DataStream编程模型之数据源、数据转换、数据输出

Flink之DataStream数据源、数据转换、数据输出（scala）

0.前言--数据源

在进行数据转换之前，需要进行数据读取。

数据读取分为4大部分：

（1）内置数据源；

又分为文件数据源；

socket数据源；

集合数据源三类

（2）Kafka数据源

第二个参数用到的SimpleStringSchema对象是一个内置的DeserializationSchema对象，可以把字节数据反序列化程一个String对象。

另外，FlinkKafkaConsumer开始读取Kafka消息时，可以配置他的读起始位置，有如下四种。

scala 复制代码

import java.util.Properties
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.streaming.api.windowing.time.Time
object KafkaWordCount {
  def main(args: Array[String]): Unit = {
 
    val kafkaProps = new Properties()
    //Kafka的一些属性
    kafkaProps.setProperty("bootstrap.servers", "localhost:9092")
    //所在的消费组
    kafkaProps.setProperty("group.id", "group1")
    
    //获取当前的执行环境
    val evn = StreamExecutionEnvironment.getExecutionEnvironment
//创建Kafka的消费者，wordsendertest是要消费的Topic
    val kafkaSource = new FlinkKafkaConsumer[String]("wordsendertest",new SimpleStringSchema,kafkaProps)
    //设置从最新的offset开始消费
    kafkaSource.setStartFromLatest()
    //自动提交offset
kafkaSource.setCommitOffsetsOnCheckpoints(true)
    //绑定数据源
    val stream = evn.addSource(kafkaSource)
 
    //设置转换操作逻辑
    val text = stream.flatMap{ _.toLowerCase().split("\\W+")filter{ _.nonEmpty} }
      .map{(_,1)}
      .keyBy(0)
      .timeWindow(Time.seconds(5))
      .sum(1)
 
      //打印输出
      text.print()
 
      //程序触发执行
      evn.execute("Kafka Word Count")
  }
}

（3）HDFS数据源

（4）自定义数据源

一个例子：

scala 复制代码

import java.util.Calendar
import org.apache.flink.streaming.api.functions.source.RichSourceFunction
import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import scala.util.Random
 
case class StockPrice(stockId:String,timeStamp:Long,price:Double)
object StockPriceStreaming {
  def main(args: Array[String]) { 
    //设置执行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
 
//设置程序并行度    
env.setParallelism(1)    
//股票价格数据流
    val stockPriceStream: DataStream[StockPrice] = env
      //该数据流由StockPriceSource类随机生成
      .addSource(new StockPriceSource)
 
    //打印结果
    stockPriceStream.print()
 
    //程序触发执行
    env.execute("stock price streaming")
  }
 class StockPriceSource extends RichSourceFunction[StockPrice]{ 
    var isRunning: Boolean = true
    val rand = new Random()
    //初始化股票价格
    var priceList: List[Double] = List(10.0d, 20.0d, 30.0d, 40.0d, 50.0d)
    var stockId = 0
    var curPrice = 0.0d
override def run(srcCtx: SourceContext[StockPrice]): Unit = {
      while (isRunning) {
        //每次从列表中随机选择一只股票
        stockId = rand.nextInt(priceList.size)
        val curPrice =  priceList(stockId) + rand.nextGaussian() * 0.05
        priceList = priceList.updated(stockId, curPrice)
        val curTime = Calendar.getInstance.getTimeInMillis
        //将数据源收集写入SourceContext
        srcCtx.collect(StockPrice("stock_" + stockId.toString, curTime, curPrice))
        Thread.sleep(rand.nextInt(10))
      }
} 
    override def cancel(): Unit = {
      isRunning = false
    }
  }
}

1.数据转换之map操作

1.数据转换算子的四种类型

基于单条记录：fliter、map

基于窗口：window

合并多条数据流：union，join，connect

拆分多条数据流：split

2.map(func)操作将一个DataStream中的每个元素传递到函数func中，并将结果返回为一个新的DataStream。输出的数据流DataStream[OUT]类型可能和输入的数据流DataStream[IN]不同

理解：一一对应的关系，一个x 得到一个y

scala 复制代码

val dataStream = env.fromElements(1,2,3,4,5)
val mapStream = dataStream.map(x=>x+10)

3.演示代码

scala 复制代码

import org.apache.flink.api.common.functions.RichMapFunction
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
 
case class StockPrice(stockId:String,timeStamp:Long,price:Double) 
object MapFunctionTest {
  def main(args: Array[String]): Unit = {
 
    //设定执行环境
   val env = StreamExecutionEnvironment.getExecutionEnvironment
 
    //设定程序并行度
   env.setParallelism(1)

   //创建数据源
   val dataStream: DataStream[Int] = env.fromElements(1, 2, 3, 4, 5, 6, 7)
 
    //设置转换操作逻辑
    val richFunctionDataStream = dataStream.map {new MyMapFunction()}

 //打印输出
    richFunctionDataStream.print()
 
//程序触发执行
    env.execute("MapFunctionTest")
  }
 
  //自定义函数，继承RichMapFunction
  class MyMapFunction extends RichMapFunction[Int, String] {
    override def map(input: Int): String =
      ("Input : " + input.toString + ", Output : " + (input * 3).toString)
  }
}

2.数据转换之flatMap操作

1.flatMap和map相似，每个输入元素都可以映射到0或多个输出结果。

scala 复制代码

val dataStream = env.fromElements("Hadoop is good","Flink is fast","Flink is better")
val flatMapStream = dataStream.flatMap(line => line.split(" "))

可以理解为flatMap比map多了flat操作。如图。map是将输入数据映射成数组，flat是将数据拍扁，成为一个个元素。把元素映射成了多个。

2.代码演示

scala 复制代码

import org.apache.flink.api.common.functions.FlatMapFunction
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.util.Collector
 
case class StockPrice(stockId:String,timeStamp:Long,price:Double) 
object FlatMapFunctionTest {
  def main(args: Array[String]): Unit = {
 
    //设定执行环境
val env = StreamExecutionEnvironment.getExecutionEnvironment
 
    //设定程序并行度
env.setParallelism(1)
//设置数据源
val dataStream: DataStream[String] = 
      env.fromElements("Hello Spark", "Flink is excellent") 
    //针对数据集的转换操作逻辑
val result = dataStream.flatMap(new WordSplitFlatMap(15)) 
    //打印输出
result.print() 
//程序触发执行
    env.execute("FlatMapFunctionTest")
  } 
  //使用FlatMapFunction实现过滤逻辑，只对字符串长度大于threshold的内容进行切词
  class WordSplitFlatMap(threshold: Int) extends FlatMapFunction[String, String] {
    override def flatMap(value: String, out: Collector[String]): Unit = {
      if (value.size > threshold) {
        value.split(" ").foreach(out.collect)
      }
    }
  }
}

预计输出：

Flink
is
excellent

这里只对字符长度超过15的做切割。threshold是阈值，少于15的不做切割。

3.数据转换之filter和keyBy操作

1.filter(func)操作会筛选出满足函数func的元素，并返回一个新的数据集

2.代码举例

scala 复制代码

val dataStream = env.fromElements("Hadoop is good","Flink is fast","Flink is better")
val filterStream = dataStream.filter(line => line.contains("Flink"))

如图所示

3.keyBy（注意方法里k小写B大写）：将相同Key的数据放置在相同的分区中。

keyBy算子根据元素的形状对数据进行分组，相同形状的元素被分到了一起，可被后续算子统一处理

比如在词频统计时：

				hello flink 
				hello hadoop
				hello zhangsan

这里词频（hello,1）,（hello,1），（hello,1）统计出来之后，通过keyBy，就可以聚合，放在了相同的分区里进行统一计算。

通过聚合函数后又可以吧KeyedStream转换成DataStream。

4.在使用keyBy算子时，需要向keyBy算子传递一个参数, 可使用数字位置来指定Key

比如刚才词频统计时，keyBy（0）就是hello这个单词。

scala 复制代码

val dataStream: DataStream[(Int, Double)] =
    env.fromElements((1, 2.0), (2, 1.7), (1, 4.9), (3, 8.5), (3, 11.2))
//使用数字位置定义Key 按照第一个字段进行分组
val keyedStream = dataStream.keyBy(0)

这里keyby 是第一个字段1或者2或者3分组（分类）。

5.keyBy代码举例：

scala 复制代码

import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
 
//声明一个样例类，包含三个字段：股票ID、交易时间、交易价格
case class StockPrice(stockId:String,timeStamp:Long,price:Double)
 
object KeyByTest{
  def main(args: Array[String]): Unit = {
 
    //获取执行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
 
    //设置程序并行度
    env.setParallelism(1)
//创建数据源
    val stockList = List(
      StockPrice("stock_4",1602031562148L,43.4D),
      StockPrice("stock_1",1602031562148L,22.9D),
      StockPrice("stock_0",1602031562153L,8.2D),
      StockPrice("stock_3",1602031562153L,42.1D),
      StockPrice("stock_2",1602031562153L,29.2D),
      StockPrice("stock_0",1602031562159L,8.1D),
      StockPrice("stock_4",1602031562159L,43.7D),
      StockPrice("stock_4",1602031562169L,43.5D)
    )
    val dataStream = env.fromCollection(stockList) 
    //设定转换操作逻辑
    val keyedStream = dataStream.keyBy("stockId") 
    //打印输出
    keyedStream.print() 
    //程序触发执行
    env.execute("KeyByTest")
  }
}

这里看起来没什么变换，因为没进行聚合操作，所以什么变化都没有，原样输出。

我加上聚合函数，看起来就有变化了。

scala 复制代码

//简写上面的代码 加上聚合函数
    val keyedStream = dataStream.keyBy("stockId")
    val aggre = keyedStream.sum(2) //这里相加的是价格price（第三个字段）

   // keyedStream.print()
    aggre.print()//聚合后打印

结果

对比上面哪里变化了呢？

stcok_id顺序，4-1-0-3-2-0(这里之前也有0,就会加上之前的0,变为16.299，后面的4也在累加前面的price了

4.数据转换之reduce操作和聚合操作

1.reduce：reduce算子将输入的KeyedStream通过传入的用户自定义函数滚动地进行数据聚合处理，处理以后得到一个新的DataStream，如下实例

scala 复制代码

import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
 
//声明一个样例类，包含三个字段：股票ID、交易时间、交易价格
case class StockPrice(stockId:String,timeStamp:Long,price:Double)
 
object ReduceTest{
  def main(args: Array[String]): Unit = {
 
    //获取执行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
 
    //设置程序并行度
    env.setParallelism(1)
    //创建数据源
    val stockList = List(
      StockPrice("stock_4",1602031562148L,43.4D),
      StockPrice("stock_1",1602031562148L,22.9D),
      StockPrice("stock_0",1602031562153L,8.2D),
      StockPrice("stock_3",1602031562153L,42.1D),
      StockPrice("stock_2",1602031562153L,29.2D),
      StockPrice("stock_0",1602031562159L,8.1D),
      StockPrice("stock_4",1602031562159L,43.7D),
      StockPrice("stock_4",1602031562169L,43.5D)
    )
    val dataStream = env.fromCollection(stockList)

    //设定转换操作逻辑
    val keyedStream = dataStream.keyBy("stockId")
    val reduceStream = keyedStream
      .reduce((t1,t2)=>StockPrice(t1.stockId,t1.timeStamp,t1.price+t2.price))
 
    //打印输出
    reduceStream.print()
 
    //程序触发执行
    env.execute("ReduceTest")
  }
}

reduce结果和上面的一样，就是累加

2.flink也支持自定义的reduce函数

scala 复制代码

import org.apache.flink.api.common.functions.ReduceFunction
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
 
//声明一个样例类，包含三个字段：股票ID，交易时间，交易价格
case class StockPrice(stockId:String,timeStamp:Long,price:Double)
 
object MyReduceFunctionTest{
  def main(args: Array[String]): Unit = {
 
    //获取执行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
 
    //设置程序并行度
    env.setParallelism(1)

    //创建数据源
    val stockList = List(
      StockPrice("stock_4",1602031562148L,43.4D),
      StockPrice("stock_1",1602031562148L,22.9D),
      StockPrice("stock_0",1602031562153L,8.2D),
      StockPrice("stock_3",1602031562153L,42.1D),
      StockPrice("stock_2",1602031562153L,29.2D),
      StockPrice("stock_0",1602031562159L,8.1D),
      StockPrice("stock_4",1602031562159L,43.7D),
      StockPrice("stock_4",1602031562169L,43.5D)
    )
    val dataStream = env.fromCollection(stockList) 


//设定转换操作逻辑
    val keyedStream = dataStream.keyBy("stockId")
    val reduceStream = keyedStream.reduce(new MyReduceFunction)
 
    //打印输出
    reduceStream.print()
 
    //程序触发执行
    env.execute("MyReduceFunctionTest")
  }
  class MyReduceFunction extends ReduceFunction[StockPrice] {
    override def reduce(t1: StockPrice,t2:StockPrice):StockPrice = {
      StockPrice(t1.stockId,t1.timeStamp,t1.price+t2.price)
    }
  }
}

主要不同的就是创建了MyReduceFunction （）.

3.聚合算子

和excel一样。

代码举例：

scala 复制代码

import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
 
//声明一个样例类，包含三个字段：股票ID、交易时间、交易价格
case class StockPrice(stockId:String,timeStamp:Long,price:Double) 
object AggregationTest{
  def main(args: Array[String]): Unit = {
 
    //获取执行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
 
    //设置程序并行度
    env.setParallelism(1)    
//创建数据源
    val stockList = List(
      StockPrice("stock_4",1602031562148L,43.4D),
      StockPrice("stock_1",1602031562148L,22.9D),
      StockPrice("stock_0",1602031562153L,8.2D),
      StockPrice("stock_3",1602031562153L,42.1D),
      StockPrice("stock_2",1602031562153L,29.2D),
      StockPrice("stock_0",1602031562159L,8.1D),
      StockPrice("stock_4",1602031562159L,43.7D),
      StockPrice("stock_4",1602031562169L,43.5D)
    )
    val dataStream = env.fromCollection(stockList)
 
    //设定转换操作逻辑
    val keyedStream = dataStream.keyBy("stockId")
    val aggregationStream = keyedStream.sum(2)  //区别在这里   sum聚合 2表示第三个字段
  
    //打印输出
    aggregationStream.print()
 
    //执行操作
    env.execute(" AggregationTest")
  }
}

运行结果

5.数据输出

1.基本数据输出包括：文件输出，客户端输出，socket网络端口输出。

文件输出具体代码

scala 复制代码

val dataStream = env.fromElements("hadoop","spark","flink")
//文件输出
dataStream.writeAsText("file:///home/hadoop/output.txt")
//hdfs输出

//把数据写入HDFS
dataStream.writeAsText("hdfs://localhost:9000/output.txt") 

//通过writeToSocket方法将DataStream数据集输出到指定socket端口
dataStream.writeToSocket(outputHost,outputPort,new SimpleStringSchema())

2.输出到kafka

代码举例：

scala 复制代码

import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer
 
object SinkKafkaTest{
  def main(args: Array[String]): Unit = {
 
    //获取执行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    
//加载或创建数据源
    val dataStream = env.fromElements("hadoop","spark","flink")
    //把数据输出到Kafka
dataStream.addSink(new FlinkKafkaProducer [String]("localhost:9092", "sinkKafka", new SimpleStringSchema()))
    
//程序触发执行
    env.execute()
  }
}