这篇文章给大家介绍一下什么是Apache Flink 中Flink DataSet编程，内容非常详细，感兴趣的小伙伴们可以参考借鉴，希望对大家能有所帮助。
Flink中DataSet编程是非常常规的编程，只需要实现他的数据集的转换（例如filtering, mapping, joining, grouping）。这个数据集最初是通过数据源创建（例如读取文件、本地数据集加载本地集合），转换的结果通过sink返回到本地（或者分布式）的文件系统或者终端。Flink程序可以运行在各种环境中例如单机，或者嵌入其他程序中。执行过程可以在本地JVM中或者集群中。
Source ===> Flink（transformation）===> Sink
readTextFile(path) / TextInputFormat - Reads files line wise and returns them as Strings.
readTextFileWithValue(path) / TextValueInputFormat - Reads files line wise and returns them as StringValues. StringValues are mutable strings.
readCsvFile(path) / CsvInputFormat - Parses files of comma (or another char) delimited fields. Returns a DataSet of tuples or POJOs. Supports the basic java types and their Value counterparts as field types.
readFileOfPrimitives(path, Class) / PrimitiveInputFormat - Parses files of new-line (or another char sequence) delimited primitive data types such as String or Integer.
readFileOfPrimitives(path, delimiter, Class) / PrimitiveInputFormat - Parses files of new-line (or another char sequence) delimited primitive data types such as String or Integer using the given delimiter.
fromCollection(Collection)
fromCollection(Iterator, Class)
fromElements(T ...)
fromParallelCollection(SplittableIterator, Class)
generateSequence(from, to)
基于集合的数据源往往用来在开发环境中或者程序员学习中，可以随意造我们所需要的数据，因为方式简单。下面从java和scala两种方式来实现使用集合作为数据源。数据源是简单的1到10
import org.apache.flink.api.java.ExecutionEnvironment;
import java.util.ArrayList;
import java.util.List;
public class JavaDataSetSourceApp {
public static void main(String[] args) throws Exception {
ExecutionEnvironment executionEnvironment = ExecutionEnvironment.getExecutionEnvironment();
fromCollection(executionEnvironment);
}
public static void fromCollection(ExecutionEnvironment env) throws Exception {
List<Integer> list = new ArrayList<Integer>();
for (int i = 1; i <= 10; i++) {
list.add(i);
}
env.fromCollection(list).print();
}
}import org.apache.flink.api.scala.ExecutionEnvironment
object DataSetSourceApp {
def main(args: Array[String]): Unit = {
val env = ExecutionEnvironment.getExecutionEnvironment
fromCollection(env)
}
def fromCollection(env: ExecutionEnvironment): Unit = {
import org.apache.flink.api.scala._
val data = 1 to 10
env.fromCollection(data).print()
}
}在本地文件夹：E:\test\input，下面有一个hello.txt，内容如下：
hello world welcome hello welcome
def main(args: Array[String]): Unit = {
val env = ExecutionEnvironment.getExecutionEnvironment
//fromCollection(env)
textFile(env)
}
def textFile(env: ExecutionEnvironment): Unit = {
val filePathFilter = "E:/test/input/hello.txt"
env.readTextFile(filePathFilter).print()
}readTextFile方法需要参数1：文件路径（可以使本地，也可以是hdfs://host:port/file/path），参数2：编码（如果不写，默认UTF-8）
是否可以指定文件夹？
我们直接传递文件夹路径
def main(args: Array[String]): Unit = {
val env = ExecutionEnvironment.getExecutionEnvironment
//fromCollection(env)
textFile(env)
}
def textFile(env: ExecutionEnvironment): Unit = {
//val filePathFilter = "E:/test/input/hello.txt"
val filePathFilter = "E:/test/input"
env.readTextFile(filePathFilter).print()
}运行结果正常。说明readTextFile方法传入文件夹，也没有问题，它将会遍历文件夹下面的所有文件
public static void main(String[] args) throws Exception {
ExecutionEnvironment executionEnvironment = ExecutionEnvironment.getExecutionEnvironment();
// fromCollection(executionEnvironment);
textFile(executionEnvironment);
}
public static void textFile(ExecutionEnvironment env) throws Exception {
String filePath = "E:/test/input/hello.txt";
// String filePath = "E:/test/input";
env.readTextFile(filePath).print();
}同样的道理，java中也可以指定文件或者文件夹，如果指定文件夹，那么将遍历文件夹下面的所有文件。
创建一个CSV文件，内容如下：
name,age,job Tom,26,cat Jerry,24,mouse sophia,30,developer
读取csv文件方法readCsvFile，参数如下：
filePath: String, lineDelimiter: String = "\n", fieldDelimiter: String = ",", 字段分隔符 quoteCharacter: Character = null, ignoreFirstLine: Boolean = false, 是否忽略第一行 ignoreComments: String = null, lenient: Boolean = false, includedFields: Array[Int] = null, 读取文件的哪几列 pojoFields: Array[String] = null)
读取csv文件代码如下：
def csvFile(env:ExecutionEnvironment): Unit = {
import org.apache.flink.api.scala._
val filePath = "E:/test/input/people.csv"
env.readCsvFile[(String, Int, String)](filePath, ignoreFirstLine = true).print()
}如何只读前两列，就需要指定includedFields了，
env.readCsvFile[(String, Int)](filePath, ignoreFirstLine = true, includedFields = Array(0, 1)).print()
之前使用Tuple方式指定类型，如何指定自定义的一个case class？
def csvFile(env: ExecutionEnvironment): Unit = {
import org.apache.flink.api.scala._
val filePath = "E:/test/input/people.csv"
// env.readCsvFile[(String, Int, String)](filePath, ignoreFirstLine = true).print()
// env.readCsvFile[(String, Int)](filePath, ignoreFirstLine = true, includedFields = Array(0, 1)).print()
env.readCsvFile[MyCaseClass](filePath, ignoreFirstLine = true, includedFields = Array(0, 1)).print()
}
case class MyCaseClass(name: String, age: Int)如何指定POJO？
新建一个POJO类，people
public class People {
private String name;
private int age;
private String job;
@Override
public String toString() {
return "People{" +
"name='" + name + '\'' +
", age=" + age +
", job='" + job + '\'' +
'}';
}
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
public int getAge() {
return age;
}
public void setAge(int age) {
this.age = age;
}
public String getJob() {
return job;
}
public void setJob(String job) {
this.job = job;
}
} env.readCsvFile[People](filePath, ignoreFirstLine = true, pojoFields = Array("name", "age", "job")).print() public static void csvFile(ExecutionEnvironment env) throws Exception {
String filePath = "E:/test/input/people.csv";
DataSource<Tuple2<String, Integer>> types = env.readCsvFile(filePath).ignoreFirstLine().includeFields("11").types(String.class, Integer.class);
types.print();
}只取出第一列和第二列的数据。
读取POJO数据：
env.readCsvFile(filePath).ignoreFirstLine().pojoType(People.class, "name", "age", "job").print();
def main(args: Array[String]): Unit = {
val env = ExecutionEnvironment.getExecutionEnvironment
//fromCollection(env)
// textFile(env)
// csvFile(env)
readRecursiveFiles(env)
}
def readRecursiveFiles(env: ExecutionEnvironment): Unit = {
val filePath = "E:/test/nested"
val parameter = new Configuration()
parameter.setBoolean("recursive.file.enumeration", true)
env.readTextFile(filePath).withParameters(parameter).print()
} def readCompressionFiles(env: ExecutionEnvironment): Unit = {
val filePath = "E:/test/my.tar.gz"
env.readTextFile(filePath).print()
}可以直接读取压缩文件。因为提高了空间利用率，但是却导致CPU的压力也提升了。因此需要一个权衡。需要调优，在各种情况下去选择更合适的方式。不是任何一种优化都能带来想要的结果。如果本身集群的CPU压力就高，那么就不应该读取压缩文件了。
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