kafka+sparkStreaming+mysql

一、说明

1、一个程序模拟用户每个时间点到达的地方和走的步数信息，并实时写入kafka主题；sparkStreaming实时从kafka消费这些信息进行分析并存储到mysql;这里直接存储到mysql;

2、sparkStreaming存储mysql的最好思路为这样：

3、mysql要提前创建表

create table walk_info(user varchar(20),counttime varchar(40),walkplace nvarchar(100),newwalknum int(20));

二、 代码实现

1、maven依赖

<?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.spark.demo</groupId>
    <artifactId>sparkProgram</artifactId>
    <version>1.0-SNAPSHOT</version>
    <dependencies>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>2.11.8</version>
        </dependency>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-compiler</artifactId>
            <version>2.11.8</version>
        </dependency>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-reflect</artifactId>
            <version>2.11.8</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>2.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>2.2.1</version>
            <!--<scope>provided</scope>-->
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
            <version>2.2.1</version>
        </dependency>
        <dependency><!-- Spark Streaming Kafka -->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka_2.10</artifactId>
            <version>1.6.3</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-hive -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>2.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.gavaghan</groupId>
            <artifactId>geodesy</artifactId>
            <version>1.1.3</version>
        </dependency>
        <dependency>
            <groupId>com.github.scopt</groupId>
            <artifactId>scopt_2.11</artifactId>
            <version>3.7.0</version>
        </dependency>
        <dependency>
            <groupId>com.google.code.gson</groupId>
            <artifactId>gson</artifactId>
            <version>2.2.4</version>
        </dependency>

        <dependency>
            <groupId>redis.clients</groupId>
            <artifactId>jedis</artifactId>
            <version>2.9.0</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.codehaus.jettison/jettison -->
        <dependency>
            <groupId>org.codehaus.jettison</groupId>
            <artifactId>jettison</artifactId>
            <version>1.1</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/net.sf.json-lib/json-lib -->
        <dependency>
            <groupId>net.sf.json-lib</groupId>
            <artifactId>json-lib</artifactId>
            <version>2.4</version>
            <classifier>jdk15</classifier>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.commons/commons-pool2 -->
        <dependency>
            <groupId>org.apache.commons</groupId>
            <artifactId>commons-pool2</artifactId>
            <version>2.4.2</version>
        </dependency>
        <!-- orcale驱动 -->
        <dependency>
            <groupId>com.oracle</groupId>
            <artifactId>ojdbc6</artifactId>
            <version>12.1.0.1-atlassian-hosted</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hbase/hbase-client -->
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.2.6</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.hbase/hbase-server -->
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>1.2.6</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-hive-thriftserver -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive-thriftserver_2.11</artifactId>
            <version>2.1.1</version>
            <scope>provided</scope>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-client -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.3</version>
        </dependency>

        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.11</artifactId>
            <version>0.11.0.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-streams</artifactId>
            <version>1.0.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>1.0.2</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka-streams-scala -->
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-streams-scala_2.11</artifactId>
            <version>2.4.0</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.38</version>
        </dependency>
        <dependency>
            <groupId>commons-dbcp</groupId>
            <artifactId>commons-dbcp</artifactId>
            <version>1.4</version>
        </dependency>
    </dependencies>
    <build>
        <finalName>CountWalk-1.0.0</finalName>
        <pluginManagement>
            <plugins>
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-compiler-plugin</artifactId>
                    <version>3.0</version>
                </plugin>
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-surefire-plugin</artifactId>
                    <version>2.12.4</version>
                </plugin>
            </plugins>


        </pluginManagement>
        <plugins>
            <plugin>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                    <encoding>UTF-8</encoding>
                </configuration>
            </plugin>
            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                    <!-- <descriptor>src/main/resources/assembly.xml</descriptor> -->
                    <appendAssemblyId>false</appendAssemblyId>
                </configuration>
            </plugin>
            <!-- 拷贝依赖的jar包到lib目录 -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-dependency-plugin</artifactId>
                <executions>
                    <execution>
                        <id>copy</id>
                        <phase>package</phase>
                        <goals>
                            <goal>copy-dependencies</goal>
                        </goals>
                        <configuration>
                            <outputDirectory>
                                ${project.build.directory}/lib
                            </outputDirectory>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

2、模拟数据代码

package com.hyj.util

import java.util.Properties

import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord}
import org.codehaus.jettison.json.JSONObject

import scala.util.Random


/**
  * 编写一个提交数据到kafka集群的producer
  * 模拟场景：
  * 统计一些用户实时步行的总步数，每隔1s统计一次，包括某个用户新统计时的时间、所在地点、新增步数；
  */
object KafkaEventProducer {

  //用户
  private val users = Array(
    "zhangSan", "liSi",
    "wangWu", "xiaoQiang"
  )

  private var pointer = -1

  //随机获得用户
  def getUser(): String = {
    pointer = (pointer + 1) % users.length
    users(pointer)
  }

  //获取新增步数
  val random = new Random()
//
  def getNewStepNum(): Int = {
    random.nextInt(users.length)+1
  }

  //获取统计时间
  def getTime(): Long = {
    System.currentTimeMillis()
  }

  //获取行走地点
  val walkPlace = Array(
    "操场南门", "操场东门", "操场北门", "操场西门", "操场东南门", "操场西北门", "操场西南门", "操场东北门"
  )

  def getWalkPlace(): String = {
    walkPlace(random.nextInt(walkPlace.length))
  }


  def main(args: Array[String]): Unit = {

    val topic = "topic_walkCount"
    val brokers = "192.168.230.21:6667,192.168.230.22:6667,192.168.230.23:6667"
    //设置属性,配置
    val props = new Properties()
    props.setProperty("bootstrap.servers", brokers)
    props.setProperty("metadata.broker.list", brokers)
    props.setProperty("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
    props.setProperty("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")

    //生成producer对象
    val producer = new KafkaProducer[String, String](props)

    //传输数据
    while (true) {
      val event = new JSONObject()
      event.put("user", getUser())
        //.put("count_time", getTime())
        .put("count_time", TimeUtil.tranTimeToString(getTime().toString))
        .put("walk_place", getWalkPlace())
        .put("new_walkNum", getNewStepNum())
      println(event.toString())
      //发送数据
      producer.send(new ProducerRecord[String, String](topic, event.toString))
      Thread.sleep(1000)
    }
  }
}

3、数据库连接池

package com.hyj.util;

import java.sql.Connection;
import java.sql.DriverManager;
import java.util.LinkedList;
public class MySqlConnectionPool {
    private static LinkedList<Connection> connectionQueue;

    static {
        try {
            Class.forName("com.mysql.jdbc.Driver");
        }catch (ClassNotFoundException e) {
            e.printStackTrace();
        }
    }

    public synchronized static Connection getConnection() {
        try {
            if (connectionQueue == null) {
                connectionQueue = new LinkedList<Connection>();
                for (int i = 0;i < 5;i ++) {
                    Connection conn = DriverManager.getConnection(
                            "jdbc:mysql://192.168.230.21:3306/test?characterEncoding=utf8&useSSL=true",
                            "root",
                            "123456"
                    );
                    connectionQueue.push(conn);
                }
            }
        }catch (Exception e) {
            e.printStackTrace();
        }
        return connectionQueue.poll();
    }

    public static void returnConnection(Connection conn) {
        connectionQueue.push(conn);
    }
}

4、工具类

package com.hyj.util

import java.text.SimpleDateFormat
import java.util.{Calendar, Date}

object TimeUtil {
  //时间转化为时间戳
  def tranTimeToLong(tm:String) :Long={
    val fm = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
    val dt = fm.parse(tm)
    val aa = fm.format(dt)
    val tim: Long = dt.getTime()
    tim
  }

  //时间戳转化为时间
  def tranTimeToString(tm:String) :String={
    val fm = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
    val tim = fm.format(new Date(tm.toLong))
    tim
  }
  //得到本月最后一天的日期
  def getLastDateOfMonth():String={
    val now: Date = new Date();
    val dateFormat: SimpleDateFormat = new SimpleDateFormat("yyyy-MM-dd");
    val dateNow = dateFormat.format(now);
    val day =dateNow.substring(0,4)+dateNow.substring(5,7)+dateNow.substring(8,10);
    val year=dateNow.substring(0,4).toInt;
    val month=dateNow.substring(5,7).toInt;
    val cal = Calendar.getInstance();
    cal.set(Calendar.YEAR, year);
    //cal.set(Calendar.MONTH, month-2);//上个月最后一天
    cal.set(Calendar.MONTH, month-1);
    cal.set(Calendar.DAY_OF_MONTH,cal.getActualMaximum(Calendar.DATE));
    new SimpleDateFormat("yyyy-MM-dd").format(cal.getTime())+" 23:59:59"
  }
  //获取当前时间
  def getCurrentTime():String={
    val now: Date = new Date();
    val dateFormat: SimpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
    dateFormat.format(now);
  }

  //获取两个日期时间间隔秒数
  def getTimeDifferenceOfTwoTime(startTime:String,endTime:String):Int={
    val l_time=(tranTimeToLong(endTime)-tranTimeToLong(startTime))/1000
    l_time.asInstanceOf[Int]
  }
  def main(args: Array[String]): Unit = {
    println(getCurrentTime())

    println(getTimeDifferenceOfTwoTime(getCurrentTime(),"2020-03-11 14:35:40"))
  }
}

5、主程序

package com.hyj.main

import com.hyj.util.{MySqlConnectionPool, RedisUtils}
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.codehaus.jettison.json.JSONObject

/**
  * 统计一些用户实时步行的步数信息，包括某个用户新统计时的时间、所在地点、新增步数；
  * 将每个用户以及实时更新的步数信息保存到mysql数据库中；
  */
object kafka2sparkStreaming2mySql {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("kafka2sparkStreaming2mySql")
      .setMaster("local[1]")
    //.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    //设置流数据每批的时间间隔为2s
    val ssc = new StreamingContext(conf, Seconds(1))
    //控制日志输出级别
    ssc.sparkContext.setLogLevel("WARN") //WARN,INFO,DEBUG
    ssc.checkpoint("checkpoint")
    val topic = "topic_walkCount"
    val groupId = "t03"
    val kafkaParams = Map[String, Object](
      "bootstrap.servers" -> "192.168.230.21:6667,192.168.230.22:6667,192.168.230.23:6667",
      "key.deserializer" -> classOf[StringDeserializer],
      "value.deserializer" -> classOf[StringDeserializer],
      "group.id" -> groupId,
      "auto.offset.reset" -> "earliest", // 初次启动从最开始的位置开始消费
      "enable.auto.commit" -> (false: java.lang.Boolean) // 自动提交设置为 false
    )

    val topics = Array(topic)
    val stream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream[String, String](
      ssc,
      LocationStrategies.PreferConsistent, //均匀分发到executor
      ConsumerStrategies.Subscribe[String, String](topics, kafkaParams)
    )
    
    stream.foreachRDD(rdd => {
      // 获取每一个分区的消费的偏移量
      val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
      rdd.foreachPartition(partitions => {
        val conn = MySqlConnectionPool.getConnection
        partitions.foreach(records => {
          val record = new JSONObject(records.value())
          val user = record.getString("user")
          val countTime = record.getString("count_time")
          val walkPlace = record.getString("walk_place")
          val newWalkNum = record.getInt("new_walkNum")

          println(record.toString)

          val sql = "insert into walk_info(user,counttime,walkplace,newwalknum) values(\'" + user + "\',\'" + countTime + "\',\'"+walkPlace+"\',"+newWalkNum+")"
          println(sql)
          val stmt = conn.createStatement
          stmt.executeUpdate(sql)
        })
        MySqlConnectionPool.returnConnection(conn)
      })
      // 手动提交偏移量
      stream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
    })
    ssc.start()
    ssc.awaitTermination()
  }
}

三、出现的问题

java.sql.SQLException: Incorrect string value: '\xE6\x93\x8D\xE5\x9C\xBA...' for column walkplace

解决办法：

将创建表的sql中，类型从varchar改为nvarchar；

四、效果

mysql> select * from walk_info;
+-----------+---------------------+-----------------+------------+
| user      | counttime           | walkplace       | newwalknum |
+-----------+---------------------+-----------------+------------+
| zhangSan  | 2020-06-30 16:45:52 | 操场西门        |          1 |
| liSi      | 2020-06-30 16:45:54 | 操场西南门      |          3 |
| wangWu    | 2020-06-30 16:45:55 | 操场北门        |          3 |
| xiaoQiang | 2020-06-30 16:45:56 | 操场东南门      |          3 |
| zhangSan  | 2020-06-30 16:45:57 | 操场北门        |          4 |
| liSi      | 2020-06-30 16:45:58 | 操场西北门      |          1 |
| zhangSan  | 2020-06-30 16:51:51 | 操场西南门      |          4 |
| liSi      | 2020-06-30 16:51:53 | 操场西北门      |          3 |
| wangWu    | 2020-06-30 16:51:54 | 操场北门        |          3 |
| xiaoQiang | 2020-06-30 16:51:55 | 操场西北门      |          3 |
| zhangSan  | 2020-06-30 16:51:56 | 操场南门        |          2 |
| liSi      | 2020-06-30 16:51:57 | 操场西南门      |          3 |
| wangWu    | 2020-06-30 16:51:58 | 操场东门        |          3 |
| xiaoQiang | 2020-06-30 16:51:59 | 操场东北门      |          2 |
| zhangSan  | 2020-06-30 16:52:00 | 操场东南门      |          4 |
+-----------+---------------------+-----------------+------------+
15 rows in set (0.00 sec)

mysql>