大数据项目实战(8) | 从 Kafka读取数据并写入到 Phoenix

╰半夏微凉° 2022-12-06 15:06 133阅读 0赞

### 目录 ###

*  从 Kafka 读取数据
 *  写入数据到 Phoenix

# 从 Kafka 读取数据 #

*  1.创建util类

import kafka.serializer.StringDecoder
    
    import org.apache.kafka.clients.consumer.ConsumerConfig
    import org.apache.spark.streaming.StreamingContext
    import org.apache.spark.streaming.dstream.DStream
    import org.apache.spark.streaming.kafka.KafkaUtils
    
    object MyKafkaUtil { 
      //从kafka读数据
      val params = Map[String,String](
        ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> "hadoop12:9092,hadoop13:9092,hadoop14:9092",
        ConsumerConfig.GROUP_ID_CONFIG->  "gmall1602"
    
      )
      def  getKafkaStream(ssc:StreamingContext,topic:String,otherTopic:String*): DStream[String] ={ 
        KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder](ssc,
          params,
          (otherTopic :+ topic).toSet).map(_._2)
      }
    }

*  2.代码实现

import org.apache.spark.SparkConf
    import org.apache.spark.streaming.{ Seconds, StreamingContext}
    import util.MyKafkaUtil
    
    object OrderApp { 
      def main(args: Array[String]): Unit = { 
        val conf = new SparkConf().setMaster("local[*]").setAppName("OrderApp")
        val ssc = new StreamingContext(conf, Seconds(3))
    
        val sourceStream = MyKafkaUtil.getKafkaStream(ssc,Constant.TOPIC_ORDER_INFO)
        sourceStream.print(1000)
    
        
        ssc.start()
        ssc.awaitTermination()
      }
    
    }

*  查看结果  
    虚拟机启动Canal，idea运行CanalClient，启动模拟数据(mysql)，再启动OrderApp  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center]

# 写入数据到 Phoenix #

*  创建OrderInfo样例类

case class OrderInfo(id: String,
                         province_id: String,
                         var consignee: String,
                         order_comment: String,
                         var consignee_tel: String,
                         order_status: String,
                         payment_way: String,
                         user_id: String,
                         img_url: String,
                         total_amount: Double,
                         expire_time: String,
                         delivery_address: String,
                         create_time: String,
                         operate_time: String,
                         tracking_no: String,
                         parent_order_id: String,
                         out_trade_no: String,
                         trade_body: String,
                         var create_date: String = null,
                         var create_hour: String = null){ 
      //1.给创建日期和创建小时赋值
      /*"payment_way":"1","delivery_address":"MkRBikAWjeWtberwgrqD","consignee":"YVIFYf","create_time":"2020-09-13 " + "08:28:53","order_comment":"SEtervbXJDPpdxdaPZEW","expire_time":"","order_status":"2","out_trade_no":"8524532922", "tracking_no":"","total_amount":"453.0","user_id":"7","img_url":"","province_id":"7","consignee_tel":"13559244734", "trade_body":"","id":"42","parent_order_id":"","operate_time":"2020-09-13 09:27:52" */
      create_date = create_time.substring(0,10)
      create_hour = create_time.substring(11,13)
    
      //2.给下单的用户名和用户电话号码，做脱敏处理
      consignee = consignee.substring(0,1) + "**"
      //13619008326 => 136****8326
      //consignee_tel = consignee_tel.substring(0,3) + "****" +consignee_tel.substring(7,11)
      consignee_tel = consignee_tel.replaceAll("(\\d{3})\\d{4}(\\d{4})","$1****$2")
    }

*  测试一下是否成功  
    启动Canal，启动模拟数据(mysql)，再启动OrderApp

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 1]

*  在gmall-common下增添下Constant增添一条数据

public final static String GMALL_ORDER_INFO = "gmall_order_info1602";

*  在 Phoenix 创建表(在SQuirreL SQL Client建表)

create table gmall_order_info1602 (
               id varchar primary key,
               province_id varchar,
               consignee varchar,
               order_comment varchar,
               consignee_tel varchar,
               order_status varchar,
               payment_way varchar,
               user_id varchar,
               img_url varchar,
               total_amount decimal, 
               expire_time varchar,
               delivery_address varchar,
               create_time varchar,
               operate_time varchar,
               tracking_no varchar,
               parent_order_id varchar,
               out_trade_no varchar,
               trade_body varchar,
               create_date varchar,
               create_hour varchar)

*  保存到phoenix中

import bean.OrderInfo
    import com.alibaba.fastjson.JSON
    import org.apache.spark.SparkConf
    import org.apache.spark.streaming.{ Seconds, StreamingContext}
    import util.MyKafkaUtil
    import org.apache.phoenix.spark._
    
    
    
    object OrderApp { 
      def main(args: Array[String]): Unit = { 
        val conf = new SparkConf().setMaster("local[*]").setAppName("OrderApp")
        val ssc = new StreamingContext(conf, Seconds(3))
        val sourceStream = MyKafkaUtil.getKafkaStream(ssc, "topic_order_info")
        //1.把读取到json字符串进行解析，封装到样例类中
        val orderInforStream = sourceStream.map(s => JSON.parseObject(s, classOf[OrderInfo]))
        //2.保存到hbase(phoenix)
        orderInforStream.foreachRDD(rdd=>{ 
          rdd.saveToPhoenix(Constant.GMALL_ORDER_INFO,
            Seq("ID", "PROVINCE_ID", "CONSIGNEE", "ORDER_COMMENT", "CONSIGNEE_TEL", "ORDER_STATUS", "PAYMENT_WAY",
              "USER_ID", "IMG_URL", "TOTAL_AMOUNT", "EXPIRE_TIME", "DELIVERY_ADDRESS", "CREATE_TIME", "OPERATE_TIME",
              "TRACKING_NO", "PARENT_ORDER_ID", "OUT_TRADE_NO", "TRADE_BODY", "CREATE_DATE", "CREATE_HOUR"),
            zkUrl = Some("hadoop12,hadoop13,hadoop14:2181"))
        })
        ssc.start()
        ssc.awaitTermination()
      }
    
    }

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 2]

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 1]: /images/20221123/3afb0b16a00d4b219259618e816b2540.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2NTQ4ODU1_size_16_color_FFFFFF_t_70_pic_center 2]: /images/20221123/479173bdf9024f14bf857f8e65915ce9.png