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SpringBoot整合Flink（施耐德PLC物联网信息采集）

SpringBoot整合Flink（施耐德PLC物联网信息采集）

Linux环境安装kafka

前情：

施耐德PLC设备（TM200C16R）设置好信息采集程序，连接局域网，SpringBoot订阅MQTT主题，消息转至kafka，由flink接收并持久化到mysql数据库；

Wireshark抓包如下：

MQTTBox测试订阅如下：

已知参数：

服务器IP：139.220.193.14

端口号：1883

应用端账号：admin@tenlink

应用端密码：Tenlink@123

物联网账号：202303171001

物联网账号密码：03171001

订阅话题（topic）：

202303171001/p（发布话题，由设备发送，应用端接收）

202303171001/s（订阅话题，由应用端发送，设备接收）

订阅mqtt （前提是kafka是已经就绪状态且plc_thoroughfare主题是存在的）

  • maven pom
        <dependency>
            <groupId>org.eclipse.paho</groupId>
            <artifactId>org.eclipse.paho.client.mqttv3</artifactId>
            <version>1.2.5</version>
        </dependency>
  • yaml配置
spring:
  kafka:
    bootstrap-servers: ip:9092
    producer:
      key-serializer: org.apache.kafka.common.serialization.StringSerializer
      value-serializer: org.apache.kafka.common.serialization.StringSerializer

## 自定义
kafka:
  topics:
    # kafka 主题
    plc1: plc_thoroughfare

plc:
  broker: tcp://139.220.193.14:1883
  subscribe-topic:  202303171001/p
  username: admin@tenlink
  password: Tenlink@123
  client-id: subscribe_client
  • 订阅mqtt并将报文发送到kafka主题
import org.eclipse.paho.client.mqttv3.*;
import org.eclipse.paho.client.mqttv3.persist.MemoryPersistence;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.stereotype.Component;

import javax.annotation.PostConstruct;

/**
 * PLC 订阅消息
 */
@Component
public class SubscribeSample {

    private static final Logger log = LoggerFactory.getLogger(SubscribeSample.class);

    @Autowired
    private KafkaTemplate<String,Object> kafkaTemplate;

    @Value("${kafka.topics.plc1}")
    private String plc1;
    @Value("${plc.broker}")
    private String broker;
    @Value("${plc.subscribe-topic}")
    private String subscribeTopic;
    @Value("${plc.username}")
    private String username;
    @Value("${plc.password}")
    private String password;
    @Value("${plc.client-id}")
    private String clientId;

    @PostConstruct
    public void plcGather() {
        int qos = 0;

        Thread thread = new Thread(new Runnable() {
            @Override
            public void run() {
                MqttClient client = null;
                try {
                    client = new MqttClient(broker, clientId, new MemoryPersistence());
                    // 连接参数
                    MqttConnectOptions options = new MqttConnectOptions();
                    options.setUserName(username);
                    options.setPassword(password.toCharArray());
                    options.setConnectionTimeout(60);
                    options.setKeepAliveInterval(60);
                    // 设置回调
                    client.setCallback(new MqttCallback() {

                        public void connectionLost(Throwable cause) {
                            System.out.println("connectionLost: " + cause.getMessage());
                        }

                        public void messageArrived(String topic, MqttMessage message) {

                            String data = new String(message.getPayload());

                            kafkaTemplate.send(plc1,data).addCallback(success ->{
                                // 消息发送到的topic
                                String kafkaTopic = success.getRecordMetadata().topic();
                                // 消息发送到的分区
//                                int partition = success.getRecordMetadata().partition();
                                // 消息在分区内的offset
//                                long offset = success.getRecordMetadata().offset();
                                log.info("mqtt成功将消息:{},转入到kafka主题->{}", data,kafkaTopic);
                            },failure ->{
                                throw new RuntimeException("发送消息失败:" + failure.getMessage());
                            });
                        }

                        public void deliveryComplete(IMqttDeliveryToken token) {
                            log.info("deliveryComplete---------{}", token.isComplete());
                        }

                    });
                    client.connect(options);
                    client.subscribe(subscribeTopic, qos);
                } catch (MqttException e) {
                    e.printStackTrace();
                }
            }
        });

        thread.start();
    }
}
  • 采集报文测试（如下图表示成功，并且已经发送到了kafka主题上）

Flink接收kafka数据

  • maven pom
<!--工具类 开始-->
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.83</version>
        </dependency>
        <dependency>
            <groupId>org.apache.commons</groupId>
            <artifactId>commons-collections4</artifactId>
            <version>4.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.commons</groupId>
            <artifactId>commons-lang3</artifactId>
        </dependency>
        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.26</version>
        </dependency>
        <!--工具类 结束-->

        <!-- flink依赖引入 开始-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.13.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>1.13.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>1.13.1</version>
        </dependency>
        <!-- flink连接kafka -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_2.11</artifactId>
            <version>1.13.1</version>
        </dependency>
        <!-- flink连接es-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-json</artifactId>
            <version>1.13.1</version>
        </dependency>
        <!-- flink连接mysql-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-jdbc_2.11</artifactId>
            <version>1.10.0</version>
        </dependency>
        <!-- flink依赖引入 结束-->

        <!--spring data jpa-->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-jpa</artifactId>
        </dependency>
  • yaml配置
# 服务接口
server:
  port: 8222

spring:
  kafka:
    bootstrap-servers: ip:9092
    consumer:
      group-id: kafka
      key-deserializer: org.apache.kafka.common.serialization.StringDeserializer
      value-deserializer: org.apache.kafka.common.serialization.StringDeserializer

  datasource:
    url:  jdbc:mysql://127.0.0.01:3306/ceshi?characterEncoding=UTF-8&useUnicode=true&useSSL=false&tinyInt1isBit=false&allowPublicKeyRetrieval=true&serverTimezone=Asia/Shanghai
    driver-class-name:  com.mysql.cj.jdbc.Driver
    username: root
    password: root
    druid:
      initial-size: 5 #初始化时建立物理连接的个数
      min-idle: 5 #最小连接池数量
      maxActive: 20 #最大连接池数量
      maxWait: 60000 #获取连接时最大等待时间，单位毫秒
      timeBetweenEvictionRunsMillis: 60000 #配置间隔多久才进行一次检测，检测需要关闭的空闲连接，单位是毫秒
      minEvictableIdleTimeMillis: 300000 #配置一个连接在池中最小生存的时间，单位是毫秒
      validationQuery: SELECT 1 #用来检测连接是否有效的sql
      testWhileIdle: true #申请连接的时候检测，如果空闲时间大于timeBetweenEvictionRunsMillis，执行validationQuery检测连接是否有效
      testOnBorrow: false #申请连接时执行validationQuery检测连接是否有效，如果为true会降低性能
      testOnReturn: false #归还连接时执行validationQuery检测连接是否有效，如果为true会降低性能
      poolPreparedStatements: true # 打开PSCache，并且指定每个连接上PSCache的大小
      maxPoolPreparedStatementPerConnectionSize: 20 #要启用PSCache，必须配置大于0，当大于0时，poolPreparedStatements自动触发修改为true。在Druid中，不会存在Oracle下PSCache占用内存过多的问题，可以把这个数值配置大一些，比如说100
      filters: stat,wall,slf4j #配置监控统计拦截的filters，去掉后监控界面sql无法统计，'wall'用于防火墙
      #通过connectProperties属性来打开mergeSql功能；慢SQL记录
      connectionProperties: druid.stat.mergeSql\=true;druid.stat.slowSqlMillis\=5000

  jpa:
    hibernate:
      ddl-auto: none
    show-sql: true
    repositories:
      packages: com.hzh.demo.domain.*

#自定义配置
customer:
  #flink相关配置
  flink:
    # 功能开关
    plc-status: true
    plc-topic: plc_thoroughfare

# 定时任务定时清理失效数据
task:
  plc-time: 0 0/1 * * * ?
  • 表结构
-- plc_test definition
CREATE TABLE `plc_test` (
                            `pkid` varchar(32) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '主键id',
                            `json_str` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT 'json格式数据',
                            `create_time` bigint NOT NULL COMMENT '创建时间',
                            PRIMARY KEY (`pkid`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci COMMENT='plc存储数据测试表';
  • 启动类
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.boot.autoconfigure.domain.EntityScan;
import org.springframework.data.jpa.repository.config.EnableJpaRepositories;
import org.springframework.scheduling.annotation.EnableScheduling;

@SpringBootApplication
@EnableJpaRepositories(basePackages = "repository basePackages")
@EntityScan("entity basePackages")
@EnableScheduling
public class PLCStorageApplication {

    public static void main(String[] args) {
        SpringApplication.run(PLCStorageApplication.class, args);
    }
}
  • 实体类
import lombok.Builder;
import lombok.Data;

import javax.persistence.Column;
import javax.persistence.Entity;
import javax.persistence.Id;
import javax.persistence.Table;
import java.io.Serializable;

/**
 * PLC接收实体
 */
@Table(name = "plc_test")
@Data
@Builder
@Entity
public class PLCDomain implements Serializable {

    private static final long serialVersionUID = 4122384962907036649L;

    @Id
    @Column(name = "pkid")
    public String id;
    @Column(name = "json_str")
    public String jsonStr;
    @Column(name = "create_time")
    private Long createTime;

    public PLCDomain(String id, String jsonStr,Long createTime) {
        this.id = id;
        this.jsonStr = jsonStr;
        this.createTime = createTime;
    }

    public PLCDomain() {

    }
}
  • jpa 接口
import com.hzh.demo.domain.PLCDomain;
import org.springframework.data.jpa.repository.JpaRepository;
import org.springframework.stereotype.Repository;

@Repository
public interface PLCRepository extends JpaRepository<PLCDomain,String> {

}
  • 封装获取上下文工具类（ApplicationContextAware）由于加载先后顺序，flink无法使用spring bean注入的方式，特此封装工具类
import org.springframework.beans.BeansException;
import org.springframework.context.ApplicationContext;
import org.springframework.context.ApplicationContextAware;
import org.springframework.context.i18n.LocaleContextHolder;
import org.springframework.stereotype.Component;

@Component
public class ApplicationContextProvider
        implements ApplicationContextAware {
    /**
     * 上下文对象实例
     */
    private static ApplicationContext applicationContext;

    /**
     * 获取applicationContext
     *
     * @return
     */
    public static ApplicationContext getApplicationContext() {
        return applicationContext;
    }

    @Override
    public void setApplicationContext(ApplicationContext applicationContext) throws BeansException {
        ApplicationContextProvider.applicationContext = applicationContext;
    }

    /**
     * 通过name获取 Bean.
     *
     * @param name
     * @return
     */
    public static Object getBean(String name) {
        return getApplicationContext().getBean(name);
    }

    /**
     * 通过class获取Bean.
     *
     * @param clazz
     * @param <T>
     * @return
     */
    public static <T> T getBean(Class<T> clazz) {
        return getApplicationContext().getBean(clazz);
    }

    /**
     * 通过name,以及Clazz返回指定的Bean
     *
     * @param name
     * @param clazz
     * @param <T>
     * @return
     */
    public static <T> T getBean(String name, Class<T> clazz) {
        return getApplicationContext().getBean(name, clazz);
    }

    /**
     * 描述 : <获得多语言的资源内容>. <br>
     * <p>
     * <使用方法说明>
     * </p>
     *
     * @param code
     * @param args
     * @return
     */
    public static String getMessage(String code, Object[] args) {
        return getApplicationContext().getMessage(code, args, LocaleContextHolder.getLocale());
    }

    /**
     * 描述 : <获得多语言的资源内容>. <br>
     * <p>
     * <使用方法说明>
     * </p>
     *
     * @param code
     * @param args
     * @param defaultMessage
     * @return
     */
    public static String getMessage(String code, Object[] args,
                                    String defaultMessage) {
        return getApplicationContext().getMessage(code, args, defaultMessage,
                LocaleContextHolder.getLocale());
    }
}
  • FIink 第三方输出（mysql写入）
import com.hzh.demo.config.ApplicationContextProvider;
import com.hzh.demo.domain.PLCDomain;
import com.hzh.demo.repository.PLCRepository;
import org.apache.flink.streaming.api.functions.sink.SinkFunction;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
import org.springframework.stereotype.Component;

import java.util.UUID;

/**
 * 向mysql写入数据
 */
@Component
@ConditionalOnProperty(name = "customer.flink.plc-status")
public class MysqlSink implements SinkFunction<String> {

    private static final Logger log = LoggerFactory.getLogger(MysqlSink.class);

    @Override
    public void invoke(String value, Context context) throws Exception {
        long currentTime = context.currentProcessingTime();
        PLCDomain build = PLCDomain.builder()
                .id(UUID.randomUUID().toString().replaceAll("-", ""))
                .jsonStr(value)
                .createTime(currentTime)
                .build();

        PLCRepository repository = ApplicationContextProvider.getBean(PLCRepository.class);
        repository.save(build);
        log.info("持久化写入：{}",build);
        SinkFunction.super.invoke(value, context);
    }
}
  • Flink订阅kafka topic读取持续数据
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
import org.springframework.stereotype.Component;

import javax.annotation.PostConstruct;
import java.util.Properties;

/**
 * 接收 kafka topic 读取数据
 */
@Component
@ConditionalOnProperty(name = "customer.flink.plc-status")
public class FlinkReceivingPLC {
    private static final Logger log = LoggerFactory.getLogger(MyKeyedProcessFunction.class);
    @Value("${spring.kafka.bootstrap-servers:localhost:9092}")
    private String kafkaServer;
    @Value("${customer.flink.plc-topic}")
    private String topic;
    @Value("${spring.kafka.consumer.group-id:kafka}")
    private String groupId;
    @Value("${spring.kafka.consumer.key-deserializer:org.apache.kafka.common.serialization.StringDeserializer}")
    private String keyDeserializer;
    @Value("${spring.kafka.consumer.value-deserializer:org.apache.kafka.common.serialization.StringDeserializer}")
    private String valueDeserializer;

    /**
     * 执行方法
     *
     * @throws Exception 异常
     */
    @PostConstruct
    public void execute(){
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.enableCheckpointing(5000);
        //设定全局并发度
        env.setParallelism(1);
        Properties properties = new Properties();
        //kafka的节点的IP或者hostName，多个使用逗号分隔
        properties.setProperty("bootstrap.servers", kafkaServer);
        //kafka的消费者的group.id
        properties.setProperty("group.id", groupId);
        properties.setProperty("key-deserializer",keyDeserializer);
        properties.setProperty("value-deserializer",valueDeserializer);

        FlinkKafkaConsumer<String> myConsumer = new FlinkKafkaConsumer<>(topic, new SimpleStringSchema(), properties);

        DataStream<String> stream = env.addSource(myConsumer);
        stream.print().setParallelism(1);

        stream
                //分组
                .keyBy(new KeySelector<String, String>() {
                    @Override
                    public String getKey(String value) throws Exception {
                        return value;
                    }
                })
                //指定处理类
//                .process(new MyKeyedProcessFunction())
                //数据第三方输出，mysql持久化
                .addSink(new MysqlSink());

        //启动任务
        new Thread(() -> {
            try {
                env.execute("PLCPersistenceJob");
            } catch (Exception e) {
                log.error(e.toString(), e);
            }
        }).start();
    }
}
  • 失效数据清理机制（为了方便测试，所以清理机制执行频率高且数据失效低）
import com.hzh.demo.repository.PLCRepository;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Configuration;
import org.springframework.scheduling.annotation.Scheduled;
import org.springframework.stereotype.Component;

import java.util.Optional;

/**
 * 定时任务配置
 */
@Component
@Configuration
public class QutrzConfig {

    private static final Logger log = LoggerFactory.getLogger(QutrzConfig.class);

    @Autowired
    private PLCRepository plcRepository;

    /**
     * 数据清理机制
     */
    @Scheduled(cron = "${task.plc-time}")
    private void PLCCleaningMechanism (){

        log.info("执行数据清理机制：{}","PLCCleaningMechanism");

        long currentTimeMillis = System.currentTimeMillis();
        Optional.of(this.plcRepository.findAll()).ifPresent(list ->{
            list.forEach(plc ->{
                Long createTime = plc.getCreateTime();

                //大于1分钟为失效数据
                if ((currentTimeMillis - createTime) > (1000 * 60 * 1) ){
                    this.plcRepository.delete(plc);
                    log.info("过期数据已经被清理：{}",plc);
                }
            });
        });
    }
}
  • 测试结果

  • mysql入库数据

标签： flink 大数据 物联网
本文转载自: https://blog.csdn.net/scdncby/article/details/129713364
版权归原作者 斯普润布特 所有， 如有侵权，请联系我们删除。
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