flink批处理中的source以及sink介绍

 

一、flink在批处理中常见的source

  flink在批处理中常见的source主要有两大类:  

    1.基于本地集合的source(Collection-based-source)   

    2.基于文件的source(File-based-source)

 

 1.基于本地集合的source

      在flink最常见的创建DataSet方式有三种。   

1.使用env.fromElements(),这种方式也支持Tuple,自定义对象等复合形式。   

2.使用env.fromCollection(),这种方式支持多种Collection的具体类型   

3.使用env.generateSequence()方法创建基于Sequence的DataSet

import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment, _}
import scala.collection.immutable.{Queue, Stack}
import scala.collection.mutable
import scala.collection.mutable.{ArrayBuffer, ListBuffer}

object DataSource001 {
  def main(args: Array[String]): Unit = {
    val env = ExecutionEnvironment.getExecutionEnvironment
    //0.用element创建DataSet(fromElements)
    val ds0: DataSet[String] = env.fromElements("spark", "flink")
    ds0.print()

    //1.用Tuple创建DataSet(fromElements)
    val ds1: DataSet[(Int, String)] = env.fromElements((1, "spark"), (2, "flink"))
    ds1.print()

    //2.用Array创建DataSet
    val ds2: DataSet[String] = env.fromCollection(Array("spark", "flink"))
    ds2.print()

    //3.用ArrayBuffer创建DataSet
    val ds3: DataSet[String] = env.fromCollection(ArrayBuffer("spark", "flink"))
    ds3.print()

    //4.用List创建DataSet
    val ds4: DataSet[String] = env.fromCollection(List("spark", "flink"))
    ds4.print()

    //5.用List创建DataSet
    val ds5: DataSet[String] = env.fromCollection(ListBuffer("spark", "flink"))
    ds5.print()

    //6.用Vector创建DataSet
    val ds6: DataSet[String] = env.fromCollection(Vector("spark", "flink"))
    ds6.print()

    //7.用Queue创建DataSet
    val ds7: DataSet[String] = env.fromCollection(Queue("spark", "flink"))
    ds7.print()

    //8.用Stack创建DataSet
    val ds8: DataSet[String] = env.fromCollection(Stack("spark", "flink"))
    ds8.print()

    //9.用Stream创建DataSet(Stream相当于lazy List,避免在中间过程中生成不必要的集合)
    val ds9: DataSet[String] = env.fromCollection(Stream("spark", "flink"))
    ds9.print()

    //10.用Seq创建DataSet
    val ds10: DataSet[String] = env.fromCollection(Seq("spark", "flink"))
    ds10.print()

    //11.用Set创建DataSet
    val ds11: DataSet[String] = env.fromCollection(Set("spark", "flink"))
    ds11.print()

    //12.用Iterable创建DataSet
    val ds12: DataSet[String] = env.fromCollection(Iterable("spark", "flink"))
    ds12.print()

    //13.用ArraySeq创建DataSet
    val ds13: DataSet[String] = env.fromCollection(mutable.ArraySeq("spark", "flink"))
    ds13.print()

    //14.用ArrayStack创建DataSet
    val ds14: DataSet[String] = env.fromCollection(mutable.ArrayStack("spark", "flink"))
    ds14.print()

    //15.用Map创建DataSet
    val ds15: DataSet[(Int, String)] = env.fromCollection(Map(1 -> "spark", 2 -> "flink"))
    ds15.print()

    //16.用Range创建DataSet
    val ds16: DataSet[Int] = env.fromCollection(Range(1, 9))
    ds16.print()

    //17.用fromElements创建DataSet
    val ds17: DataSet[Long] =  env.generateSequence(1,9)
    ds17.print()
  }
}

2.基于文件的source(File-based-source)

flink支持多种存储设备上的文件,包括本地文件,hdfs文件,alluxio文件等。
flink支持多种文件的存储格式,包括text文件,CSV文件等。
import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment,_}

object DataSource002 {
  def main(args: Array[String]): Unit = {

    val env = ExecutionEnvironment.getExecutionEnvironment
    //1.读取本地文本文件,本地文件以file://开头
    val ds1: DataSet[String] = env.readTextFile("file:///Applications/flink-1.1.3/README.txt")
    ds1.print()

    //2.读取hdfs文本文件,hdfs文件以hdfs://开头,不指定master的短URL
    val ds2: DataSet[String] = env.readTextFile("hdfs:///input/flink/README.txt")
    ds2.print()

    //3.读取hdfs CSV文件,转化为tuple
    val path = "hdfs://qingcheng11:9000/input/flink/sales.csv"
    val ds3 = env.readCsvFile[(String, Int, Int, Double)](
      filePath = path,
      lineDelimiter = "\n",
      fieldDelimiter = ",",
      lenient = false,
      ignoreFirstLine = true,
      includedFields = Array(0, 1, 2, 3))
    ds3.print()

    //4.读取hdfs CSV文件,转化为case class
    case class Sales(transactionId: String, customerId: Int, itemId: Int, amountPaid: Double)
    val ds4 = env.readCsvFile[Sales](
      filePath = path,
      lineDelimiter = "\n",
      fieldDelimiter = ",",
      lenient = false,
      ignoreFirstLine = true,
      includedFields = Array(0, 1, 2, 3),
      pojoFields = Array("transactionId", "customerId", "itemId", "amountPaid")
    )
    ds4.print()
  }
}

3.基于文件的source(遍历目录)

flink支持对一个文件目录内的所有文件,包括所有子目录中的所有文件的遍历访问方式。
import org.apache.flink.api.scala.ExecutionEnvironment
import org.apache.flink.configuration.Configuration

/**
  * 递归读取hdfs目录中的所有文件,会遍历各级子目录
  */
object DataSource003 {
  def main(args: Array[String]): Unit = {
    val env = ExecutionEnvironment.getExecutionEnvironment
    // create a configuration object
    val parameters = new Configuration
    // set the recursive enumeration parameter
    parameters.setBoolean("recursive.file.enumeration", true)
    // pass the configuration to the data source
    val ds1 = env.readTextFile("hdfs:///input/flink").withParameters(parameters)
    ds1.print()
  }
}
 
 
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