Flink 学习 — 自定义 Data Source
前言
在 《Flink学习》—— Data Source 介绍 文章中，我给大家介绍了 Flink Data Source 以及简短的介绍了一下自定义 Data Source，这篇文章更详细的介绍下，并写一个 demo 出来让大家理解。
Flink Kafka source
准备工作
我们先来看下 Flink 从 Kafka topic 中获取数据的 demo，首先你需要安装好了 FLink 和 Kafka 。
运行启动 Flink、Zookepeer、Kafka，
好了，都启动了！
maven 依赖
<!--flink java-->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<!--日志-->
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.7</version>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
<version>1.2.17</version>
<scope>runtime</scope>
</dependency>
<!--flink kafka connector-->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka-0.11_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<!--alibaba fastjson-->
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.51</version>
</dependency>
测试发送数据到 kafka topic
实体类，Metric.java
package com.zhisheng.flink.model;
import java.util.Map;
public class Metric {
public String name;
public long timestamp;
public Map<String, Object> fields;
public Map<String, String> tags;
public Metric() {
}
public Metric(String name, long timestamp, Map<String, Object> fields, Map<String, String> tags) {
this.name = name;
this.timestamp = timestamp;
this.fields = fields;
this.tags = tags;
}
@Override
public String toString() {
return "Metric{" +
"name='" + name + '\'' +
", timestamp='" + timestamp + '\'' +
", fields=" + fields +
", tags=" + tags +
'}';
}
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
public long getTimestamp() {
return timestamp;
}
public void setTimestamp(long timestamp) {
this.timestamp = timestamp;
}
public Map<String, Object> getFields() {
return fields;
}
public void setFields(Map<String, Object> fields) {
this.fields = fields;
}
public Map<String, String> getTags() {
return tags;
}
public void setTags(Map<String, String> tags) {
this.tags = tags;
}
}
往 kafka 中写数据工具类：KafkaUtils.java
import com.alibaba.fastjson.JSON;
import com.zhisheng.flink.model.Metric;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.util.HashMap;
import java.util.Map;
import java.util.Properties;
public class KafkaUtils {
public static final String broker_list = "localhost:9092";
public static final String topic = "metric"; // kafka topic，Flink 程序中需要和这个统一
public static void writeToKafka() throws InterruptedException {
Properties props = new Properties();
props.put("bootstrap.servers", broker_list);
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); //key 序列化
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer"); //value 序列化
KafkaProducer producer = new KafkaProducer<String, String>(props);
Metric metric = new Metric();
metric.setTimestamp(System.currentTimeMillis());
metric.setName("mem");
Map<String, String> tags = new HashMap<>();
Map<String, Object> fields = new HashMap<>();
tags.put("cluster", "zhisheng");
tags.put("host_ip", "101.147.022.106");
fields.put("used_percent", 90d);
fields.put("max", 27244873d);
fields.put("used", 17244873d);
fields.put("init", 27244873d);
metric.setTags(tags);
metric.setFields(fields);
ProducerRecord record = new ProducerRecord<String, String>(topic, null, null, JSON.toJSONString(metric));
producer.send(record);
System.out.println("发送数据: " + JSON.toJSONString(metric));
producer.flush();
}
public static void main(String[] args) throws InterruptedException {
while (true) {
Thread.sleep(300);
writeToKafka();
}
}
}
运行：
如果出现如上图标记的，即代表能够不断的往 kafka 发送数据的。
Flink 程序
Main.java
package com.zhisheng.flink;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011;
import java.util.Properties;
public class Main {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("zookeeper.connect", "localhost:2181");
props.put("group.id", "metric-group");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer"); //key 反序列化
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("auto.offset.reset", "latest"); //value 反序列化
DataStreamSource<String> dataStreamSource = env.addSource(new FlinkKafkaConsumer011<>(
"metric", //kafka topic
new SimpleStringSchema(), // String 序列化
props)).setParallelism(1);
dataStreamSource.print(); //把从 kafka 读取到的数据打印在控制台
env.execute("Flink add data source");
}
}
运行起来：
看到没程序，Flink 程序控制台能够源源不断的打印数据呢。
自定义 Source
上面就是 Flink 自带的 Kafka source，那么接下来就模仿着写一个从 MySQL 中读取数据的 Source。
首先 pom.xml 中 添加 MySQL 依赖 ：
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.34</version>
</dependency>
数据库建表 如下：
DROP TABLE IF EXISTS `student`;
CREATE TABLE `student` (
`id` int(11) unsigned NOT NULL AUTO_INCREMENT,
`name` varchar(25) COLLATE utf8_bin DEFAULT NULL,
`password` varchar(25) COLLATE utf8_bin DEFAULT NULL,
`age` int(10) DEFAULT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=5 DEFAULT CHARSET=utf8 COLLATE=utf8_bin;
插入数据 ：
INSERT INTO `student` VALUES ('1', 'zhisheng01', '123456', '18'), ('2', 'zhisheng02', '123', '17'), ('3', 'zhisheng03', '1234', '18'), ('4', 'zhisheng04', '12345', '16');
COMMIT;
新建实体类 ：Student.java
package com.zhisheng.flink.model;
public class Student {
public int id;
public String name;
public String password;
public int age;
public Student() {
}
public Student(int id, String name, String password, int age) {
this.id = id;
this.name = name;
this.password = password;
this.age = age;
}
@Override
public String toString() {
return "Student{" +
"id=" + id +
", name='" + name + '\'' +
", password='" + password + '\'' +
", age=" + age +
'}';
}
public int getId() {
return id;
}
public void setId(int id) {
this.id = id;
}
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
public String getPassword() {
return password;
}
public void setPassword(String password) {
this.password = password;
}
public int getAge() {
return age;
}
public void setAge(int age) {
this.age = age;
}
}
新建 Source 类 SourceFromMySQL.java，该类继承 RichSourceFunction ，实现里面的 open、close、run、cancel 方法：
package com.zhisheng.flink.source;
import com.zhisheng.flink.model.Student;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
public class SourceFromMySQL extends RichSourceFunction<Student> {
PreparedStatement ps;
private Connection connection;
/**
* open() 方法中建立连接，这样不用每次 invoke 的时候都要建立连接和释放连接。
*
* @param parameters
* @throws Exception
*/
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
connection = getConnection();
String sql = "select * from Student;";
ps = this.connection.prepareStatement(sql);
}
/**
* 程序执行完毕就可以进行，关闭连接和释放资源的动作了
*
* @throws Exception
*/
@Override
public void close() throws Exception {
super.close();
if (connection != null) { //关闭连接和释放资源
connection.close();
}
if (ps != null) {
ps.close();
}
}
/**
* DataStream 调用一次 run() 方法用来获取数据
*
* @param ctx
* @throws Exception
*/
@Override
public void run(SourceContext<Student> ctx) throws Exception {
ResultSet resultSet = ps.executeQuery();
while (resultSet.next()) {
Student student = new Student(
resultSet.getInt("id"),
resultSet.getString("name").trim(),
resultSet.getString("password").trim(),
resultSet.getInt("age"));
ctx.collect(student);
}
}
@Override
public void cancel() {
}
private static Connection getConnection() {
Connection con = null;
try {
Class.forName("com.mysql.jdbc.Driver");
con = DriverManager.getConnection("jdbc:mysql://localhost:3306/test?useUnicode=true&characterEncoding=UTF-8", "root", "root123456");
} catch (Exception e) {
System.out.println("-----------mysql get connection has exception , msg = "+ e.getMessage());
}
return con;
}
}
Flink 程序 ：
package com.zhisheng.flink;
import com.zhisheng.flink.source.SourceFromMySQL;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
public class Main2 {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.addSource(new SourceFromMySQL()).print();
env.execute("Flink add data sourc");
}
}
运行 Flink 程序，控制台日志中可以看见打印的 student 信息。
RichSourceFunction
从上面自定义的 Source 可以看到我们继承的就是这个 RichSourceFunction 类，那么来了解一下：
一个抽象类，继承自 AbstractRichFunction。为实现一个 Rich SourceFunction 提供基础能力。该类的子类有三个，两个是抽象类，在此基础上提供了更具体的实现，另一个是 ContinuousFileMonitoringFunction。
- MessageAcknowledgingSourceBase ：它针对的是数据源是消息队列的场景并且提供了基于 ID 的应答机制。
- MultipleIdsMessageAcknowledgingSourceBase ： 在 MessageAcknowledgingSourceBase 的基础上针对 ID 应答机制进行了更为细分的处理，支持两种 ID 应答模型：session id 和 unique message id。
- ContinuousFileMonitoringFunction：这是单个（非并行）监视任务，它接受 FileInputFormat，并且根据 FileProcessingMode 和 FilePathFilter，它负责监视用户提供的路径；决定应该进一步读取和处理哪些文件；创建与这些文件对应的 FileInputSplit 拆分，将它们分配给下游任务以进行进一步处理。
最后
本文主要讲了下 Flink 使用 Kafka Source 的使用，并提供了一个 demo 教大家如何自定义 Source，从 MySQL 中读取数据，当然你也可以从其他地方读取，实现自己的数据源 source。可能平时工作会比这个更复杂，需要大家灵活应对！
.
