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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）

article 2024/11/15 17:35:39

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_ID
        prop.put(ConsumerConfig.GROUP_ID_CONFIG, "evaluation_group");

        KafkaConsumer<String, String> consumer = new KafkaConsumer<>(prop);
        consumer.subscribe(Collections.singleton("topic_internal_data_evaluation"));
        ObjectMapper mapper = new ObjectMapper();

        // 初始化 Kafka Producer
        KafkaProducer<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();
                    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 Kafka
                        ProducerRecord<String, 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

[root@localhost 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 day='2024-09-03';

搞定，这样就不需要在Hive中解析JSON数据了！！！

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