 关闭函数
if(consumer != null)
consumer.close()
}
// 定义类中的方法
def createCunsumerConfig(brokers: String,groupId: String): Properties ={ // 定义类中的方法
val props = new Properties()
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,brokers) // 设置kafaka集群地址
props.put(ConsumerConfig.GROUP_ID_CONFIG,groupId)
props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,"true") // 设置自动提交
props.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG,"1000")
props.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG,"30000")
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer")
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringDeserializer")
return props
}
// 定义无返回值的方法
def run()={
consumer.subscribe(Collections.singletonList(this.topic)) // 一个topic主题数组
Executors.newSingleThreadExecutor.execute(new Runnable {
override def run(): Unit = { // 覆盖run方法
//不断拉取指定Topic的数据，并打印输出查看+ T
while (true) {
val records = consumer.poll(Duration.ofSeconds(1000)) // 1秒钟拉一次
for (record <- records) {
System.out.println("Received message: (" + record.key() + ", " + record.value() + ") at offset " + record.offset())
}
}
}
})
}
}
object ProducerConsumer extends App { // 伴生对象
override def main(args: Array[String]): Unit = {
val brokers = "192.168.30.21:9092,192.168.30.22:9092,192.168.30.23:9092"
val groupId ="KafkaProducerConsumerGroup"
val topic ="t01"
val example = new ProducerConsumer(brokers,groupId,topic)
example.run()
}
}结果：
