目录

一.说明

二.flume

三.kafka

四.MySQL

五.IDEA写程序

六.运行

一.说明

1.1使用工具：IDEA，spark-2.1.0-bin-hadoop2.7，kafka_2.11-2.3.1，zookeeper-3.4.5，apache-flume-1.9.0-bin，jdk1.8.0_171 

Scala版本：2.12.15

 相关工具的安装请关注我的博客！

1.2日志可以到这里下载：testlog7.log-spark文档类资源-CSDN下载

也可以用代码生成：Scala模拟日志生成_一个人的牛牛的博客-CSDN博客

1.3一定要在数据库中建表！！！！！

二.flume

2.1在flume的conf下写flume-kafka.conf

a5.channels=c5
a5.sources=s5
a5.sinks=k5

a5.sources.s5.type=spooldir
#/root/testdata/f-k是flume监控的文件夹
a5.sources.s5.spoolDir=/root/testdata/f-k
a5.sources.s5.interceptors=head_filter
#正则拦截器
a5.sources.s5.interceptors.head_filter.type=regex_filter
a5.sources.s5.interceptors.head_filter.regex=^event_id.*
a5.sources.s5.interceptors.head_filter.excludeEvents=true

#用来关联kafka
a5.sinks.k5.type=org.apache.flume.sink.kafka.KafkaSink
#连接kafka，hadoop01是我的虚拟机主机名
a5.sinks.k5.kafka.bootstrap.servers=hadoop01:9092
#topic的主题名要一致fktest
a5.sinks.k5.kafka.topic=fktest

a5.channels.c5.type=memory
a5.channels.c5.capacity=10000
a5.channels.c5.transactionCapacity=10000

a5.sinks.k5.channel=c5
a5.sources.s5.channels=c5

 2.2建/root/testdata/f-k文件夹

2.3启动flume的flume-kafka.conf（在flume的目录下）

bin/flume-ng agent -f conf/flume-kafka.conf -n a5 -Dflume.root.logger=INFO,console

看到k5,c5,s5到成功启动了（有Successfully）就是正常

三.kafka

3.1开启kafka（在kafka的目录下）（一定要先开启zookeeper）

bin/kafka-server-start.sh -daemon config/server.properties

 3.2建topic在kafka的目录下

bin/kafka-topics.sh --create --zookeeper hadoop01:2181 --replication-factor 1 --partitions 1 --topic fktest

查看topic

bin/kafka-topics.sh --zookeeper hadoop01:2181 --list

3.3打开kafka的消费者（在kafka的目录下）（hadoop01是我的主机名）

bin/kafka-console-consumer.sh --bootstrap-server hadoop01:9092 --topic fktest --from-beginning

四.MySQL

4.1建表

五.IDEA写程序

5.1导入依赖（我的依赖）

<dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.6</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>2.4.8</version>
        </dependency>
        <dependency>
            <groupId>com.google.guava</groupId>
            <artifactId>guava</artifactId>
            <version>14.0.1</version>
        </dependency>
        <dependency>
           <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-hdfs</artifactId>
        <version>2.7.3</version>
    </dependency>
            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-core_${spark.artifact.version}</artifactId>
                <version>${spark.version}</version>
            </dependency>
            <!-- 使用scala2.11.8进行编译和打包 -->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.12</artifactId>
            <version>2.4.8</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.12</artifactId>
            <version>2.4.8</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_2.12</artifactId>
            <version>2.4.8</version>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>0.11.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>2.4.8</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.6</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.12</artifactId>
            <version>2.4.8</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hive</groupId>
            <artifactId>hive-jdbc</artifactId>
            <version>1.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-flume_2.12</artifactId>
            <version>2.4.8</version>

        <dependency>
        <groupId>org.apache.flume.flume-ng-clients</groupId>
        <artifactId>flume-ng-log4jappender</artifactId>
        <version>1.9.0</version>
    </dependency>

5.2代码：

import java.sql.{Connection, DriverManager, PreparedStatement}
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}

//写人MySQL表的表名和列名，string，int是数据格式
case class phone(
                 name: String,
                 count:Int
               )

object KafkaDemo {
  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setMaster("local[2]").setAppName("KafkaTest")
    val streamingContext = new StreamingContext(sparkConf, Seconds(20))
//20秒更新一次结果

    //连接kafka导入数据，hadoop01是我的虚拟机主机名
    val kafkaParams = Map[String, Object](
      "bootstrap.servers" -> "hadoop01:9092",从broker消费数据
      "key.deserializer" -> classOf[StringDeserializer],//发序列化的参数，因为写入kafka的数据经过序列化
      "value.deserializer" -> classOf[StringDeserializer],//发序列化的参数，因为写入kafka的数据经过序列化
      "group.id" -> "use_a_separate_group_id_for_each_stream",//指定group.id
      "auto.offset.reset" -> "latest",//指定消费的offset从哪里开始：① earliest:从头开始  ；② latest从消费者启动之后开始
      "enable.auto.commit" -> (false: java.lang.Boolean) //是否自动提交偏移量 offset 。默认值就是true【5秒钟更新一次】, "false" 不让kafka自动维护偏移量     手动维护偏移
    )

  
    //kafka的topic
    val topics = Array("fktest", "test")

    //订阅主题
    val stream = KafkaUtils.createDirectStream[String, String](
      streamingContext,
      PreferConsistent,
      Subscribe[String, String](topics, kafkaParams)
    )

    //转换格式
    val mapDStream: DStream[(String, String)] = stream.map(record => (record.key, record.value))
    //分析处理出想要的数据
    val resultRDD: DStream[(String, Int)] = mapDStream.map(lines=>{
      val data = lines._2.split("_")
      (data(1),1)}).reduceByKey(_+_)


    //将DStream中的数据存储到mysql数据库中
    resultRDD.foreachRDD(
      rdd=>{
        val url = "jdbc:mysql://192.168.17.128:3306/hive?useUnicode=true&characterEncoding=UTF-8"//192.168.17.140是我的主机IP地址，可以用localhost。hive是我的数据库名
        val user = "root"//MySQL用户名
        val password = "1234567"//MySQL密码
        Class.forName("com.mysql.jdbc.Driver").newInstance()//驱动
        rdd.foreach(
          data=>{
            var conn: Connection = DriverManager.getConnection(url,user,password)
            val sql = "insert into phone(name,count) values(?,?)"
//第一个phone是表名，(name,count)是列名
            var stmt : PreparedStatement = conn.prepareStatement(sql)
            stmt.setString(1,data._1.toString)
            stmt.setString(2,data._2.toString)
            stmt.executeUpdate()
            conn.close()
          }
        )
      }

    )

    //输出结果到控制台
    resultRDD.print()
    // 启动
    streamingContext.start()
    // 等待计算结束
    streamingContext.awaitTermination()
  }
}

4.3运行程序

六.运行

6.1在/root/testdata/f-k文件夹里面添加数据，直接拖入xxx.log。

flume处理后是这样的。

6.2拖入后kafka消费者会显示内容。

6.3没有开消费者的话可以到kafka设置的日志文件夹下查看。

 6.4MySQL的显示

谢谢！！！！

# mysql # flume # kafka # 大数据 # scala
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