标签:String producer ods JSON 发送 json score new Spark
题目一:以下为学生期末考试的部分数据,请按要求完成统计,格式如下
{"name":"zhangsan","sex":"m",”kemu”:”yuwen”,"score":66}
1) 创建kafka主题ods_score_topic,要求一个备份,一个分区
2) 创建生产者,往主题里添加15条以上数据
3) 创建maven项目
4) 导入sparkstreaming依赖
5) 创建sparkconf环境
6) 设置批时间5S
7) 设置日志等级味error
8) 获取kafka数据源
9) 解析json数据,返回4元组格式数据
10) 遍历元组计算每个学生的总成绩
11) 遍历元组计算每个科目的最高分
遍历元组计算每个科目的平均分
生产者
package com.lq.scala import com.alibaba.fastjson.JSON import org.apache.kafka.clients.producer.KafkaProducer import org.apache.kafka.clients.producer.ProducerRecord import java.util.Properties import scala.beans.BeanProperty
// @BeanProperty 自动生成set get
case class Stu(@BeanProperty name:String,@BeanProperty sex:String,@BeanProperty kemu:String,@BeanProperty score:Int) object KafkaProducerTest { def main(args: Array[String]): Unit = { val props = new Properties() props.put("bootstrap.servers", "hdp1:9092,hdp2:9092,hdp3:9092") props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer") props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer") val producer: KafkaProducer[String,String] = new KafkaProducer[String,String](props) //2)创建生产者,往主题里添加15条以上数据 producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(Stu("张三", "m", "yuwen", 66)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("张三","m","yingyu",77)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("张三","m","shuxue",88)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("李四","m","yuwen",80)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("李四","m","yingyu",90)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("李四","m","shuxue",100)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("王五","n","yuwen",100)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("王五","n","yingyu",80)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("王五","n","shuxue",70)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("赵六","n","yuwen",80)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("赵六","n","yingyu",80)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("赵六","n","shuxue",80)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("郑七","m","yuwen",95)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("郑七","m","yingyu",85)).toString)) producer.send(new ProducerRecord[String, String]("ods_score_topic","",JSON.toJSON(new Stu("郑七","m","shuxue",75)).toString)) producer.close() } }
消费者
package com.lq.scala import com.alibaba.fastjson.JSON import org.apache.spark.SparkConf import org.apache.spark.streaming.{Seconds, StreamingContext} import org.apache.kafka.clients.consumer.ConsumerRecord import org.apache.kafka.common.serialization.StringDeserializer import org.apache.spark.rdd.RDD import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.kafka010._ import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe object KafkaConsumerTest { def main(args: Array[String]): Unit = { //5)创建sparkconf环境 setMaster 设置线程数量 val conf = new SparkConf().setMaster("local[*]").setAppName("week1") //6)设置批时间5S val ssc = new StreamingContext(conf, Seconds(5)) //7)设置日志等级味error ssc.sparkContext.setLogLevel("ERROR") //8)获取kafka数据源 val kafkaParams = Map[String, Object]( "bootstrap.servers" -> "hdp1:9092,hdp2:9092,hdp3:9092", "key.deserializer" -> classOf[StringDeserializer], "value.deserializer" -> classOf[StringDeserializer], "group.id" -> "group1", "auto.offset.reset" -> "latest", "enable.auto.commit" -> (false: java.lang.Boolean) ) val topics = Array("ods_score_topic") val stream = KafkaUtils.createDirectStream[String, String]( ssc, PreferConsistent, Subscribe[String, String](topics, kafkaParams) ) val dataDS: DStream[(String, String, String, Int)] = stream.map(record => record.value()) //9)解析json数据,返回4元组格式数据 .map(JSON.parseObject(_, classOf[Stu])) .map(stu => ( stu.name, stu.sex, stu.kemu, stu.score )) dataDS.foreachRDD(rdd=>{ //10)遍历元组计算每个学生的总成绩 println("计算每个学生的总成绩") rdd.map(s => (s._1, s._4)).reduceByKey(_ + _).foreach(println) //11)遍历元组计算每个科目的最高分(10分) println("计算每个科目的最高分") rdd.groupBy(_._3).map(s=>( s._1,s._2.map(_._4).max )).foreach(println) //12)遍历元组计算每个科目的平均分(10分) println("计算每个科目的平均分") rdd.groupBy(_._3).map(s=>( s._1,s._2.map(_._4).sum * 1.0/s._2.size )).foreach(println) }) ssc.start() ssc.awaitTermination() } }
标签:String,producer,ods,JSON,发送,json,score,new,Spark 来源: https://www.cnblogs.com/lenny-z/p/16194965.html
本站声明: 1. iCode9 技术分享网(下文简称本站)提供的所有内容,仅供技术学习、探讨和分享; 2. 关于本站的所有留言、评论、转载及引用,纯属内容发起人的个人观点,与本站观点和立场无关; 3. 关于本站的所有言论和文字,纯属内容发起人的个人观点,与本站观点和立场无关; 4. 本站文章均是网友提供,不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属;如您发现该文章侵犯了您的权益,可联系我们第一时间进行删除; 5. 本站为非盈利性的个人网站,所有内容不会用来进行牟利,也不会利用任何形式的广告来间接获益,纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。