大數據開發-Spark-Streaming處理數據到mysql

前面一篇講到streamin讀取kafka數據加工處理後寫到kafka數據，大數據開發-Spark-開發Streaming處理數據 && 寫入Kafka是針對好比推薦領域，實時標籤等場景對於實時處理結果放到mysql也是一種經常使用方式，假設一些車輛調度的地理位置信息處理後寫入到mysql

1.說明

數據表以下：

create database test;
use test;
DROP TABLE IF EXISTS car_gps;
CREATE TABLE IF NOT EXISTS car_gps(
deployNum VARCHAR(30) COMMENT '調度編號',
plateNum VARCHAR(10) COMMENT '車牌號',
timeStr VARCHAR(20) COMMENT '時間戳',
lng VARCHAR(20) COMMENT '經度',
lat VARCHAR(20) COMMENT '緯度',
dbtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT '數據入庫時間',
PRIMARY KEY(deployNum, plateNum, timeStr))

2.編寫程序

首先引入mysql的驅動

<dependency>
      <groupId>mysql</groupId>
      <artifactId>mysql-connector-java</artifactId>
      <version>5.1.44</version>
  </dependency>

2.1 jdbc寫入mysql

package com.hoult.Streaming.work

import java.sql.{Connection, DriverManager, PreparedStatement}
import java.util.Properties

import com.hoult.structed.bean.BusInfo
import org.apache.spark.sql.ForeachWriter

class JdbcHelper extends ForeachWriter[BusInfo] {
  var conn: Connection = _
  var statement: PreparedStatement = _
  override def open(partitionId: Long, epochId: Long): Boolean = {
    if (conn == null) {
      conn = JdbcHelper.openConnection
    }
    true
  }

  override def process(value: BusInfo): Unit = {
    //把數據寫入mysql表中
    val arr: Array[String] = value.lglat.split("_")
    val sql = "insert into car_gps(deployNum,plateNum,timeStr,lng,lat) values(?,?,?,?,?)"
    statement = conn.prepareStatement(sql)
    statement.setString(1, value.deployNum)
    statement.setString(2, value.plateNum)
    statement.setString(3, value.timeStr)
    statement.setString(4, arr(0))
    statement.setString(5, arr(1))
    statement.executeUpdate()
  }

  override def close(errorOrNull: Throwable): Unit = {
    if (null != conn) conn.close()
    if (null != statement) statement.close()
  }
}

object JdbcHelper {
  var conn: Connection = _
  val url = "jdbc:mysql://hadoop1:3306/test?useUnicode=true&characterEncoding=utf8"
  val username = "root"
  val password = "123456"
  def openConnection: Connection = {
    if (null == conn || conn.isClosed) {
      val p = new Properties
      Class.forName("com.mysql.jdbc.Driver")
      conn = DriverManager.getConnection(url, username, password)
    }
    conn
  }
}

2.2 經過foreach來寫入mysql

package com.hoult.Streaming.work
import com.hoult.structed.bean.BusInfo
import org.apache.spark.sql.{Column, DataFrame, Dataset, SparkSession}

object KafkaToJdbc {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME", "root")
    //1 獲取sparksession
    val spark: SparkSession = SparkSession.builder()
      .master("local[*]")
      .appName(KafkaToJdbc.getClass.getName)
      .getOrCreate()
    spark.sparkContext.setLogLevel("WARN")
    import spark.implicits._
    //2 定義讀取kafka數據源
    val kafkaDf: DataFrame = spark.readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", "linux121:9092")
      .option("subscribe", "test_bus_info")
      .load()
    //3 處理數據
    val kafkaValDf: DataFrame = kafkaDf.selectExpr("CAST(value AS STRING)")
    //轉爲ds
    val kafkaDs: Dataset[String] = kafkaValDf.as[String]
    //解析出經緯度數據，寫入redis
    //封裝爲一個case class方便後續獲取指定字段的數據
    val busInfoDs: Dataset[BusInfo] = kafkaDs.map(BusInfo(_)).filter(_ != null)

    //將數據寫入MySQL表
    busInfoDs.writeStream
      .foreach(new JdbcHelper)
      .outputMode("append")
      .start()
      .awaitTermination()
  }
}

2.4 建立topic和從消費者端寫入數據

kafka-topics.sh --zookeeper linux121:2181/myKafka --create --topic test_bus_info --partitions 2 --replication-factor 1
kafka-console-producer.sh --broker-list linux121:9092 --topic test_bus_info

吳邪，小三爺，混跡於後臺，大數據，人工智能領域的小菜鳥。
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