Flink DataSet API (三)Data Sinks

原文链接： https://www.cnblogs.com/niutao/p/10548466.html

flink在批处理中常见的sink

1.基于本地集合的sink（Collection-based-sink）

2.基于文件的sink（File-based-sink）

基于本地集合的sink（Collection-based-sink）

    import org.apache.flink.api.scala.extensions._
    import org.apache.flink.api.scala._
    import org.apache.flink.streaming.api.scala.extensions._

    //初始化执行环境
    val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment

    //2.定义数据 stu(age,name,height)
    val stu: DataSet[(Int, String, Double)] = env.fromElements(
      (19, "zhangsan", 178.8),
      (17, "lisi", 168.8),
      (18, "wangwu", 184.8),
      (21, "zhaoliu", 164.8)
    )
    //3.TODO sink到标准输出
    stu.print

    //3.TODO sink到标准error输出
    stu.printToErr()

    //4.TODO sink到本地Collection
    print(stu.collect())

基于文件的sink（File-based-sink）

flink支持多种存储设备上的文件，包括本地文件，hdfs文件等。

flink支持多种文件的存储格式，包括text文件，CSV文件等。

Ø writeAsText()：TextOuputFormat - 将元素作为字符串写入行。字符串是通过调用每个元素的toString()方法获得的。

注意：不论是本地还是hdfs.若Parallelism>1将把path当成目录名称，若Parallelism=1将把path当成文件名。

1、将数据写入本地文件

    import org.apache.flink.api.scala.extensions._
    import org.apache.flink.api.scala._
    import org.apache.flink.streaming.api.scala.extensions._



    //0.注意：不论是本地还是hdfs.若Parallelism>1将把path当成目录名称，若Parallelism=1将把path当成文件名。
    val env = ExecutionEnvironment.getExecutionEnvironment
    val ds1: DataSet[(Int, String)] = env.fromCollection(Map(1 -> "spark", 2 -> "flink"))
    //1.TODO 写入到本地，文本文档,NO_OVERWRITE模式下如果文件已经存在，则报错，OVERWRITE模式下如果文件已经存在，则覆盖
    ds1.setParallelism(1).writeAsText("data/ds/", WriteMode.OVERWRITE)
    env.execute()

2、将数据写入HDFS

    //TODO writeAsText将数据写入HDFS
    val env = ExecutionEnvironment.getExecutionEnvironment
    val ds1: DataSet[Map[Int, String]] = env.fromElements(Map(1 -> "spark" , 2 -> "flink"))
    ds1.setParallelism(1).writeAsText("hdfs://hadoop01:9000/a", WriteMode.OVERWRITE)
    env.execute()

可以使用sortPartition对数据进行排序后再sink到外部系统。

    import org.apache.flink.api.scala.extensions._
    import org.apache.flink.api.scala._
    import org.apache.flink.streaming.api.scala.extensions._

    //TODO 使用sortPartition对数据进行排序后再sink到外部系统
    val env = ExecutionEnvironment.getExecutionEnvironment
    //stu(age,name,height)
    val stu: DataSet[(Int, String, Double)] = env.fromElements(
      (19, "zhangsan", 178.8),
      (17, "lisi", 168.8),
      (18, "wangwu", 184.8),
      (21, "zhaoliu", 164.8)
    )
    //1.以age从小到大升序排列(0->9)
    stu.sortPartition(0, Order.ASCENDING).print
    //2.以name从大到小降序排列(z->a)
    stu.sortPartition(1, Order.ASCENDING).print
    //3.以age升序，height降序排列
    stu.sortPartition(0, Order.ASCENDING).sortPartition(2, Order.DESCENDING).print
    //4.所有字段升序排列
    stu.sortPartition("_", Order.ASCENDING).print

    import org.apache.flink.api.scala.extensions._
    import org.apache.flink.api.scala._
    import org.apache.flink.streaming.api.scala.extensions._

    val env = ExecutionEnvironment.getExecutionEnvironment
    //5.1准备数据
    case class Student(name: String, age: Int)
    val ds1: DataSet[(Student, Double)] = env.fromElements(
      (Student("zhangsan", 18), 178.5),
      (Student("lisi", 19), 176.5),
      (Student("wangwu", 17), 168.5)
    )
    val ds2 = ds1.sortPartition("_1.age", Order.ASCENDING).setParallelism(1)
//    val ds2 = ds1.sortPartition(_._1.age, Order.ASCENDING).setParallelism(1) //和上面语句相同含义

    //5.2写入到hdfs,文本文档
    val outPath1="hdfs://hadoop01:9000/Student001.txt"
    ds2.writeAsText(outPath1, WriteMode.OVERWRITE)
    env.execute()
    //5.3写入到hdfs,CSV文档
    val outPath2="hdfs://hadoop01:9000/Student002.csv"
    ds2.writeAsCsv(outPath2, "\n", "|||",WriteMode.OVERWRITE)
    env.execute()