这篇文章主要讲解了“SparkStreaming的实现和使用方法”，文中的讲解内容简单清晰，易于学习与理解，下面请大家跟着小编的思路慢慢深入，一起来研究和学习“SparkStreaming的实现和使用方法”吧！
一.DStream 整合RDD
1.官网算子
2.使用案例
生产中使用多的是一个文件中有很多域名，另一个中是黑名单，要进行剔除数据一：日志信息 DStream domain,traffic xinlang.com xinlang.com baidu.com数据二：已有的文件 黑名单 RDD domain baidu.com3.RDD实现上述需求
package sparkstreaming02import org.apache.spark.{SparkConf, SparkContext}import scala.collection.mutable.ListBufferobject Demo1 { def main(args: Array[String]): Unit = { val conf = new SparkConf().setAppName("Demo1").setMaster("local[2]") val sc = new SparkContext(conf) val input1 = new ListBuffer[(String,Long)] input1.append(("www.xinlang.com", 8888)) input1.append(("www.xinalng.com", 9999)) input1.append(("www.baidu.com", 7777)) val data1 = sc.parallelize(input1) //进行join一定要是key,value形式的 val input2 = new ListBuffer[(String,Boolean)] input2.append(("www.baidu.com",true)) val data2 = sc.parallelize(input2) data1.leftOuterJoin(data2) .filter(x => { x._2._2.getOrElse(false) != true }).map(x => (x._1,x._2._1)) .collect().foreach(println) }}4.SparkStreaming实现
package sparkstreaming02import org.apache.spark.SparkConfimport org.apache.spark.streaming.{Seconds, StreamingContext}import scala.collection.mutable.ListBufferobject Streaming { def main(args: Array[String]): Unit = { val conf = new SparkConf().setAppName("Streaming").setMaster("local[2]") val ssc = new StreamingContext(conf,Seconds(10)) val lines = ssc.socketTextStream("s201",9999) // 数据二： rdd val input2 = new ListBuffer[(String,Boolean)] input2.append(("www.baidu.com",true)) val data2 = ssc.sparkContext.parallelize(input2) lines.map(x=>(x.split(",")(0), x)).transform( rdd => { rdd.leftOuterJoin(data2) .filter(x => { x._2._2.getOrElse(false) != true //注意 join之后过滤 }).map(x => (x._1,x._2._1)) } ).print() ssc.start() ssc.awaitTermination() }}二.SparkStreaming插入外部数据源
1.插入外部数据源用的，但是使用这个有几个坑
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2.错误一官网例子
3.原因
connect 在Driver端创建，record在executor，发过去序列化错误
4.解决
解决：第一种把connect放到executor端这样弊端是每条记录会生成一个connect太耗费资源 words.foreachRDD { rdd => rdd.foreach { record => val connection = createConnection() // executed at the driver val word = record._1 val count = record._2.toInt val sql = s"insert into wc (wc,count) values($word,$count)" connection.createStatement().execute(sql) }5.最终解决办法
package sparkstreaming02import java.sql.DriverManagerimport org.apache.spark.SparkConfimport org.apache.spark.streaming.{Seconds, StreamingContext}object MysqlStreaming { def main(args: Array[String]): Unit = { val conf = new SparkConf().setMaster("local[2]").setAppName("MysqlStreaming") val ssc = new StreamingContext(conf,Seconds(1)) val lines = ssc.socketTextStream("s201",9999) val words = lines.flatMap(x => x.split(",")).map((_,1)).reduceByKey(_+_)// words.foreachRDD { rdd =>// val connection = createConnection() // executed at the driver// rdd.foreach { record =>// val word = record._1// val count = record._2// val sql = s"insert into wc (word,count) values($word,$count)"// connection.createStatement().execute(sql)// }// }// words.foreachRDD { rdd =>// rdd.foreach { record =>// val connection = createConnection() // executed at the driver// val word = record._1// val count = record._2.toInt// val sql = s"insert into wc (wc,count) values($word,$count)"// connection.createStatement().execute(sql)// }// } //最终的写法 words.foreachRDD { rdd => rdd.foreachPartition { partitionOfRecords => val connection = createConnection() partitionOfRecords.foreach( record =>{ val word = record._1 val count = record._2 val sql = s"insert into wc (wc,count) values('$word',$count)" connection.createStatement().execute(sql)} ) } } ssc.start() ssc.awaitTermination() } def createConnection() = { Class.forName("com.mysql.cj.jdbc.Driver") DriverManager.getConnection("jdbc:mysql://localhost:3306/hive?serverTimezone=UTC&useSSL=false","root","123456") }}6.出现问题
错误，插入数据库时，你要插入字符串要用''例如：val sql = s"insert into wc (wc,count) values($word,$count)"word是字符串，你要不加双引号就报这个错误正确val sql = s"insert into wc (wc,count) values('$word',$count)"
感谢各位的阅读，以上就是“SparkStreaming的实现和使用方法”的内容了，经过本文的学习后，相信大家对SparkStreaming的实现和使用方法这一问题有了更深刻的体会，具体使用情况还需要大家实践验证。这里是编程网，小编将为大家推送更多相关知识点的文章，欢迎关注！
