Spark Streaming基于状态、窗口的实时数据流

与前两篇Spark Streaming的实时处理案例，原理基本一致，这里不再演示。最大的不同是，这两种方式必须设置checkpoint。
(注：模拟器前面已给出)

基于状态的实时数据分析

使用updateStateByKey(func)步骤：

步骤1. 定义状态state
步骤2. 定义状态更新函数func
步骤3. 对DStream进行checkpoint
步骤4. 以func为参数，在DStream上调用updateStateByKey

package sparkimport org.apache.spark.{SparkContext, SparkConf}import org.apache.spark.streaming.{Milliseconds,Seconds, StreamingContext}import org.apache.log4j.{Level, Logger}object StatefulWordCount {  def main(args:Array[String]): Unit ={    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)//定义状态函数    val updateFunc=(values: Seq[Int],state:Option[Int])=>{      val currentCount=values.foldLeft(0)(_+_)      val previousCount=state.getOrElse(0)      Some(currentCount+previousCount)    }//创建ssc    val conf=new SparkConf().      setAppName("StatefulWordCount").      //setMaster("spark://192.168.71.129:7077")      setMaster("local[2]")    val sc=new SparkContext(conf)    val ssc=new StreamingContext(sc, Seconds(5))//基于状态的操作需要进行checkpoint，输出路径如果是在HDFS上的必须原先不存在    //ssc.checkpoint("hdfs://node01:9000/streamingdata/StatefulWordCountlog")    ssc.checkpoint(".") //当前路径//处理数据流    val lines=ssc.socketTextStream(args(0),args(1).toInt)/*hello，hello,hello,spark,spark,!!!!!!!!!!!!!hello，hbase,hello,spark,hbase, wordcounts={   RDD1={(hello,1),(hello,1),(hello,1),(spark,1),(spark,1)},        RDD2={(hello,1),(hbase,1),(hello,1),(spark,1),(hbase,1)}....}RDD1={(hello,3),(spark,2)}RDD2={(hello,5),(spark,3),(hbase,2)}*/    val words=lines.flatMap(_.split(","))    val wordcounts=words.map(x=>(x,1))    val stateDstream=wordcounts.updateStateByKey[Int](updateFunc)    stateDstream.print()    ssc.start()    ssc.awaitTermination()  }}

分别执行模拟器和解析器。
补充一点：如果是本地执行解析器，需在IDE中手动给定args(0)和args(1)参数，IntelliJ参数添加路径：Run -> Edit Configurations -> + ->Application

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基于窗口的实时数据分析

构建模拟器，模拟网络环境下的数据流；编辑Spark Streaming应用程序，在node01提交以集群模式运行，获取node02上端口9999中的文本数据流，每隔10s统计过去30s内数据流中各单词累计出现次数。

步骤1. 定义聚合函数func
步骤2. 对DStream进行checkpoint
步骤3. 确定窗口长度、滑动时间间隔
步骤4. 在DStream上调用window相关操作

package sparkimport org.apache.log4j.{Level, Logger}import org.apache.spark.{SparkContext, SparkConf}import org.apache.spark.streaming._import org.apache.spark.storage.StorageLevelobject WindowWordCount {  def main(args: Array[String]) = {    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)    val conf = new SparkConf().               setAppName("WindowWordCount").               setMaster("spark://192.168.71.129:7077")    val sc = new SparkContext(conf)    val ssc = new StreamingContext(sc, Seconds(5))    //设置checkpoint路径    ssc.checkpoint("hdfs://node01:9000/WindowWordCountlog")    val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_ONLY_SER)    val words = lines.flatMap(_.split(","))    val wordcounts = words.      map(x => (x, 1)).      reduceByKeyAndWindow((a: Int, b: Int) => a+b, Seconds(args(2).toInt), Seconds(args(3).toInt))    wordcounts.print()    ssc.start()    ssc.awaitTermination()  }}

这里的解析器在spark下提交时，执行以下命令：

bin/spark-submit ~/WindowWordCount.jar node02 9999 30 10

30和10的含义为：每隔10s统计过去30s内的数据流，时间单位与执行模拟器时是不一样的。

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SQL语句在Spark Streaming上的简单应用

数据源：

Tom 3200
Jerry 21.5
Tom 123.7
Lucy 259
Ben 125
John 546
John 125.8

package sparkimport org.apache.spark.storage.StorageLevelimport org.apache.spark.{SparkConf, SparkContext }import org.apache. spark.sql.SQLContextimport org.apache. spark.streaming.dstream.DStreamimport org.apache.spark.streaming.{Seconds ,StreamingContext }/*使用lazy加载的单件模式(singleton pattem)的方式来构建SQLContext实例,可以避免在foreachRDD中重复构建*/object SQLContextSingleton {  @transient private var instance: SQLContext = null  //lazy方式实例化  def getInstance (sparkContext:SparkContext):SQLContext= synchronized{    if (instance == null){      instance = new SQLContext(sparkContext)    }    instance  //用以返回SQLContext的对象  }}//样本类，用于构建RDD对应的DataFrame实例//可以根据实际的数据格式，给出对应的解析样本类case class Item(name:String,amount:Double)object Streaming_SQL{  def main( args : Array [ String] ){    //减少控制台输出信息    import org. apache. log4j.  { Level, Logger }    Logger. getLogger("org.apache.spark" ).setLevel(Level.WARN)    Logger. getLogger("org.apache.spark.sql").setLevel(Level.WARN)    Logger. getLogger("org.apache.spark.streaming").setLevel(Level.WARN)    //创建StreamingContext实例    val conf = new SparkConf( ).setAppName("Streaming_SQL").setMaster("spark://192.168.71.129:7077")//.setMaster("local[2]")    val ssc=new StreamingContext(conf,Seconds(10))    val words=ssc.socketTextStream("192.168.71.129",9999,StorageLevel.MEMORY_AND_DISK)    words.foreachRDD { rdd =>      //将每个rdd转换为DataFrame即wordsDF      val sqlContext = SQLContextSingleton.getInstance(rdd.sparkContext)      import sqlContext.implicits._      val wordsDF = rdd.map(x => x.split(" ")).map(x => Item(x(0),x(1).toDouble)).toDF()      //将每个wordsDF注册成临时表tb_wordsDF      wordsDF.registerTempTable("tb_wordsDF")      //对tb_wordsDF执行SQL查询      sqlContext.sql("select name, sum(amount) from tb_wordsDF group by name").show()    }    ssc.start()    ssc.awaitTermination()  }}