package com.sql.it

 import org.apache.spark.sql.SQLContext

 import org.apache.spark.storage.StorageLevel

 import org.apache.spark.streaming.kafka.KafkaUtils

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

 import org.apache.spark.{SparkConf, SparkContext}

 object StreamingSQL {

   def main(args: Array[String]): Unit = {

     val conf = new SparkConf()

       .setAppName("StreamingWindowOfKafka22")

       .setMaster("local[*]")

     val sc = SparkContext.getOrCreate(conf)

     val ssc = new StreamingContext(sc, Seconds(5))

     // 当调用updateStateByKey函数API的时候，必须给定checkpoint dir

     // 路径对应的文件夹不能存在

     ssc.checkpoint("hdfs://linux-hadoop01.ibeifeng.com:8020/beifeng/spark/streaming/9421151351")

     val kafkaParams = Map(

       "group.id" -> "streaming-kafka-78912151",

       "zookeeper.connect" -> "linux-hadoop01.ibeifeng.com:2181/kafka",

       "auto.offset.reset" -> "smallest"

     )

     val topics = Map("beifeng" -> 4) // topics中value是读取数据的线程数量，所以必须大于等于1

     val dstream = KafkaUtils.createStream[String, String, kafka.serializer.StringDecoder, kafka.serializer.StringDecoder](

       ssc, // 给定SparkStreaming上下文

       kafkaParams, // 给定连接kafka的参数信息 ===> 通过Kafka HighLevelConsumerAPI连接

       topics, // 给定读取对应topic的名称以及读取数据的线程数量

       StorageLevel.MEMORY_AND_DISK_2 // 指定数据接收器接收到kafka的数据后保存的存储级别

     ).map(_._2)

     /**

       * transform：将DStream的操作转换为RDD的操作，调用该api最终只需要返回一个新的RDD即可

       */

     dstream.transform(rdd => {

       // 使用sql统计wordcoount

       val sqlContext = SQLContextSingelton.getSQLContext(rdd.sparkContext)

       import sqlContext.implicits._

       val procedRDD = rdd.filter(_.nonEmpty).flatMap(_.split(" ").map((_, 1)))

       procedRDD.toDF("word", "c").registerTempTable("tb_word")

       val resultRDD = sqlContext.sql("select word, count(c) as vc from tb_word group by word").map(row => {

         val word = row.getAs[String]("word")

         val count = row.getAs[Long]("vc")

         (word, count)

       })

       resultRDD

     }).print()

     // 启动开始处理

     ssc.start()

     ssc.awaitTermination() // 等等结束，监控一个线程的中断操作

   }

 }

 object SQLContextSingelton {

   @transient private var instance: SQLContext = _

   def getSQLContext(sc: SparkContext): SQLContext = {

     if (instance == null) {

       synchronized[SQLContext] {

         if (instance == null) {

           instance = new SQLContext(sc)

         }

         instance

       }

     }

     instance

   }

 }