练习-统计卡口通过的车辆数量（及提交到集群中的问题）

统计卡口查流量
1、统计最近15分分钟的车流量
2、每隔5分钟统计一次
3、使用事件时间
4、数据源使用kafka
5、将统计好的结果保存到mysql中

1、创建kafka生产者生产几条数据

#创建一个Topic
kafka-topics.sh --create --zookeeper master:2181,node1:2181,node2:2181 --replication-factor 1 --partitions 1 --topic cars

#生产数据
kafka-console-producer.sh --broker-list master:9092,node1:9092,node2:9092 --topic cars

数据

{"car":"皖A9A7N2","city_code":"340100","county_code":"340122","card":116696031866010,"camera_id":"01123","orientation":"西","road_id":34044594,"time":1614718101,"speed":86.24}
{"car":"皖AR3HY0","city_code":"340100","county_code":"340102","card":117350032007010,"camera_id":"00002","orientation":"南","road_id":34044594,"time":1614718202,"speed":72.23}
{"car":"皖AR3HY0","city_code":"340100","county_code":"340102","card":117350032007010,"camera_id":"00002","orientation":"南","road_id":34044594,"time":1614718303,"speed":72.23}
{"car":"皖AR3HY0","city_code":"340100","county_code":"340102","card":117350032007010,"camera_id":"00002","orientation":"南","road_id":34044594,"time":1614718604,"speed":72.23}
{"car":"皖AR3HY0","city_code":"340100","county_code":"340102","card":117350032007010,"camera_id":"00002","orientation":"南","road_id":34044594,"time":1614718905,"speed":72.23}

代码如下：

package com.wt.flink.core

import com.alibaba.fastjson.{JSON, JSONObject}
import org.apache.flink.api.common.eventtime.{SerializableTimestampAssigner, WatermarkStrategy}
import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.configuration.Configuration
import org.apache.flink.connector.kafka.source.KafkaSource
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer
import org.apache.flink.streaming.api.functions.sink.{RichSinkFunction, SinkFunction}
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.windows.TimeWindow

import java.sql.{Connection, DriverManager, PreparedStatement}
import java.time.Duration

object Demo6Cars {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment

    env.setParallelism(1)

    /**
     * 1、从kafka中读取卡口过车数据
     */

    val source: KafkaSource[String] = KafkaSource
      .builder[String]
      .setBootstrapServers("master:9092,node1:9092,node2:9092") //kafka集群broker列表
      .setTopics("cars") //指定topic
      .setGroupId("asdasdasd") //指定消费者组，一条数据在一个组内只被消费一次
      .setStartingOffsets(OffsetsInitializer.latest()) //读取数据的位置，earliest：读取所有的数据，latest：读取最新的数据
      .setValueOnlyDeserializer(new SimpleStringSchema()) //反序列的类
      .build

    //使用kafka source
    val carsDS: DataStream[String] = env.fromSource(source, WatermarkStrategy.noWatermarks(), "Kafka Source")

    /**
     * 解析json格式数据，将卡口编号和时间字段取出来
     * fastJson
     *
     */
    val cardAndTimeDS: DataStream[(Long, Long)] = carsDS.map(line => {
      //将字符串转换成json对象
      val jsonObj: JSONObject = JSON.parseObject(line)
      //使用字段名获取字段值
      //卡口编号
      val card: Long = jsonObj.getLong("card")
      //事件时间，事件时间要求时毫秒级别
      val time: Long = jsonObj.getLong("time") * 1000
      (card, time)
    })

    /**
     *
     * 设置时间字段和水位线
     *
     */
    val assDS: DataStream[(Long, Long)] = cardAndTimeDS.assignTimestampsAndWatermarks(
      WatermarkStrategy
        //设置水位线的生成策略，前移5秒
        .forBoundedOutOfOrderness(Duration.ofSeconds(5))
        //设置时间字段
        .withTimestampAssigner(new SerializableTimestampAssigner[(Long, Long)] {
          override def extractTimestamp(element: (Long, Long), recordTimestamp: Long): Long = {
            //时间字段
            element._2
          }
        })
    )

    /**
     * 统计车流量
     *
     */
    val kvDS: DataStream[(Long, Int)] = assDS.map(kv => (kv._1, 1))

    //按照卡口分组
    val keyBYDS: KeyedStream[(Long, Int), Long] = kvDS.keyBy(_._1)

    //开窗口
    val windowDS: WindowedStream[(Long, Int), Long, TimeWindow] = keyBYDS
      .window(SlidingEventTimeWindows.of(Time.minutes(15), Time.minutes(4)))

    //统计车流量
    val flowDS: DataStream[(Long, Int)] = windowDS.sum(1)

    /**
     * 将统计的结果保存到mysql中
     *
     */

    flowDS.addSink(new RichSinkFunction[(Long, Int)] {
      //插入数据
      override def invoke(value: (Long, Int), context: SinkFunction.Context): Unit = {
        println("数据写入mysql")
        stat.setLong(1, value._1)
        stat.setInt(2, value._2)

        stat.execute()
      }
      var con: Connection = _
      var stat: PreparedStatement = _

      //创建链接
      override def open(parameters: Configuration): Unit = {
        //1、加载驱动
        Class.forName("com.mysql.jdbc.Driver")
        //创建链接
        con = DriverManager.getConnection("jdbc:mysql://master:3306/bigdata17", "root", "123456")
        //编写插入数据的sql
        //replace :如果不存在插入，如果存在就替换，需要在表中设置主键
        stat = con.prepareStatement("replace into card_flow(card,flow) values(?,?)")
      }

      //关闭链接
      override def close(): Unit = {
        stat.close()
        con.close()
      }
    })
    env.execute()
  }
}

结果如下：

将代码提交到集群中运行

1、启动yarn-session.

yarn-session.sh -d

2、提交任务到session集群

# -Dyarn.application.id 每一次重启yarn-session都会变
flink run -t yarn-session -Dyarn.application.id=application_1658841626681_0003  -c com.wt.flink.core.Demo6Cars  flink-1.0-SNAPSHOT.jar

错误

错误的原因：在集群中找到kafka的依赖

# 缺少kafka的依赖
java.lang.ClassNotFoundException: org.apache.flink.connector.kafka.source.KafkaSource

# 缺少mysql驱动
java.lang.ClassNotFoundException: com.mysql.jdbc.Driver

# 缺少fastjson依赖
 java.lang.ClassNotFoundException: com.alibaba.fastjson.JSON

错误-1

错误差不多，就不一一展示了

解决的方法

将kafka的依赖上传到flink的lib目录下

将mysql的依赖上传到flink的lib目录下

# flink  链接kafka依赖
flink-sql-connector-kafka-1.15.0.jar
# mysql 驱动
mysql-connector-java-5.1.49.jar

如下：

需要重启yarn-session

# 关闭yarn-session.sh
yarn application -kill application_1658546198162_0001
# 启动yarn session
yarn-session.sh -d

此时查看结果：

标签：卡口,flink,val,Flink,Long,kafka,import,id
来源： https://www.cnblogs.com/atao-BigData/p/16523229.html