一、目的
由于Hive是单机环境，因此庞大的原始JSON数据在Hive中解析的话就太慢了，必须放在Hive之前解析成一个个字段、一条条CSV数据
二、IDEA创建SpringBoot项目
三、项目中各个文件
3.1 pom.xml
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.hurys</groupId>
<artifactId>hurrys-jw-kafka</artifactId>
<version>1.0.0</version>
<properties>
<maven.compiler.source>8</maven.compiler.source>
<maven.compiler.target>8</maven.compiler.target>
<java.version>1.8</java.version>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<spring-boot.version>2.6.13</spring-boot.version>
</properties>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-dependencies</artifactId>
<version>${spring-boot.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
<exclusions>
<exclusion>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-logging</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-log4j2</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.kafka</groupId>
<artifactId>spring-kafka</artifactId>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.83</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.8.1</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
<encoding>UTF-8</encoding>
</configuration>
</plugin>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
<version>${spring-boot.version}</version>
<executions>
<execution>
<id>repackage</id>
<goals>
<goal>repackage</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>3.2 application.yml
kafka:
servers: 192.168.10.12:9092
server:
port: 9830
spring:
application:
name: jw-kafka
kafka:
bootstrap-servers: ${kafka.servers}
consumer:
group-id: jw-kafka
key-deserializer: org.apache.kafka.common.serialization.StringDeserializer
value-deserializer: org.apache.kafka.common.serialization.StringDeserializer
auto-offset-reset: earliest
producer:
key-serializer: org.apache.kafka.common.serialization.StringSerializer
value-serializer: org.apache.kafka.common.serialization.StringSerializer3.3 log4j2.xml
<?xml version="1.0" encoding="UTF-8"?>
<!--Configuration后面的status，这个用于设置log4j2自身内部的信息输出，可以不设置，当设置成trace时，你会看到log4j2内部各种详细输出-->
<!--monitorInterval：Log4j能够自动检测修改配置 文件和重新配置本身，设置间隔秒数-->
<Configuration status="OFF" monitorInterval="600">
<!--日志级别以及优先级排序: OFF > FATAL > ERROR > WARN > INFO > DEBUG > TRACE > ALL
如果查看DEBUG级别日志，需要修改<RollingFile name="RollingFileInfo"> <ThresholdFilter level="INFO">和<root level="DEBUG">-->
<!--变量配置-->
<Properties>
<!-- 格式化输出：%date表示日期，%thread表示线程名，%-5level：级别从左显示5个字符宽度 %msg：日志消息，%n是换行符-->
<!-- %logger{36} 表示 Logger 名字最长36个字符 -->
<property name="LOG_PATTERN" value="%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %class{36} %M() @%L - %msg%n"/>
<!-- 定义日志存储的路径 -->
<property name="FILE_PATH" value="/home/hurys-log/jw-kafka"/>
<property name="FILE_DAY_PATH" value="/home/hurys-log/jw-kafka/%d{yyyy-MM}/%d{yyyy-MM-dd}"/>
</Properties>
<Appenders>
<!-- 这个输出到控制台的配置-->
<Console name="Console" target="SYSTEM_OUT">
<!--控制台只输出level及以上级别的信息（onMatch），其他的直接拒绝（onMismatch） -->
<ThresholdFilter level="DEBUG" onMatch="ACCEPT" onMismatch="DENY"/>
<!--输出日志的格式-->
<PatternLayout pattern="${LOG_PATTERN}"/>
</Console>
<!-- 这个会打印出所有的info及以下级别的信息，每次大小超过size，则这size大小的日志会自动存入按年份-月份建立的文件夹下面并进行压缩，作为存档-->
<RollingFile name="RollingFileInfo" fileName="${FILE_PATH}/info.log"
filePattern="${FILE_DAY_PATH}/INFO-%d{yyyy-MM-dd}_%i.log.gz">
<!--控制台只输出level及以上级别的信息（onMatch），其他的直接拒绝（onMismatch） -->
<ThresholdFilter level="DEBUG" onMatch="ACCEPT" onMismatch="DENY"/>
<!-- 输出格式 -->
<PatternLayout pattern="${LOG_PATTERN}"/>
<Policies>
<!--interval属性用来指定多久滚动一次，默认是1 hour-->
<TimeBasedTriggeringPolicy modulate="true" interval="1"/>
<!-- 此处为每个文件大小策略限制，使用它一般会在文件中filePattern采用%i模式 -->
<SizeBasedTriggeringPolicy size="100MB"/>
</Policies>
<!-- DefaultRolloverStrategy属性如不设置，则默认为最多同一文件夹下7个文件开始覆盖-->
<DefaultRolloverStrategy max="20">
<!-- 从basePath到日志文件路径%d{yyyy-MM}/%d{yyyy-MM-dd}/INFO-%d{yyyy-MM-dd}_%i.log.gz的maxDepth是3-->
<Delete basePath="${FILE_PATH}" maxDepth="3">
<!-- 这里的age必须和filePattern协调, 后者是精确到dd, 这里就要写成xd, xD就不起作用
另外, 数字最好>2, 否则可能造成删除的时候, 最近的文件还处于被占用状态,导致删除不成功!-->
<IfLastModified age="30d"/>
</Delete>
</DefaultRolloverStrategy>
</RollingFile>
<!-- 这个会打印出所有的warn及以下级别的信息，每次大小超过size，则这size大小的日志会自动存入按年份-月份建立的文件夹下面并进行压缩，作为存档-->
<RollingFile name="RollingFileWarn" fileName="${FILE_PATH}/warn.log"
filePattern="${FILE_DAY_PATH}/WARN-%d{yyyy-MM-dd}_%i.log.gz">
<!--控制台只输出level及以上级别的信息（onMatch），其他的直接拒绝（onMismatch）-->
<ThresholdFilter level="WARN" onMatch="ACCEPT" onMismatch="DENY"/>
<PatternLayout pattern="${LOG_PATTERN}"/>
<Policies>
<!--interval属性用来指定多久滚动一次，默认是1 hour-->
<TimeBasedTriggeringPolicy modulate="true" interval="1"/>
<SizeBasedTriggeringPolicy size="100MB"/>
</Policies>
<!-- DefaultRolloverStrategy属性如不设置，则默认为最多同一文件夹下7个文件开始覆盖-->
<DefaultRolloverStrategy max="15"/>
</RollingFile>
<!-- 这个会打印出所有的error及以下级别的信息，每次大小超过size，则这size大小的日志会自动存入按年份-月份建立的文件夹下面并进行压缩，作为存档-->
<RollingFile name="RollingFileError" fileName="${FILE_PATH}/error.log"
filePattern="${FILE_DAY_PATH}/ERROR-%d{yyyy-MM-dd}_%i.log.gz">
<!--控制台只输出level及以上级别的信息（onMatch），其他的直接拒绝（onMismatch）-->
<ThresholdFilter level="ERROR" onMatch="ACCEPT" onMismatch="DENY"/>
<PatternLayout pattern="${LOG_PATTERN}"/>
<Policies>
<!--interval属性用来指定多久滚动一次，默认是1 hour-->
<TimeBasedTriggeringPolicy interval="1"/>
<SizeBasedTriggeringPolicy size="100MB"/>
</Policies>
<!-- DefaultRolloverStrategy属性如不设置，则默认为最多同一文件夹下7个文件开始覆盖-->
<DefaultRolloverStrategy max="15"/>
</RollingFile>
</Appenders>
<!--Logger节点用来单独指定日志的形式，比如要为指定包下的class指定不同的日志级别等。-->
<!--然后定义loggers，只有定义了logger并引入的appender，appender才会生效-->
<loggers>
<!--过滤掉spring和mybatis的一些无用的DEBUG信息-->
<logger name="org.mybatis" level="error" additivity="false">
<AppenderRef ref="Console"/>
</logger>
<!--监控系统信息-->
<!--若是additivity设为false，则 子Logger 只会在自己的appender里输出，而不会在 父Logger 的appender里输出。-->
<Logger name="org.springframework" level="error" additivity="false">
<AppenderRef ref="Console"/>
</Logger>
<root level="INFO">
<appender-ref ref="Console"/>
<appender-ref ref="RollingFileInfo"/>
<appender-ref ref="RollingFileWarn"/>
<appender-ref ref="RollingFileError"/>
</root>
</loggers>
</Configuration>3.4 KafkaConstants
package com.hurys.kafka.constant;
public interface KafkaConstants {
/**
* 静态排队数据
*/
String TOPIC_INTERNAL_DATA_STATIC_QUEUE = "topic_internal_data_static_queue";
/**
* 动态排队数据
*/
String TOPIC_INTERNAL_DATA_DYNAMIC_QUEUE = "topic_internal_data_dynamic_queue";
}3.5 JsonUtil
package com.hurys.kafka.util;
import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.serializer.SerializerFeature;
public class JsonUtil {
/**
* 将对象转换为 JSON 字符串，不忽略空值字段。
*
* @param object 要序列化的对象
* @return 转换后的 JSON 字符串
*/
public static String objectToJson(Object object) {
return JSON.toJSONString(object, SerializerFeature.WriteMapNullValue);
}
}3.6 KafkaApplication
package com.hurys.kafka;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class KafkaApplication {
public static void main(String[] args) {
SpringApplication.run(KafkaApplication.class, args);
}
}3.7 KafkaServiceListener
package com.hurys.kafka.listener;
import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONArray;
import com.alibaba.fastjson.JSONObject;
import com.hurys.kafka.constant.KafkaConstants;
import com.hurys.kafka.util.JsonUtil;
import lombok.extern.log4j.Log4j2;
import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.stereotype.Service;
import javax.annotation.Resource;
import java.util.List;
/**
* kafka消费service
*
* @author wangjing
* @Date 2024/09/09
*/
@Service
@Log4j2
public class KafkaServiceListener {
@Resource
private KafkaTemplate kafkaTemplate;
// 1、转向比数据
@KafkaListener(topics = KafkaConstants.TOPIC_INTERNAL_DATA_TURN_RATIO)
public void processData(String message) {
try {
JSONObject jsonObject = JSON.parseObject(message);
// System.out.println("原始数据"+JsonUtil.objectToJson(jsonObject));
//获取雷达数据
String device_no = jsonObject.getString("deviceNo");
String source_device_type = jsonObject.getString("sourceDeviceType");
String sn = jsonObject.getString("sn");
String model = jsonObject.getString("model");
String createTime = jsonObject.getString("createTime");
String create_time = createTime.substring(0,19);
JSONObject data = jsonObject.getJSONObject("data");
String cycle = data.getString("cycle");
String volume_sum = data.getString("volumeSum");
String speed_avg = data.getString("speedAvg");
String volume_left = data.getString("volumeLeft");
String speed_left = data.getString("speedLeft");
String volume_straight = data.getString("volumeStraight");
String speed_straight = data.getString("speedStraight");
String volume_right = data.getString("volumeRight");
String speed_right = data.getString("speedRight");
String volume_turn = data.getString("volumeTurn");
String speed_turn = data.getString("speedTurn");
String outputLine = (device_no +","+source_device_type+","+sn+","+model+","+create_time+","+cycle+","+volume_sum+","+speed_avg+","+ volume_left+","+speed_left+","+volume_straight+","+speed_straight+","+volume_right+","+speed_right+","+volume_turn+","+speed_turn);
// System.out.println("outputLine数据1"+outputLine);
kafkaTemplate.send("topic_db_data_turn_ratio", outputLine);
} catch (Exception e) {
log.error("process turn_ratio error", e);
}
}
// 2、静态排队数据
@KafkaListener(topics = KafkaConstants.TOPIC_INTERNAL_DATA_STATIC_QUEUE)
public void processData2(String message) {
try {
JSONObject jsonObject = JSON.parseObject(message);
//获取雷达数据
String device_no = jsonObject.getString("deviceNo");
String source_device_type = jsonObject.getString("sourceDeviceType");
String sn = jsonObject.getString("sn");
String model = jsonObject.getString("model");
String createTime = jsonObject.getString("createTime");
String create_time = createTime.substring(0,19);
JSONObject data = jsonObject.getJSONObject("data");
List<JSONObject> queueList = data.getJSONArray("queueList").toJavaList(JSONObject.class);
for (JSONObject queueItem:queueList) {
String lane_no = queueItem.getString("laneNo");
String lane_type = queueItem.getString("laneType");
String queue_count = queueItem.getString("queueCount");
String queue_len = queueItem.getString("queueLen");
String queue_head = queueItem.getString("queueHead");
String queue_tail = queueItem.getString("queueTail");
String outputLine = ( device_no+","+ source_device_type+","+ sn+","+ model+","+ create_time+","+ lane_no+","+ lane_type+","+ queue_count+","+ queue_len+","+ queue_head+","+queue_tail);
System.out.println("outputLine数据2"+outputLine);
kafkaTemplate.send("topic_db_data_static_queue", outputLine);
}
} catch (Exception e) {
log.error("process static_queue error", e);
}
}
// 7、区域数据
@KafkaListener(topics = KafkaConstants.TOPIC_INTERNAL_DATA_AREA)
public void processData7(String message) {
try {
JSONObject jsonObject = JSON.parseObject(message);
//获取雷达数据
String device_no = jsonObject.getString("deviceNo");
String source_device_type = jsonObject.getString("sourceDeviceType");
String sn = jsonObject.getString("sn");
String model = jsonObject.getString("model");
String createTime = jsonObject.getString("createTime");
String create_time = createTime.substring(0,19);
JSONObject data = jsonObject.getJSONObject("data");
List<JSONObject> areaStatusList = data.getJSONArray("areaStatusList").toJavaList(JSONObject.class);
for (JSONObject areaStatus:areaStatusList) {
String area_no = areaStatus.getString("areaNo");
List<JSONObject> laneStatusList = areaStatus.getJSONArray("laneStatusList").toJavaList(JSONObject.class);
for (JSONObject laneItem : laneStatusList) {
String lane_no = laneItem.getString("laneNo");
String lane_type = laneItem.getString("laneType");
String target_count = laneItem.getString("targetCount");
String space_occupancy = laneItem.getString("spaceOccupancy");
String pareto = laneItem.getString("pareto");
String speed_avg = laneItem.getString("speedAvg");
String speed_head = laneItem.getString("speedHead");
String speed_tail = laneItem.getString("speedTail");
String pos_head = laneItem.getString("posHead");
String pos_tail = laneItem.getString("posTail");
String average_arrival_time = laneItem.getString("averageArrivalTime");
String head_position = laneItem.getString("headPosition");
String tail_position = laneItem.getString("tailPosition");
String outputLine = (device_no + "," + source_device_type + "," + sn+ "," +model+ "," +create_time+ "," + lane_no+ "," + lane_type+ "," + target_count+ "," + space_occupancy+ "," + pareto+ "," + speed_avg+ "," + speed_head+ "," + speed_tail+ "," + pos_head+ "," + pos_tail+ "," + area_no+ "," + average_arrival_time+ "," + head_position+ "," + tail_position);
// System.out.println("outputLine数据7" + outputLine);
kafkaTemplate.send("topic_db_data_area", outputLine);
}
}
} catch (Exception e) {
log.error("process area error", e);
}
}
// 8、统计数据
@KafkaListener(topics = KafkaConstants.TOPIC_INTERNAL_DATA_STATISTICS)
public void processData8(String message) {
try {
JSONObject jsonObject = JSON.parseObject(message);
//获取雷达数据
String device_no = jsonObject.getString("deviceNo");
String source_device_type = jsonObject.getString("sourceDeviceType");
String sn = jsonObject.getString("sn");
String model = jsonObject.getString("model");
String createTime = jsonObject.getString("createTime");
String create_time = createTime.substring(0,19);
JSONObject data = jsonObject.getJSONObject("data");
String cycle = data.getString("cycle");
List<JSONObject> sectionList = data.getJSONArray("sectionList").toJavaList(JSONObject.class);
for (JSONObject sectionStatus:sectionList) {
String section_no = sectionStatus.getString("sectionNo");
List<JSONObject> coilList = sectionStatus.getJSONArray("coilList").toJavaList(JSONObject.class);
for (JSONObject coilItem : coilList) {
String lane_no = coilItem.getString("laneNo");
String lane_type = coilItem.getString("laneType");
String coil_no = coilItem.getString("coilNo");
String volume_sum = coilItem.getString("volumeSum");
String volume_person = coilItem.getString("volumePerson");
String volume_car_non = coilItem.getString("volumeCarNon");
String volume_car_small = coilItem.getString("volumeCarSmall");
String volume_car_middle = coilItem.getString("volumeCarMiddle");
String volume_car_big = coilItem.getString("volumeCarBig");
String speed_avg = coilItem.getString("speedAvg");
String speed_85 = coilItem.getString("speed85");
String time_occupancy = coilItem.getString("timeOccupancy");
String average_headway = coilItem.getString("averageHeadway");
String average_gap = coilItem.getString("averageGap");
String outputLine = (device_no + "," + source_device_type + "," + sn+ "," +model+ "," +create_time+ "," + cycle+ "," + lane_no+ "," + lane_type+ "," + section_no+ "," + coil_no+ "," + volume_sum+ "," + volume_person+ "," + volume_car_non+ "," + volume_car_small+ "," + volume_car_middle+ "," + volume_car_big+ "," + speed_avg+ "," + speed_85+ "," + time_occupancy+ "," + average_headway+ "," + average_gap);
// System.out.println("outputLine数据8" + outputLine);
kafkaTemplate.send("topic_db_data_statistics", outputLine);
}
}
} catch (Exception e) {
log.error("process statistics error", e);
}
}
// 9、事件资源
@KafkaListener(topics = KafkaConstants.TOPIC_INTERNAL_DATA_EVENT_RESOURCE)
public void processData9(String message) {
try {
JSONObject jsonObject = JSON.parseObject(message);
//获取雷达数据
String device_no = jsonObject.getString("deviceNo");
String source_device_type = jsonObject.getString("sourceDeviceType");
String sn = jsonObject.getString("sn");
String model = jsonObject.getString("model");
String createTime = jsonObject.getString("createTime");
String create_time = createTime.substring(0,19);
JSONObject data = jsonObject.getJSONObject("data");
String event_id = data.getString("eventId");
// 获取数组中的第一个元素，并转换为文本
JSONArray pictureArray = data.getJSONArray("picture");
String picture = pictureArray.getString(0);// 获取数组中的第一个元素
// 获取数组中的第一个元素，并转换为文本
JSONArray videoArray = data.getJSONArray("video");
String video = videoArray.getString(0);// 获取数组中的第一个元素
String outputLine = (device_no +","+source_device_type+","+sn+","+model+","+create_time+","+event_id+","+picture+","+ video);
// System.out.println("outputLine数据9" + outputLine);
kafkaTemplate.send("topic_db_data_event_resource", outputLine);
} catch (Exception e) {
log.error("process event_resource error", e);
}
}
// 10、事件数据
@KafkaListener(topics = KafkaConstants.TOPIC_INTERNAL_DATA_EVENT)
public void processData10(String message) {
try {
JSONObject jsonObject = JSON.parseObject(message);
// System.out.println("原始数据"+JsonUtil.objectToJson(jsonObject));
//获取雷达数据
String device_no = jsonObject.getString("deviceNo");
String source_device_type = jsonObject.getString("sourceDeviceType");
String sn = jsonObject.getString("sn");
String model = jsonObject.getString("model");
String createTime = jsonObject.getString("createTime");
String create_time = createTime.substring(0,19);
String event_id = jsonObject.getString("eventId");
String event_type = jsonObject.getString("eventType");
String state = jsonObject.getString("state");
switch (event_type) {
case "QueueOverrun":
// 1、处理QueueOverrun事件
JSONObject data = jsonObject.getJSONObject("data");
String station = data.getString("station");
String flow = data.getString("flow");
List<JSONObject> queueList = data.getJSONArray("queueList").toJavaList(JSONObject.class);
for (JSONObject queueItem:queueList) {
String lane_no = queueItem.getString("laneNo");
String queue_len = queueItem.getString("queueLen");
String geography_head = queueItem.getString("geographyHead");
String geography_tail = queueItem.getString("geographyTail");
String queue_count = queueItem.getString("queueCount");
String speed_avg = queueItem.getString("speedAvg");
String event_type_detail = null;
String area_no = null;
String lane_no_original = null;
String target_id = null;
String target_type = null;
String speed = null;
String limit_speed = null;
String pos_x = null;
String pos_y = null;
String pos_z = null;
String longitude = null;
String latitude = null;
String altitude = null;
String area_num = null;
String space_occupancy = null;
String congestion_grade = null;
String congestion_length = null;
String length = null;
String width = null;
String height = null;
String vehicle_type = null;
String vehicle_color = null;
String plate_type = null;
String plate_color = null;
String plate_number = null;
String outputLine = (device_no+","+ source_device_type+","+sn+","+model+","+ create_time+","+ event_id+","+event_type+","+event_type_detail+","+ state+","+area_no+","+station+","+ flow+","+ lane_no+","+ lane_no_original+","+target_id+","+target_type+","+ queue_len+","+ queue_count+","+ speed+","+ speed_avg+","+limit_speed+","+ pos_x+","+ pos_y+","+ pos_z+","+ geography_head+","+ geography_tail+","+longitude+","+latitude+","+altitude+","+area_num+","+ space_occupancy+","+ congestion_grade+","+ congestion_length+","+length+","+width+","+height+","+ vehicle_type+","+vehicle_color+","+plate_type+","+plate_color+","+ plate_number);
// System.out.println("outputLine数据10"+outputLine);
kafkaTemplate.send("topic_db_data_event", outputLine);
}
break;
case "Debris":
// 12、处理Debris事件
JSONObject data12 = jsonObject.getJSONObject("data");
String event_type_detail12 = null;
String area_no12 = data12.getString("areaNo");
String station12 = data12.getString("station");
String flow12 = null;
String lane_no12 = null;
String lane_no_original12 = null;
String target_id12 = null;
String target_type12 =null;
String queue_len12 = null;
String queue_count12 = null;
String speed12 = null;
String speed_avg12 =null;
String limit_speed12 = null;
String pos_x12 = data12.getString("posX");
String pos_y12 = data12.getString("posY");
String pos_z12 = data12.getString("posZ");
String geography_head12 = null;
String geography_tail12 = null;
String longitude12 = data12.getString("longitude");
String latitude12 = data12.getString("latitude");
String altitude12 = data12.getString("altitude");
String area_num12 = null;
String space_occupancy12 = null;
String congestion_grade12 = null;
String congestion_length12 =null;
String length12 = data12.getString("length");
String width12 = data12.getString("width");
String height12 = data12.getString("height");
String vehicle_type12 = null;
String vehicle_color12 = null;
String plate_type12 = null;
String plate_color12 = null;
String plate_number12 = null;
String outputLine12 = (device_no+","+ source_device_type+","+sn+","+model+","+ create_time+","+ event_id+","+event_type+","+event_type_detail12+"," + state+","+area_no12+"," +station12+","+ flow12+","+ lane_no12+","+ lane_no_original12+","+target_id12+","+target_type12+","+ queue_len12+"," + queue_count12+","+ speed12+","+ speed_avg12+"," +limit_speed12+","+ pos_x12+","+ pos_y12+","+ pos_z12+","+ geography_head12+","+ geography_tail12+"," +longitude12+","+latitude12+","+altitude12+","+area_num12+"," + space_occupancy12+","+ congestion_grade12+","+ congestion_length12+","+length12+"," +width12+","+height12+","+ vehicle_type12+","+vehicle_color12+"," +plate_type12+","+plate_color12+","+ plate_number12);
// System.out.println("outputLine数据22"+outputLine12);
kafkaTemplate.send("topic_db_data_event", outputLine12);
break;
default:
// 默认处理
break;
}
} catch (Exception e) {
log.error("process event error", e);
}
}
}四、启动KafkaApplication任务，可以打开Kafka主题B的消费窗口进行查看
4.1 启动KafkaApplication任务
4.2 打开Kafka主题B的消费窗口
搞定！！！