二百六十、Java——采集Kafka数据，解析成一条条数据，写入另一Kafka中（复杂JSON）

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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格式比较复杂，对象中包含数组，数组中包含对象

{
    "deviceNo": "39",
    "sourceDeviceType": null,
    "sn": null,
    "model": null,
    "createTime": "2024-09-03 14:40:00",
    "data": {
        "cycle": 300,
        "sectionList": [{
            "sectionNo": 1,
            "coilList": [{
                "laneNo": 1,
                "laneType": null,
                "coilNo": 1,
                "volumeSum": 3,
                "volumePerson": 0,
                "volumeCarNon": 0,
                "volumeCarSmall": 3,
                "volumeCarMiddle": 0,
                "volumeCarBig": 0,
                "speedAvg": 24.15,
                "timeOccupancy": 0.98,
                "averageHeadway": 162.36,
                "averageGap": 161.63,
                "speed85": 38.0
            },
            {
                "laneNo": 8,
                "laneType": null,
                "coilNo": 8,
                "volumeSum": 1,
                "volumePerson": 0,
                "volumeCarNon": 0,
                "volumeCarSmall": 1,
                "volumeCarMiddle": 0,
                "volumeCarBig": 0,
                "speedAvg": 49.43,
                "timeOccupancy": 0.3,
                "averageHeadway": 115.3,
                "averageGap": 115.1,
                "speed85": 49.43
            }]
        }]
    }
}

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 KafkaKafkaStatistics {// 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, "statistics_group");KafkaConsumer<String, String> consumer = new KafkaConsumer<>(prop);consumer.subscribe(Collections.singleton("topic_internal_data_statistics"));ObjectMapper mapper = new ObjectMapper();// 初始化 Kafka ProducerKafkaProducer<String, String> producer = new KafkaProducer<>(producerProps());while (true) {ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(1000));for (ConsumerRecord<String, 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();JsonNode dataNode = rootNode.get("data");String  cycle = dataNode.get("cycle").asText();for (JsonNode sectionStatus : dataNode.get("sectionList")) {String section_no = sectionStatus.get("sectionNo").asText();JsonNode coilList = sectionStatus.get("coilList");for (JsonNode coilItem : coilList) {String lane_no = coilItem.get("laneNo").asText();String lane_type = coilItem.get("laneType").asText();String coil_no = coilItem.get("coilNo").asText();String volume_sum = coilItem.get("volumeSum").asText();String volume_person = coilItem.get("volumePerson").asText();String volume_car_non = coilItem.get("volumeCarNon").asText();String volume_car_small = coilItem.get("volumeCarSmall").asText();String volume_car_middle = coilItem.get("volumeCarMiddle").asText();String volume_car_big = coilItem.get("volumeCarBig").asText();String speed_avg = coilItem.get("speedAvg").asText();String speed_85 = coilItem.get("speed85").asText();String time_occupancy = coilItem.get("timeOccupancy").asText();String average_headway = coilItem.get("averageHeadway").asText();String average_gap = coilItem.get("averageGap").asText();String outputLine = String.format("%s,%s,%s,%s,%s,%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, 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);// 发送数据到 KafkaProducerRecord<String, String> producerRecord = new ProducerRecord<>("topic_db_data_statistics", 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();}}}

剩下的几步参考二百五十九、Java——采集Kafka数据，解析成一条条数据，写入另一Kafka中（一般JSON）这篇博客

四、开启Kafka主题消费者

五、运行测试

六、ODS层新表结构

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

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