【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 系列文章
1、Flink 专栏等系列综合文章链接
文章目录
- Flink 系列文章
- 一、Flink的23种算子说明及示例
-
- 1、maven依赖
- 2、java bean
- 3、map
- 4、flatmap
- 5、Filter
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-8encoding>
<project.build.sourceEncoding>UTF-8project.build.sourceEncoding>
<maven.compiler.source>1.8maven.compiler.source>
<maven.compiler.target>1.8maven.compiler.target>
<java.version>1.8java.version>
<scala.version>2.12scala.version>
<flink.version>1.17.0flink.version>
properties>
<dependencies>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-clients_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-scala_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-javaartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-streaming-scala_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-streaming-java_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-api-scala-bridge_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-api-java-bridge_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-planner-blink_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-commonartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.slf4jgroupId>
<artifactId>slf4j-log4j12artifactId>
<version>1.7.7version>
<scope>runtimescope>
dependency>
<dependency>
<groupId>log4jgroupId>
<artifactId>log4jartifactId>
<version>1.2.17version>
<scope>runtimescope>
dependency>
<dependency>
<groupId>org.projectlombokgroupId>
<artifactId>lombokartifactId>
<version>1.18.2version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.hadoopgroupId>
<artifactId>hadoop-commonartifactId>
<version>3.1.4version>
dependency>
<dependency>
<groupId>org.apache.hadoopgroupId>
<artifactId>hadoop-clientartifactId>
<version>3.1.4version>
dependency>
<dependency>
<groupId>org.apache.hadoopgroupId>
<artifactId>hadoop-hdfsartifactId>
<version>3.1.4version>
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", "[email protected]", 12, 1000),
new User(2, "alan2", "2", "[email protected]", 19, 200),
new User(3, "alan1", "3", "[email protected]", 28, 1500),
new User(5, "alan1", "5", "[email protected]", 15, 500),
new User(4, "alan2", "4", "[email protected]", 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 protected], age=17, balance=2000.0)
// 14> User(id=4, name=alan2, pwd=4, [email protected], age=35, balance=800.0)
// 11> User(id=2, name=alan2, pwd=2, [email protected], age=24, balance=400.0)
// 12> User(id=3, name=alan1, pwd=3, [email protected], age=33, balance=3000.0)
// 13> User(id=5, name=alan1, pwd=5, [email protected], 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 protected], age=17, balance=2000.0)
// 1> User(id=3, name=alan1, pwd=3, [email protected], age=33, balance=3000.0)
// 16> User(id=2, name=alan2, pwd=2, [email protected], age=24, balance=400.0)
// 3> User(id=4, name=alan2, pwd=4, [email protected], age=35, balance=800.0)
// 2> User(id=5, name=alan1, pwd=5, [email protected], age=20, balance=1000.0)
// 1> User(id=3, name=alan1, pwd=3, [email protected], 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 protected], age=20, balance=1000.0)
// 7> User(id=3, name=alan1, pwd=3, [email protected], age=33, balance=3000.0)
// 6> User(id=2, name=alan2, pwd=2, [email protected], age=24, balance=400.0)
// 9> User(id=4, name=alan2, pwd=4, [email protected], age=35, balance=800.0)
// 5> User(id=1, name=alan1, pwd=1, [email protected], age=17, balance=2000.0)
// 7> User(id=3, name=alan1, pwd=3, [email protected], age=33, balance=3000.0)
// 7> User(id=3, name=alan1, pwd=3, [email protected], 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", "[email protected]", 12, 1000),
new User(2, "alan2", "2", "[email protected]", 19, 200),
new User(3, "alan1", "3", "[email protected]", 28, 1500),
new User(5, "alan1", "5", "[email protected]", 15, 500),
new User(4, "alan2", "4", "[email protected]", 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 protected], age=15, balance=500.0)
// 15> User(id=4, name=alan2, pwd=4, [email protected], 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）