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: 192.168.10.12:9092server:port: 9830spring:application:name: jw-kafkakafka:bootstrap-servers: ${kafka.servers}consumer:group-id: jw-kafkakey-deserializer: org.apache.kafka.common.serialization.StringDeserializervalue-deserializer: org.apache.kafka.common.serialization.StringDeserializerauto-offset-reset: earliestproducer:key-serializer: org.apache.kafka.common.serialization.StringSerializervalue-serializer: org.apache.kafka.common.serialization.StringSerializer
3.3 log4j2.xml
<?xml version="1.0" encoding="UTF-8"?>
<!--Configuration后面的status，这个用于设置log4j2自身内部的信息输出，可以不设置，当设置成trace时，你会看到log4j2内部各种详细输出-->
<!--monitorInterval：Log4j能够自动检测修改配置 文件和重新配置本身，设置间隔秒数-->
<Configuration status="OFF" monitorInterval="600"><!--日志级别以及优先级排序: OFF > FATAL > ERROR > WARN > INFO > DEBUG > TRACE > ALL如果查看DEBUG级别日志，需要修改<RollingFile name="RollingFileInfo"> <ThresholdFilter level="INFO">和<root level="DEBUG">--><!--变量配置--><Properties><!-- 格式化输出：%date表示日期，%thread表示线程名，%-5level：级别从左显示5个字符宽度 %msg：日志消息，%n是换行符--><!-- %logger{36} 表示 Logger 名字最长36个字符 --><property name="LOG_PATTERN" value="%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %class{36} %M() @%L - %msg%n"/><!-- 定义日志存储的路径 --><property name="FILE_PATH" value="/home/hurys-log/jw-kafka"/><property name="FILE_DAY_PATH" value="/home/hurys-log/jw-kafka/%d{yyyy-MM}/%d{yyyy-MM-dd}"/></Properties><Appenders><!-- 这个输出到控制台的配置--><Console name="Console" target="SYSTEM_OUT"><!--控制台只输出level及以上级别的信息（onMatch），其他的直接拒绝（onMismatch） --><ThresholdFilter level="DEBUG" onMatch="ACCEPT" onMismatch="DENY"/><!--输出日志的格式--><PatternLayout pattern="${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 {@Resourceprivate 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！！！
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