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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！
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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.com
package sparkstreaming02
import org.apache.spark.{SparkConf, SparkContext}
import scala.collection.mutable.ListBuffer
object 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)
}
}package sparkstreaming02
import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
import scala.collection.mutable.ListBuffer
object 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()
}
}
、
connect 在Driver端创建，record在executor，发过去序列化错误
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)
}package sparkstreaming02
import java.sql.DriverManager
import org.apache.spark.SparkConf
import 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")
}
}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。这里是创新互联，小编将为大家推送更多相关知识点的文章，欢迎关注！
