新手学习，如有错误请指正，感谢！

1.启动zookeeper和kafka，并建立一个topic为test-fkss，为了方便观察我是通过kafka-manager添加

2.配置Flume并启动，监听文件为/home/czh/docker-public-file/testflume.log，发送给kafka

a1.sources = r1
a1.sinks = k1
a1.channels = c1

a1.sources.r1.type = exec  
a1.sources.r1.command = tail -F /home/czh/docker-public-file/testflume.log
a1.sources.r1.channels = c1

a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.topic = test-fkss
a1.sinks.k1.brokerList = 172.17.0.2:9092
a1.sinks.k1.requiredAcks = 1
a1.sinks.k1.batchSize = 2
a1.sinks.k1.channel = c1

a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

在flume文件夹执行启动命令bin/flume-ng agent --conf conf --conf-file conf/flume-conf.properties.template --name a1 -Dflume.root.logger=INFO,console

出现最下面一行说明启动成功

3.启动redis

4.启动sparkstreaming日志处理程序，具体代码如下

分析程序SparkStreamDemo.scala

import kafka.serializer.StringDecoder
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}


object SparkStreamDemo {

  def main(args: Array[String]) {

    val conf = new SparkConf()
    conf.setAppName("flume_spark_streaming")
    conf.setMaster("local[4]")

    val sc = new SparkContext(conf)
    sc.setCheckpointDir("/home/czh/IdeaProjects/SparkDemo/out/artifacts")
    sc.setLogLevel("ERROR")

    val ssc = new StreamingContext(sc, Seconds(3))
    val kafkaParams = Map[String, String](
      "bootstrap.servers" -> "172.17.0.2:9092",
      "group.id" -> "test-fkss",
      "auto.offset.reset" -> "smallest"
    )
    val topics = Set("test-fkss");
    val stream =  createStream(ssc, kafkaParams, topics)
    val lines = stream.map(_._2)
    lines.foreachRDD { rdd =>
      rdd.foreachPartition { partitionOfRecords =>
        partitionOfRecords.foreach(pair => {
          val time = parseLog(pair)._1
          val ip = parseLog(pair)._2
          println(time+" "+ip)
          val jedis = RedisClient.pool.getResource
          jedis.select(0)
          if(null == jedis.get(ip))
            jedis.set(ip, "1");
          else
            jedis.incr(ip)
          val at = jedis.get(ip).toInt
          val c = new JDBCCommend()
          c.add(time, ip, at)
          jedis.close()
        })
      }
    }
    ssc.start()
    ssc.awaitTermination()

  }

  
  def createStream(scc: StreamingContext, kafkaParam: Map[String, String], topics: Set[String]) = {
    KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](scc, kafkaParam, topics)
  }


  def parseLog(log: String): (String, String) = {
    var time = ""
    var ip = ""
    try{
      time = log.split(" ")(0)
      ip = log.split(" ")(1)
    }
    catch {
      case e: Exception =>
        e.printStackTrace()
    }
    (time, ip)
  }
}

Redis连接池RedisClient.scala

import org.apache.commons.pool2.impl.GenericObjectPoolConfig
import redis.clients.jedis.JedisPool

object RedisClient extends Serializable {

  val redisHost = "127.0.0.1"
  val redisPort = 6599
  val redisTimeout = 30000
  lazy val pool = new JedisPool(new GenericObjectPoolConfig(), redisHost, redisPort, redisTimeout)

  lazy val hook = new Thread {
    override def run = {
      println("Execute hook thread: " + this)
      pool.destroy()
    }
  }
  sys.addShutdownHook(hook.run)
}

JDBC连接JDBCConnection.scala

import java.sql.{Connection, DriverManager}

object JDBCConnection {

  val IP = "172.17.0.2"
  val Port = "3306"
  val DBType = "mysql"
  val DBName = "st"
  val username = "root"
  val password = "root"
  val url = "jdbc:" + DBType + "://" + IP + ":" + Port + "/" + DBName
  classOf[com.mysql.jdbc.Driver]

  def getConnection(): Connection = {
    DriverManager.getConnection(url, username, password)
  }
  def close(conn: Connection): Unit = {
    try {
      if (!conn.isClosed() || conn != null) {
        conn.close()
      }
    }
    catch {
      case ex: Exception => {
        ex.printStackTrace()
      }
    }
  }
}

JDBC操作JDBCCommend.scala

class JDBCCommend {

  case class Ip(time: String, ip: String, access_times: Int)

  
  def add(time: String, ip: String, access_times: Int): Boolean = {
    val conn = JDBCConnection.getConnection()
    try {
      val sql = new StringBuilder()
        .append("INSERT INTO ip_table (time, ip, access_times)")
        .append("VALUES(?, ?, ?)")
      val ps = conn.prepareStatement(sql.toString())
      ps.setObject(1, time)
      ps.setObject(2, ip)
      ps.setObject(3, access_times)
      ps.executeUpdate() > 0
    }
    finally {
      conn.close()
    }
  }
}

  pom.xml

<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/maven-v4_0_0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>com</groupId>
  <artifactId>czh</artifactId>
  <version>1.0-SNAPSHOT</version>
  <inceptionYear>2008</inceptionYear>
  <properties>
    <scala.version>2.11.0</scala.version>
  </properties>

  <repositories>
    <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>scala-tools.org</id>
      <name>Scala-Tools Maven2 Repository</name>
      <url>http://scala-tools.org/repo-releases</url>
    </pluginRepository>
  </pluginRepositories>

  <dependencies>
    <dependency>
      <groupId>org.scala-lang</groupId>
      <artifactId>scala-library</artifactId>
      <version>${scala.version}</version>
    </dependency>
    <dependency>
      <groupId>org.specs</groupId>
      <artifactId>specs</artifactId>
      <version>1.2.5</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.11</artifactId>
      <version>2.3.2</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_2.11</artifactId>
      <version>2.3.2</version>
    </dependency>
    <dependency>
      <groupId>mysql</groupId>
      <artifactId>mysql-connector-java</artifactId>
      <version>5.1.12</version>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming_2.11</artifactId>
      <version>2.3.2</version>
    </dependency>
      <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming-kafka_2.11</artifactId>
      <version>1.6.3</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/redis.clients/jedis -->
    <dependency>
      <groupId>redis.clients</groupId>
      <artifactId>jedis</artifactId>
      <version>2.9.0</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/com.alibaba/fastjson -->
    <dependency>
      <groupId>com.alibaba</groupId>
      <artifactId>fastjson</artifactId>
      <version>1.2.47</version>
    </dependency>

  </dependencies>

  <build>
    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>
    <plugins>
      <plugin>
        <groupId>org.scala-tools</groupId>
        <artifactId>maven-scala-plugin</artifactId>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>testCompile</goal>
            </goals>
          </execution>
        </executions>
        <configuration>
          <scalaVersion>${scala.version}</scalaVersion>
          <args>
            <arg>-target:jvm-1.5</arg>
          </args>
        </configuration>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-eclipse-plugin</artifactId>
        <configuration>
          <downloadSources>true</downloadSources>
          <buildcommands>
            <buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>
          </buildcommands>
          <additionalProjectnatures>
            <projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>
          </additionalProjectnatures>
          <classpathContainers>
            <classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>
            <classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>
          </classpathContainers>
        </configuration>
      </plugin>
    </plugins>
  </build>
  <reporting>
    <plugins>
      <plugin>
        <groupId>org.scala-tools</groupId>
        <artifactId>maven-scala-plugin</artifactId>
        <configuration>
          <scalaVersion>${scala.version}</scalaVersion>
        </configuration>
      </plugin>
    </plugins>
  </reporting>
</project>

5.启动模拟ip访问产生日志程序

write-flumelog.py

import time
import random

for i in range(10):
    f = open('/home/czh/docker-public-file/testflume.log', 'a')
    time.sleep(random.randint(4, 9) * 0.1)
    current_time = time.time()
    ip = str(random.randint(4, 5)) + "." + str(random.randint(4, 4))
         + "." + str(random.randint(4, 5)) + "." + str(random.randint(4, 4))
    print(str(current_time) + " " + ip + "n")
    f.write(current_time.__str__() + " " + ip + "n")
    f.close()

整体运行结果如下：

python程序写入ip和访问时间到log文件

sparkstreaming程序实时获取flume传入的数据计算访问次数写入mysql