织梦网站修改教程视频,网站404页面优化,类wordpress,闵行营销型网站建设公司一、目的
由于部分数据类型频率为1s#xff0c;从而数据规模特别大#xff0c;因此完整的JSON放在Hive中解析起来#xff0c;尤其是在单机环境下#xff0c;效率特别慢#xff0c;无法满足业务需求。
而Flume的拦截器并不能很好的转换数据#xff0c;因为只能采用Java方…一、目的
由于部分数据类型频率为1s从而数据规模特别大因此完整的JSON放在Hive中解析起来尤其是在单机环境下效率特别慢无法满足业务需求。
而Flume的拦截器并不能很好的转换数据因为只能采用Java方式从Kafka的主题A中采集数据并解析字段然后写入到放在Kafka主题B中
二 、原始数据格式
JSON格式比较正常对象中包含数组
{ deviceNo: 39, sourceDeviceType: null, sn: null, model: null, createTime: 2024-09-03 14:10:00, data: { cycle: 300, evaluationList: [{ laneNo: 1, laneType: null, volume: 3, queueLenMax: 11.43, sampleNum: 0, stopAvg: 0.54, delayAvg: 0.0, passRate: 0.0, travelDist: 140.0, travelTimeAvg: 0.0 }, { laneNo: 2, laneType: null, volume: 7, queueLenMax: 23.18, sampleNum: 0, stopAvg: 0.47, delayAvg: 10.57, passRate: 0.0, travelDist: 140.0, travelTimeAvg: 0.0 }, { laneNo: 3, laneType: null, volume: 9, queueLenMax: 11.54, sampleNum: 0, stopAvg: 0.18, delayAvg: 9.67, passRate: 0.0, travelDist: 140.0, travelTimeAvg: 0.0 }, { laneNo: 4, laneType: null, volume: 6, queueLenMax: 11.36, sampleNum: 0, stopAvg: 0.27, delayAvg: 6.83, passRate: 0.0, travelDist: 140.0, travelTimeAvg: 0.0 }] } }
三、Java代码
package com.kgc;import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.apache.kafka.common.serialization.StringSerializer;
import java.time.Duration;
import java.util.Collections;
import java.util.Properties;public class KafkaKafkaEvaluation {// 添加 Kafka Producer 配置private static Properties producerProps() {Properties props new Properties();props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, 192.168.0.70:9092);props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);props.put(ProducerConfig.ACKS_CONFIG, -1);props.put(ProducerConfig.RETRIES_CONFIG, 3);props.put(ProducerConfig.BATCH_SIZE_CONFIG, 16384);props.put(ProducerConfig.LINGER_MS_CONFIG, 1);props.put(ProducerConfig.BUFFER_MEMORY_CONFIG, 33554432);return props;}public static void main(String[] args) {Properties prop new Properties();prop.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, 192.168.0.70:9092);prop.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);prop.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);prop.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, false);prop.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, 1000);prop.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, earliest);// 每一个消费都要定义不同的Group_IDprop.put(ConsumerConfig.GROUP_ID_CONFIG, evaluation_group);KafkaConsumerString, String consumer new KafkaConsumer(prop);consumer.subscribe(Collections.singleton(topic_internal_data_evaluation));ObjectMapper mapper new ObjectMapper();// 初始化 Kafka ProducerKafkaProducerString, String producer new KafkaProducer(producerProps());while (true) {ConsumerRecordsString, String records consumer.poll(Duration.ofMillis(1000));for (ConsumerRecordString, String record : records) {try {JsonNode rootNode mapper.readTree(record.value());System.out.println(原始数据rootNode);String device_no rootNode.get(deviceNo).asText();String source_device_type rootNode.get(sourceDeviceType).asText();String sn rootNode.get(sn).asText();String model rootNode.get(model).asText();String create_time rootNode.get(createTime).asText();String cycle rootNode.get(data).get(cycle).asText();JsonNode evaluationList rootNode.get(data).get(evaluationList);for (JsonNode evaluationItem : evaluationList) {String lane_no evaluationItem.get(laneNo).asText();String lane_type evaluationItem.get(laneType).asText();String volume evaluationItem.get(volume).asText();String queue_len_max evaluationItem.get(queueLenMax).asText();String sample_num evaluationItem.get(sampleNum).asText();String stop_avg evaluationItem.get(stopAvg).asText();String delay_avg evaluationItem.get(delayAvg).asText();String pass_rate evaluationItem.get(passRate).asText();String travel_dist evaluationItem.get(travelDist).asText();String travel_time_avg evaluationItem.get(travelTimeAvg).asText();String outputLine String.format(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,device_no, source_device_type, sn, model, create_time, cycle,lane_no, lane_type,volume,queue_len_max,sample_num,stop_avg,delay_avg,pass_rate,travel_dist,travel_time_avg);// 发送数据到 KafkaProducerRecordString, String producerRecord new ProducerRecord(topic_db_data_evaluation, record.key(), outputLine);producer.send(producerRecord, (RecordMetadata metadata, Exception e) - {if (e ! null) {e.printStackTrace();} else {System.out.println(The offset of the record we just sent is: metadata.offset());}});}} catch (Exception e) {e.printStackTrace();}}consumer.commitAsync();}}}1、服务器IP都是 192.168.0.70
2、消费Kafka主题数据源topic_internal_data_evaluation
3、生产Kafka主题目标源topic_db_data_evaluation
4、注意字段顺序与ODS层表结构字段顺序一致
四、开启Kafka主题topic_db_data_evaluation消费者
[rootlocalhost bin]# ./kafka-console-consumer.sh --bootstrap-server 192.168.0.70:9092 --topic topic_db_data_evaluation --from-beginning
五、运行测试
1、启动项目 2、消费者输出数据 然后再用Flume采集写入HDFS就行了不过ODS层表结构需要转变
六、ODS层新表结构
create external table if not exists hurys_dc_ods.ods_evaluation(device_no string COMMENT 设备编号,source_device_type string COMMENT 设备类型,sn string COMMENT 设备序列号 ,model string COMMENT 设备型号,create_time timestamp COMMENT 创建时间,cycle int COMMENT 评价数据周期,lane_no int COMMENT 车道编号,lane_type int COMMENT 车道类型 0:渠化1:来向2:出口3:去向4:左弯待转区5:直行待行区6:右转专用道99:未定义车道,volume int COMMENT 车道内过停止线流量辆,queue_len_max float COMMENT 车道内最大排队长度m,sample_num int COMMENT 评价数据计算样本量,stop_avg float COMMENT 车道内平均停车次数次,delay_avg float COMMENT 车道内平均延误时间s,pass_rate float COMMENT 车道内一次通过率,travel_dist float COMMENT 车道内检测行程距离m,travel_time_avg float COMMENT 车道内平均行程时间
)
comment 评价数据外部表——静态分区
partitioned by (day string)
row format delimited fields terminated by ,
stored as SequenceFile
;
七、Flume采集配置文件 八、运行Flume任务检查HDFS文件、以及ODS表数据
--刷新表分区
msck repair table ods_evaluation;
--查看表分区
show partitions hurys_dc_ods.ods_evaluation;
--查看表数据
select * from hurys_dc_ods.ods_evaluation
where day2024-09-03;
搞定这样就不需要在Hive中解析JSON数据了
