Spark Streaming整合logstash + Kafka wordCount

1、安装logstash，直接解压即可

测试logstash是否可以正常运行

bin/logstash -e 'input { stdin { } } output { stdout {codec => rubydebug } }'

只获取消息

bin/logstash -e 'input { stdin { } } output { stdout {codec => plain { format => "%{message}" } } }'

2、编写logstash配置文件
2、1在logstash目录下创建conf目录
2、2在conf目录下创建文件logstash.conf,内容如下

input {
file {
type => "logs"
path => "/home/hadoop/logs/*.log"
discover_interval => 10
start_position => "beginning" 
}
}output {
kafka {
codec => plain {
format => "%{message}"
}
topic_id => "spark"	
}
}

logstash input: https://www.elastic.co/guide/en/logstash/current/input-plugins.html
logstash output: https://www.elastic.co/guide/en/logstash/current/output-plugins.html

3、启动logstash采集数据

bin/logstash -f conf/logstash.conf

4、代码

 

package bigdata.sparkimport org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkContext, SparkConf}/*** Created by Administrator on 2017/4/28.*/
object SparkStreamDemo {def main(args: Array[String]) {val conf = new SparkConf()conf.setAppName("spark_streaming")conf.setMaster("local[*]")val sc = new SparkContext(conf)sc.setCheckpointDir("D:/checkpoints")sc.setLogLevel("ERROR")val ssc = new StreamingContext(sc, Seconds(5))val topics = Map("spark" -> 2)val lines = KafkaUtils.createStream(ssc, "m1:2181,m2:2181,m3:2181", "spark", topics).map(_._2)val ds1 = lines.flatMap(_.split(" ")).map((_, 1))val ds2 = ds1.updateStateByKey[Int]((x:Seq[Int], y:Option[Int]) => {Some(x.sum + y.getOrElse(0))})ds2.print()ssc.start()ssc.awaitTermination()}
}