实现：

首先基于topic，创建出kafka的DStream流

val sparkConf = new SparkConf().setAppName(appParams.appName)

val sc = new SparkContext(sparkConf)

val streamingContext = new StreamingContext(sc, Seconds(appParams.batchProcInterval))

val kafkaParams = Map[String, String](“metadata.broker.list” -> appParams.brokers)

val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](streamingContext, kafkaParams, Set[String](appParams.messageInTopic))

创建时间窗：

val windows = messages.map(_._2).window(Seconds(appParams.windownTime), Seconds(appParams.windownTime))

针对每个时间窗做计算

windows.foreachRDD { rdd =>

……

}

每个时间窗内部的处理：

创建case class

case class Record(channelid: String, starttime: Long)

创建sqlContext

val sqlContext = SQLContext.getOrCreate(rdd.sparkContext)

import sqlContext.implicits._

将当前消息流转换成DataFrame：

val df = rdd.map(_.split(“\\|”)).map(line => Record(line(5), line(2).toLong)).toDF()

注册成一张表：

df.registerTempTable(“UserPlayChannel”)

读取parquet数据，注册成另一张表：

val paraquetFile = sqlContext.read.parquet(filePath)

paraquetFile.registerTempTable(“ProgramMetaData”)

现在有了两张表，关联查询只需要写好sql语句就可以了，样例：

select b.programid , count(b.programid) as count from UserPlayChannel a join ProgramMetaData b on a.channelid = b.channelid and a.starttime >= b.starttime and a.starttime <= b.endtime group by b.programid order by count DESC

代码执行：

val hotProgramList = sqlContext.sql(“select b.programid , count(b.programid) as count from UserPlayChannel a join ProgramMetaData b on a.channelid = b.channelid and a.starttime >= b.starttime and a.starttime <= b.endtime group by b.programid order by count DESC”)

现在hotProgramList就是关联查询出的结果。