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 spark

import 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

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 spark

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.streaming._
import org.apache.spark.storage.StorageLevel

object 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。

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 spark

import org.apache.spark.storage.StorageLevel
import org.apache.spark.{SparkConf, SparkContext }
import org.apache. spark.sql.SQLContext
import org.apache. spark.streaming.dstream.DStream
import 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()
}
}