【云星数据---Apache Flink实战系列(精品版)】：Apache Flink高级特性与高级应用013-Flink在批处理中常见的sink和source001

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

执行程序

package code.book.batch.sinksource.scalaimport org.apache.flink.api.scala.{DataSet, ExecutionEnvironment, _}import scala.collection.immutable.{Queue, Stack}import scala.collection.mutableimport 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。

执行程序

package code.book.batch.sinksource.scalaimport 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。

执行程序

package code.book.batch.sinksource.scalaimport org.apache.flink.api.scala.ExecutionEnvironmentimport 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()  }}