7.FLINK Source\基于集合\基于文件\基于Socket\自定义Source--随机订单数量\自定义Source\自定义Source-MySQL
7.Source
7.1.基于集合
7.2.基于文件
7.3.基于Socket
7.4.自定义Source–随机订单数量
7.4.1.自定义Source
7.5.自定义Source-MySQL
7.Source
7.1.基于集合
基于集合的Source
一般用于学习测试时编造数据时使用。
env.fromElements(可变参数);
env.fromCollection(各种集合);
env.generateSequence(开始，结束);
env.fromSequence(开始，结束);
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import java.util.Arrays;
/**
* TODO
*
* @author tuzuoquan
* @date 2022/4/1 21:52
*/
public class SourceDemo01_Collection {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<String> ds1 = env.fromElements("hadoop spark flink", "hadoop spark flink");
DataStream<String> ds2 = env.fromCollection(Arrays.asList("hadoop spark flink", "hadoop spark flink"));
DataStream<Long> ds3 = env.generateSequence(1, 100);
DataStream<Long> ds4 = env.fromSequence(1, 100);
//TODO 2.transformation
//TODO 3.sink
ds1.print();
ds2.print();
ds3.print();
ds4.print();
//TODO 4.execute
env.execute();
}
}
7.2.基于文件
一般用于学习测试时编造数据时使用
env.readTextFile(本地/HDFS文件/文件夹); //压缩文件也可以
public class SourceDemo02_File {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<String> ds1 = env.readTextFile("data/input/words.txt");
DataStream<String> ds2 = env.readTextFile("data/input/dir");
DataStream<String> ds3 = env.readTextFile("data/input/wordcount.txt.gz");
//TODO 2.transformation
//TODO 3.sink
ds1.print();
ds2.print();
ds3.print();
//TODO 4.execute
env.execute();
}
}
7.3.基于Socket
需求：
1.在node1上使用nc -lk 9999向指定端口发送数据
nc是netcat的简称，原本是用来设置路由器，我们可以利用它向某个端口发送数据。
如果没有该命令可以下安装
yum install -y nc
2.使用Flink编写流处理应用程序实时统计单词数量
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
/**
* @author tuzuoquan
* @date 2022/4/1 23:49
*/
public class SourceDemo03_Socket {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<String> line = env.socketTextStream("node1", 9999);
//TODO 2.transformation
/*SingleOutputStreamOperator words = lines.flatMap(new FlatMapFunction() {
@Override
public void flatMap(String value, Collector out) throws Exception {
String[] arr = value.split(" ");
for (String word : arr) {
out.collect(word);
}
}
});
words.map(new MapFunction>() {
@Override
public Tuple2 map(String value) throws Exception {
return Tuple2.of(value,1);
}
});*/
//注意:下面的操作将上面的2步合成了1步,直接切割单词并记为1返回
SingleOutputStreamOperator<Tuple2<String,Integer>> wordAndOne =
line.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
@Override
public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {
String[] arr = value.split(" ");
for (String word : arr) {
out.collect(Tuple2.of(word, 1));
}
}
});
SingleOutputStreamOperator<Tuple2<String, Integer>> result =
wordAndOne.keyBy(t -> t.f0).sum(1);
// TODO 3.sink
result.print();
// TODO 4.execute
env.execute();
}
}
7.4.自定义Source–随机订单数量
注意：lombok的使用
<dependency>
<groupId>org.projectlombokgroupId>
<artifactId>lombokartifactId>
<version>1.18.2version>
<scope>providedscope>
dependency>
package demo3;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
/**
* @author tuzuoquan
* @date 2022/4/2 0:02
*/
@Data
@AllArgsConstructor
@NoArgsConstructor
public class Order {
private String id;
private Integer userId;
private Integer money;
private Long createTime;
}
7.4.1.自定义Source
随机生成数据
Flink还提供了数据源接口，我们实现该接口就可以实现自定义数据源，不同的接口有不同的功能，分类如下：
SourceFunction: 非并行数据源（并行度只能 = 1）
RichSourceFunction: 多功能非并行数据源（并行度只能 = 1）
ParallelSourceFunction: 并行数据源（并行度能够 >= 1）
RichParallelSourceFunction: 多功能并行数据源（并行度能够 >= 1）
需求
每隔1秒随机生成一条订单信息(订单ID、用户ID、订单金额、时间戳)
要求：
- 随机生成订单ID (UUID)
- 随机生成用户ID (0 - 2)
- 随机生成订单金额 (0 - 100)
- 时间戳为当前的系统时间
CREATE TABLE `t_student` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`name` varchar(255) DEFAULT NULL,
`age` int(11) DEFAULT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=7 DEFAULT CHARSET=utf8;
INSERT INTO `t_student` VALUES ('1', 'jack', '18');
INSERT INTO `t_student` VALUES ('2', 'tom', '19');
INSERT INTO `t_student` VALUES ('3', 'rose', '20');
INSERT INTO `t_student` VALUES ('4', 'tom', '19');
INSERT INTO `t_student` VALUES ('5', 'jack', '18');
INSERT INTO `t_student` VALUES ('6', 'rose', '20');
package demo3;
import demo3.Order;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;
import java.util.Random;
import java.util.UUID;
/**
* TODO
*
* @author tuzuoquan
* @date 2022/4/2 0:13
*/
public class SourceDemo04_Customer {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<Order> orderDS = env.addSource(new MyOrderSource()).setParallelism(2);
//TODO 2.transformation
//TODO 3.sink
orderDS.print();
//TODO 4.execute
env.execute();
}
public static class MyOrderSource extends RichParallelSourceFunction<Order> {
private Boolean flag = true;
// 执行并生数据
@Override
public void run(SourceContext<Order> sourceContext) throws Exception {
Random random = new Random();
while (flag) {
String oid = UUID.randomUUID().toString();
int userId = random.nextInt(3);
int money = random.nextInt(101);
long createTime = System.currentTimeMillis();
sourceContext.collect(new Order(oid, userId, money, createTime));
Thread.sleep(1000);
}
}
//执行cancel命令的时候执行
@Override
public void cancel() {
flag = false;
}
}
}
执行结果：
1> Order(id=20783def-ede5-4fd3-88b8-79023f936bf2, userId=2, money=13, createTime=1648830057754)
3> Order(id=3c1daa2f-ff8e-42fc-bdfa-b88de08df592, userId=0, money=86, createTime=1648830057754)
2> Order(id=e55ee291-6ecf-4545-b85e-ff9a22726e59, userId=1, money=93, createTime=1648830058765)
4> Order(id=60aafc7f-39a1-4911-8d02-bb3f0de99024, userId=0, money=60, createTime=1648830058765)
3> Order(id=77ccdb4f-3e67-46f0-9920-b285c9c78fd3, userId=2, money=36, createTime=1648830059775)
5> Order(id=c1635e13-b134-424f-90b8-d2f738a7e3bc, userId=0, money=42, createTime=1648830059776)
6> Order(id=e4cbc59c-a3ab-4969-8b7c-828f5cb72a38, userId=0, money=59, createTime=1648830060778)
7.5.自定义Source-MySQL
MySQL
实际开发中，经常会实时接收一些数据,要和MySQL中存储的一些规则进行匹配，那么这时候就可以使用Flink自定义数据源从MySQL中读取数据
那么现在先完成一个简单的需求：
1、从MySQL中实时加载数据
2、要求MySQL中的数据有变化，也能被实时加载出来。
package demo4;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
/**
* @author tuzuoquan
* @date 2022/4/14 9:49
*/
@Data
@NoArgsConstructor
@AllArgsConstructor
public class Student {
private Integer id;
private String name;
private Integer age;
}
package demo4;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
/**
* TODO
*
* @author tuzuoquan
* @date 2022/4/14 9:51
*/
public class MySQLSource extends RichParallelSourceFunction<Student> {
private boolean flag = true;
private Connection conn = null;
private PreparedStatement ps = null;
private ResultSet rs = null;
/**
* open只执行一次，适合开启资源
* @param parameters
* @throws Exception
*/
@Override
public void open(Configuration parameters) throws Exception {
conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/bigdata",
"root", "root");
String sql = "select id,name,age from t_student";
ps = conn.prepareStatement(sql);
}
@Override
public void run(SourceContext<Student> ctx) throws Exception {
while (flag) {
rs = ps.executeQuery();
while (rs.next()) {
int id = rs.getInt("id");
String name = rs.getString("name");
int age = rs.getInt("age");
ctx.collect(new Student(id, name, age));
}
Thread.sleep(5000);
}
}
/**
* 接收到cancel命令时取消数据生成
*/
@Override
public void cancel() {
flag = false;
}
/**
* close里面关闭资源
* @throws Exception
*/
@Override
public void close() throws Exception {
if (conn != null) { conn.close(); }
if (ps != null) { ps.close(); }
if (rs != null) { rs.close(); }
}
}
/*
CREATE TABLE `t_student` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`name` varchar(255) DEFAULT NULL,
`age` int(11) DEFAULT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=7 DEFAULT CHARSET=utf8;
INSERT INTO `t_student` VALUES ('1', 'jack', '18');
INSERT INTO `t_student` VALUES ('2', 'tom', '19');
INSERT INTO `t_student` VALUES ('3', 'rose', '20');
INSERT INTO `t_student` VALUES ('4', 'tom', '19');
INSERT INTO `t_student` VALUES ('5', 'jack', '18');
INSERT INTO `t_student` VALUES ('6', 'rose', '20');
*/
package demo4;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
/**
* @author tuzuoquan
* @date 2022/4/14 9:42
*/
public class SourceDemo05_Customer_MySQL {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<Student> studentDS = env.addSource(new MySQLSource()).setParallelism(1);
//TODO 2.transformation
//TODO 3.sink
studentDS.print();
//TODO 4.execute
env.execute();
}
}