package com.yys.spark
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
import kafka.serializer.StringDecoder
/**
* Spark Streaming对接Kafka的方式二，工作中90%使用这种方式
*/
object KafkaDirectWordCount {
def main(args: Array[String]): Unit = {
if(args.length != 2) {
System.err.println("Usage: KafkaDirectWordCount <brokers> <topics>")
System.exit(1)
}
val Array(brokers, topics) = args
val sparkConf = new SparkConf() //.setAppName("KafkaReceiverWordCount")
//.setMaster("local[2]")
val ssc = new StreamingContext(sparkConf, Seconds(5))
val topicsSet = topics.split(",").toSet
val kafkaParams = Map[String,String]("metadata.broker.list"-> brokers)
// TODO... Spark Streaming如何对接Kafka
val messages = KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder](
ssc,kafkaParams,topicsSet
)
// TODO... 自己去测试为什么要取第二个
messages.map(_._2).flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).print()
ssc.start()
ssc.awaitTermination()
}
}
