【flink番外篇】1、flink的23种常用算子介绍及详细示例（1）- map、flatmap和filter

原创

一瓢一瓢的饮 alanchan

发布于 2023-12-13 16:28:10

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发布于 2023-12-13 16:28:10

文章被收录于专栏：Flink 专栏Flink 专栏

Flink 系列文章

一、Flink 专栏

Flink 专栏系统介绍某一知识点，并辅以具体的示例进行说明。

1、Flink 部署系列本部分介绍Flink的部署、配置相关基础内容。
2、Flink基础系列本部分介绍Flink 的基础部分，比如术语、架构、编程模型、编程指南、基本的datastream api用法、四大基石等内容。
3、Flik Table API和SQL基础系列本部分介绍Flink Table Api和SQL的基本用法，比如Table API和SQL创建库、表用法、查询、窗口函数、catalog等等内容。
4、Flik Table API和SQL提高与应用系列本部分是table api 和sql的应用部分，和实际的生产应用联系更为密切，以及有一定开发难度的内容。
5、Flink 监控系列本部分和实际的运维、监控工作相关。

二、Flink 示例专栏

Flink 示例专栏是 Flink 专栏的辅助说明，一般不会介绍知识点的信息，更多的是提供一个一个可以具体使用的示例。本专栏不再分目录，通过链接即可看出介绍的内容。

两专栏的所有文章入口点击：Flink 系列文章汇总索引

@TOC

本文主要介绍Flink 的3种常用的operator（map、flatmap和filter）及以具体可运行示例进行说明.

如果需要了解更多内容，可以在本人Flink 专栏中了解更新系统的内容。

本文除了maven依赖外，没有其他依赖。

本专题分为五篇，即：

【flink番外篇】1、flink的23种常用算子介绍及详细示例（1）- map、flatmap和filter

【flink番外篇】1、flink的23种常用算子介绍及详细示例（2）- keyby、reduce和Aggregations

【flink番外篇】1、flink的23种常用算子介绍及详细示例（3）-window、distinct、join等

【flink番外篇】1、flink的23种常用算子介绍及详细示例（4）- union、window join、connect、outputtag、cache、iterator、project

【flink番外篇】1、flink的23种常用算子介绍及详细示例（完整版）

一、Flink的23种算子说明及示例

1、maven依赖

下文中所有示例都是用该maven依赖，除非有特殊说明的情况。

<properties>
        <encoding>UTF-8</encoding>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <java.version>1.8</java.version>
        <scala.version>2.12</scala.version>
        <flink.version>1.17.0</flink.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-scala-bridge_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-common</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <!-- 日志 -->
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>1.7.7</version>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>log4j</groupId>
            <artifactId>log4j</artifactId>
            <version>1.2.17</version>
            <scope>runtime</scope>
        </dependency>

        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.2</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-common</artifactId>
			<version>3.1.4</version>
		</dependency>
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-client</artifactId>
			<version>3.1.4</version>
		</dependency>
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-hdfs</artifactId>
			<version>3.1.4</version>
		</dependency>
    </dependencies>

2、java bean

下文所依赖的User如下

import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;

/**
 * @author alanchan
 *
 */
@Data
@AllArgsConstructor
@NoArgsConstructor
public class User {
	private int id;
	private String name;
	private String pwd;
	private String email;
	private int age;
	private double balance;
}

3、map

DataStream->DataStream

这是最简单的转换之一，其中输入是一个数据流，输出的也是一个数据流。

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.datastreamapi.User;

/**
 * @author alanchan
 *
 */
public class TestMapDemo {

	/**
	 * @param args
	 * @throws Exception
	 */
	public static void main(String[] args) throws Exception {
		// env
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		// source

		// transformation
		mapFunction5(env);
		// sink
		// execute
		env.execute();
	}

	// 构造一个list，然后将list中数字乘以2输出，内部匿名类实现
	public static void mapFunction1(StreamExecutionEnvironment env) throws Exception {

		List<Integer> data = new ArrayList<Integer>();
		for (int i = 1; i <= 10; i++) {
			data.add(i);
		}
		DataStreamSource<Integer> source = env.fromCollection(data);

		SingleOutputStreamOperator<Integer> sink = source.map(new MapFunction<Integer, Integer>() {

			@Override
			public Integer map(Integer inValue) throws Exception {
				return inValue * 2;
			}
		});

		sink.print();
//		9> 12
//		4> 2
//		10> 14
//		8> 10
//		13> 20
//		7> 8
//		12> 18
//		11> 16
//		5> 4
//		6> 6
	}

	// 构造一个list，然后将list中数字乘以2输出，lambda实现
	public static void mapFunction2(StreamExecutionEnvironment env) throws Exception {
		List<Integer> data = new ArrayList<Integer>();
		for (int i = 1; i <= 10; i++) {
			data.add(i);
		}
		DataStreamSource<Integer> source = env.fromCollection(data);
		SingleOutputStreamOperator<Integer> sink = source.map(i -> 2 * i);
		sink.print();
//		3> 4
//		4> 6
//		9> 16
//		7> 12
//		10> 18
//		2> 2
//		6> 10
//		5> 8
//		8> 14
//		11> 20
	}

	// 构造User数据源
	public static DataStreamSource<User> source(StreamExecutionEnvironment env) {
		DataStreamSource<User> source = env.fromCollection(Arrays.asList(
				new User(1, "alan1", "1", "1@1.com", 12, 1000), 
				new User(2, "alan2", "2", "2@2.com", 19, 200),
				new User(3, "alan1", "3", "3@3.com", 28, 1500), 
				new User(5, "alan1", "5", "5@5.com", 15, 500), 
				new User(4, "alan2", "4", "4@4.com", 30, 400)
				)
			);
		return source;
	}

	// lambda实现用户对象的balance×2和age+5功能
	public static SingleOutputStreamOperator<User> mapFunction3(StreamExecutionEnvironment env) throws Exception {
		DataStreamSource<User> source = source(env);

		SingleOutputStreamOperator<User> sink = source.map((MapFunction<User, User>) user -> {
			User user2 = user;
			user2.setAge(user.getAge() + 5);
			user2.setBalance(user.getBalance() * 2);

			return user2;
		});
		sink.print();
//		10> User(id=1, name=alan1, pwd=1, email=1@1.com, age=17, balance=2000.0)
//		14> User(id=4, name=alan2, pwd=4, email=4@4.com, age=35, balance=800.0)
//		11> User(id=2, name=alan2, pwd=2, email=2@2.com, age=24, balance=400.0)
//		12> User(id=3, name=alan1, pwd=3, email=3@3.com, age=33, balance=3000.0)
//		13> User(id=5, name=alan1, pwd=5, email=5@5.com, age=20, balance=1000.0)
		return sink;
	}

	// lambda实现balance*2和age+5后，balance》=2000和age》=20的数据过滤出来
	public static SingleOutputStreamOperator<User> mapFunction4(StreamExecutionEnvironment env) throws Exception {

		SingleOutputStreamOperator<User> sink = mapFunction3(env).filter(user -> user.getBalance() >= 2000 && user.getAge() >= 20);
		sink.print();
//		15> User(id=1, name=alan1, pwd=1, email=1@1.com, age=17, balance=2000.0)
//		1> User(id=3, name=alan1, pwd=3, email=3@3.com, age=33, balance=3000.0)
//		16> User(id=2, name=alan2, pwd=2, email=2@2.com, age=24, balance=400.0)
//		3> User(id=4, name=alan2, pwd=4, email=4@4.com, age=35, balance=800.0)
//		2> User(id=5, name=alan1, pwd=5, email=5@5.com, age=20, balance=1000.0)
//		1> User(id=3, name=alan1, pwd=3, email=3@3.com, age=33, balance=3000.0)
		return sink;
	}

	// lambda实现balance*2和age+5后，balance》=2000和age》=20的数据过滤出来并通过flatmap收集
	public static SingleOutputStreamOperator<User> mapFunction5(StreamExecutionEnvironment env) throws Exception {

		SingleOutputStreamOperator<User> sink = mapFunction4(env).flatMap((FlatMapFunction<User, User>) (user, out) -> {
			if (user.getBalance() >= 3000) {
				out.collect(user);
			}
		}).returns(User.class);

		sink.print();
//		8> User(id=5, name=alan1, pwd=5, email=5@5.com, age=20, balance=1000.0)
//		7> User(id=3, name=alan1, pwd=3, email=3@3.com, age=33, balance=3000.0)
//		6> User(id=2, name=alan2, pwd=2, email=2@2.com, age=24, balance=400.0)
//		9> User(id=4, name=alan2, pwd=4, email=4@4.com, age=35, balance=800.0)
//		5> User(id=1, name=alan1, pwd=1, email=1@1.com, age=17, balance=2000.0)
//		7> User(id=3, name=alan1, pwd=3, email=3@3.com, age=33, balance=3000.0)
//		7> User(id=3, name=alan1, pwd=3, email=3@3.com, age=33, balance=3000.0)
		return sink;
	}

}

4、flatmap

DataStream->DataStream

FlatMap 采用一条记录并输出零个，一个或多个记录。将集合中的每个元素变成一个或多个元素,并返回扁平化之后的结果。

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

/**
 * @author alanchan
 *
 */
public class TestFlatMapDemo {

	/**
	 * @param args
	 * @throws Exception
	 */
	public static void main(String[] args) throws Exception {
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

		flatMapFunction3(env);

		env.execute();
	}

	// 构造User数据源
	public static DataStreamSource<String> source(StreamExecutionEnvironment env) {
		List<String> info = new ArrayList<>();
		info.add("i am alanchan");
		info.add("i like hadoop");
		info.add("i like flink");
		info.add("and you ?");

		DataStreamSource<String> dataSource = env.fromCollection(info);

		return dataSource;
	}

	// 将句子以空格进行分割-内部匿名类实现
	public static void flatMapFunction1(StreamExecutionEnvironment env) throws Exception {

		DataStreamSource<String> source = source(env);
		SingleOutputStreamOperator<String> sink = source.flatMap(new FlatMapFunction<String, String>() {
			@Override
			public void flatMap(String value, Collector<String> out) throws Exception {
				String[] splits = value.split(" ");
				for (String split : splits) {
					out.collect(split);
				}
			}
		});
		sink.print();
//		11> and
//		10> i
//		8> i
//		9> i
//		8> am
//		10> like
//		11> you
//		10> flink
//		8> alanchan
//		9> like
//		11> ?
//		9> hadoop
	}

	// lambda实现
	public static void flatMapFunction2(StreamExecutionEnvironment env) throws Exception {
		DataStreamSource<String> source = source(env);
		SingleOutputStreamOperator<String> sink = source.flatMap((FlatMapFunction<String, String>) (input, out) -> {
			String[] splits = input.split(" ");
			for (String split : splits) {
				out.collect(split);
			}
		}).returns(String.class);

		sink.print();
//		6> i
//		8> and
//		8> you
//		8> ?
//		5> i
//		7> i
//		5> am
//		5> alanchan
//		6> like
//		7> like
//		6> hadoop
//		7> flink
	}

	// lambda实现
	public static void flatMapFunction3(StreamExecutionEnvironment env) throws Exception {
		DataStreamSource<String> source = source(env);
		SingleOutputStreamOperator<String> sink = source.flatMap((String input, Collector<String> out) -> Arrays.stream(input.split(" ")).forEach(out::collect))
				.returns(String.class);

		sink.print();
//		8> i
//		11> and
//		10> i
//		9> i
//		10> like
//		11> you
//		8> am
//		11> ?
//		10> flink
//		9> like
//		9> hadoop
//		8> alanchan
	}

}

5、Filter

DataStream → DataStream

Filter 函数根据条件判断出结果。按照指定的条件对集合中的元素进行过滤,过滤出返回true/符合条件的元素。

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.datastreamapi.User;

/**
 * @author alanchan
 *
 */
public class TestFilterDemo {
	// 构造User数据源
	public static DataStreamSource<User> sourceUser(StreamExecutionEnvironment env) {
		DataStreamSource<User> source = env.fromCollection(Arrays.asList(
			new User(1, "alan1", "1", "1@1.com", 12, 1000), 
			new User(2, "alan2", "2", "2@2.com", 19, 200),
			new User(3, "alan1", "3", "3@3.com", 28, 1500), 
			new User(5, "alan1", "5", "5@5.com", 15, 500), 
			new User(4, "alan2", "4", "4@4.com", 30, 400)));
		return source;
	}

	// 构造User数据源
	public static DataStreamSource<Integer> sourceList(StreamExecutionEnvironment env) {
		List<Integer> data = new ArrayList<Integer>();
		for (int i = 1; i <= 10; i++) {
			data.add(i);
		}
		DataStreamSource<Integer> source = env.fromCollection(data);

		return source;
	}

	// 过滤出大于5的数字，内部匿名类
	public static void filterFunction1(StreamExecutionEnvironment env) throws Exception {
		DataStream<Integer> source = sourceList(env);

		SingleOutputStreamOperator<Integer> sink = source.map(new MapFunction<Integer, Integer>() {
			public Integer map(Integer value) throws Exception {
				return value + 1;
			}
		}).filter(new FilterFunction<Integer>() {
			@Override
			public boolean filter(Integer value) throws Exception {
				return value > 5;
			}
		});
		sink.print();
//		1> 10
//		14> 7
//		16> 9
//		13> 6
//		2> 11
//		15> 8
	}

	// lambda实现
	public static void filterFunction2(StreamExecutionEnvironment env) throws Exception {
		DataStream<Integer> source = sourceList(env);
		SingleOutputStreamOperator<Integer> sink = source.map(i -> i + 1).filter(value -> value > 5);
		sink.print();
//		12> 7
//		15> 10
//		11> 6
//		13> 8
//		14> 9
//		16> 11
	}

	// 查询user id大于3的记录
	public static void filterFunction3(StreamExecutionEnvironment env) throws Exception {
		DataStream<User> source = sourceUser(env);
		SingleOutputStreamOperator<User> sink = source.filter(user -> user.getId() > 3);
		sink.print();
//		14> User(id=5, name=alan1, pwd=5, email=5@5.com, age=15, balance=500.0)
//		15> User(id=4, name=alan2, pwd=4, email=4@4.com, age=30, balance=400.0)
	}

	/**
	 * @param args
	 */
	public static void main(String[] args) throws Exception {
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		filterFunction3(env);
		env.execute();

	}

}

本文主要介绍Flink 的3种常用的operator及以具体可运行示例进行说明。

如果需要了解更多内容，可以在本人Flink 专栏中了解更新系统的内容。

本专题分为五篇，即：

【flink番外篇】1、flink的23种常用算子介绍及详细示例（1）- map、flatmap和filter

【flink番外篇】1、flink的23种常用算子介绍及详细示例（2）- keyby、reduce和Aggregations

【flink番外篇】1、flink的23种常用算子介绍及详细示例（3）-window、distinct、join等

【flink番外篇】1、flink的23种常用算子介绍及详细示例（4）- union、window join、connect、outputtag、cache、iterator、project

【flink番外篇】1、flink的23种常用算子介绍及详细示例（完整版）

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

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