测试文本内容:(hanzi.txt)
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（一）maven构建
<?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">
<parent>
<artifactId>SSO-Scala</artifactId>
<groupId>org.jy.data.yh.bigdata.drools.scala</groupId>
<version>1.0-SNAPSHOT</version>
</parent>
<modelVersion>4.0.0</modelVersion>
<groupId>org.jy.bigdata.drools.scala.kafka.freq</groupId>
<artifactId>SSO-Scala-kafka-streaming</artifactId>
<!--<version>1.0-SNAPSHOT</version>-->
<name>SSO-Scala-kafka-streaming</name>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<scala.version>2.11.8</scala.version>
<spark.version>2.4.0</spark.version>
</properties>
<repositories>
<repository>
<id>repos</id>
<name>Repository</name>
<url>http://maven.aliyun.com/nexus/content/groups/public</url>
</repository>
<repository>
<id>scala-tools.org</id>
<name>Scala-Tools Maven2 Repository</name>
<url>http://scala-tools.org/repo-releases</url>
</repository>
</repositories>
<pluginRepositories>
<pluginRepository>
<id>repos</id>
<name>Repository</name>
<url>http://maven.aliyun.com/nexus/content/groups/public</url>
</pluginRepository>
<pluginRepository>
<id>scala-tools.org</id>
<name>Scala-Tools Maven2 Repository</name>
<url>http://scala-tools.org/repo-releases</url>
</pluginRepository>
</pluginRepositories>
<dependencies>
<!--Spark Core核心依赖-->
<dependency> <!-- Spark ,依赖的Scala版本为Scala 2.12.x版本 (View all targets) 否则会报错
java.lang.NoSuchMethodError: scala.Predef$.refArrayOps([Ljava/lang/Object;)[Ljava/lang/Object; -->
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.4.0</version>
<!--<scope>provided</scope>--><!--运行时提供,打包不添加,Spark集群已自带-->
</dependency>
<!-- Spark SQL核心依赖-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>2.4.0</version>
<!--<scope>provided</scope>--><!--运行时提供,打包不添加,Spark集群已自带-->
</dependency>
<!-- Spark Streaming依赖包-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>2.4.0</version>
<!--<scope>provided</scope>--><!--运行时提供,打包不添加,Spark集群已自带-->
</dependency>
<dependency><!-- Spark Streaming with Kafka 版本要与scala匹配-->
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency><!-- Mysql -->
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.31</version>
</dependency>
<!--数据库连接池-->
<dependency>
<groupId>c3p0</groupId>
<artifactId>c3p0</artifactId>
<version>0.9.1.2</version>
</dependency>
<dependency><!-- JSON -->
<groupId>io.spray</groupId>
<artifactId>spray-json_2.10</artifactId>
<version>1.3.2</version>
</dependency>
<dependency><!-- Http -->
<groupId>org.scalaj</groupId>
<artifactId>scalaj-http_2.10</artifactId>
<version>2.3.0</version>
</dependency>
<dependency><!-- Time Parse -->
<groupId>joda-time</groupId>
<artifactId>joda-time</artifactId>
<version>2.9.4</version>
</dependency>
<dependency><!-- Log -->
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
<version>1.2.17</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.12</version>
</dependency>
<!-- 2.12.x需要与spark的2.12对应-->
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
</dependencies>
<build>
<finalName>SSO-Analysis-system</finalName>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<!-- 混合Scala/Java编译-->
<plugin><!--Scala编译插件 -->
<groupId>org.scala-tools</groupId>
<artifactId>maven-scala-plugin</artifactId>
<version>2.15.2</version>
<executions>
<execution>
<id>scala-compile-first</id>
<goals>
<goal>compile</goal>
</goals>
<configuration>
<includes>
<include>**/*.scala</include>
</includes>
</configuration>
</execution>
<execution>
<id>scala-test-compile</id>
<goals>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<configuration>
<source>1.8</source><!-- 设置Java源-->
<target>1.8</target>
</configuration>
</plugin>
<plugin><!-- 将所有的依赖包打入同一个jar包-->
<artifactId>maven-assembly-plugin</artifactId>
<configuration>
<appendAssemblyId>false</appendAssemblyId>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef><!--jar包的后缀名-->
</descriptorRefs>
<archive>
<manifest>
<mainClass>org.jy.bigdata.drools.scala.data.freq.main.KafkaProducer</mainClass>
</manifest>
</archive>
</configuration>
<executions>
<execution>
<id>make-assembly</id>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin><!--Maven打包插件 -->
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-jar-plugin</artifactId>
<version>2.4</version>
<configuration>
<archive>
<manifest>
<addClasspath>true</addClasspath><!--添加类路径-->
<!--设置程序的入口类-->
<mainClass>org.jy.bigdata.drools.scala.data.freq.main.KafkaProducer</mainClass>
</manifest>
</archive>
</configuration>
</plugin>
</plugins>
</build>
</project>
（二）生产者（完整代码D:\\Bigdata-Drools-platform\\SSO-Scala\\SSO-Scala-Kafka-streaming\\hanzi.txt 该路径为本地测试文本）
package org.jy.bigdata.drools.scala.data.freq.main
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 KafkaProducerStream extends App { // App为scala中的特质，实现了main方法
@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","node-1:9092,node-2:9092,node-3:9092") // 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:\\Bigdata-Drools-platform\\SSO-Scala\\SSO-Scala-Kafka-streaming\\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]("freqCountTopic",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)
}
System.out.println("send per second: "+events*1000/(System.currentTimeMillis()-startTime))
producer.close()
}
}
(三) 消费者实例代码
package org.jy.bigdata.drools.scala.data.freq.main
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}
/**
* 创建消费消息的实体类
*/
class KafkaProducerConsumer(val brokers:String,
val groupId:String,
val topic:String ) { // 伴生类
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
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 KafkaProducerConsumer extends App { // 伴生对象
override def main(args: Array[String]): Unit = {
val brokers = "node-1:9092,node-2:9092,node-3:9092"
val groupId ="KafkaProducerConsumerGroup"
val topic ="freqCountTopic"
val example = new KafkaProducerConsumer(brokers,groupId,topic)
example.run()
}
}
测试结果如下图
