spark-stream简单使用案例

>spark-stream检测liunx nc模式下的简单操作

 加入的jar包

 <dependency>
         <groupId>org.apache.spark</groupId>
         <artifactId>spark-streaming_2.11</artifactId>
        <version>${spark.version}</version>    //2.1.1版本
</dependency>

package SparkStream

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}

object streamDome {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[2]").setAppName("streamDome")
    val stream =new StreamingContext(conf,Seconds(5))
    val text = stream.socketTextStream("192.168.188.131",6666)
    text.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).print()
    text.print()
    stream.start()
    stream.awaitTermination() //等待终止
  }
}

 >spark-streaming-kafka的操作

依赖包:

 <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
            <version>${spark.version}</version> //2.1.1       
 </dependency>

 >Dstream

代码:

package SparkStream

import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

object StreamKafka {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[2]").setAppName("kafka")
    val stream = new StreamingContext(conf,Seconds(5))
    stream.checkpoint("D://stkafka")  //采集日志存储信息,容错性
    val zk = "192.168.188.130:2181,192.168.188.131:2181,192.168.188.132:2181"//zekeeper地址
    val topic = Map("test"->3); //test为kafka的主题名称,可以是多个同时采集
    val groupId = "spark_kafka" //名称任意
    val text = KafkaUtils.createStream(stream,zk,groupId,topic)
    text.print()
    stream.start()
    stream.awaitTermination()
    stream.stop()
  }
}

  >DirectStream

package SparkStream

import kafka.serializer.StringDecoder
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

object StreamKafkaDirectStream {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[2]").setAppName("kafkaRedict")
    val stream = new StreamingContext(conf,Seconds(10))
    val topic = Set("test")
    val brokers = "192.168.188.130:9092,192.168.188.131:9092,192.168.188.132:9092"
    val kafkaParams = Map[String,String]("metadata.broker.list" -> brokers,
      "serializer.class" -> "kafka.serializer.StringDecoder")
    val text = KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder](stream,kafkaParams,topic)
    text.print()
    stream.start()
    stream.awaitTermination()
    stream.stop()
  }
}