（3）sparkstreaming从kafka接入实时数据流最实现数据可视化

（1）sparkstreaming从kafka接入实时数据流最终实现数据可视化展示，我们先看下整体方案架构：

（2）方案说明：

1）我们通过kafka与各个业务系统的数据对接，将各系统中的数据实时接到kafka；

2）通过sparkstreaming接入kafka数据流，定义时间窗口和计算窗口大小，业务计算逻辑处理；

3）将结果数据写入到mysql；

4）通过可视化平台接入mysql数据库，这里使用的是NBI大数据可视化构建平台；

5）在平台上通过拖拽式构建各种数据应用，数据展示；

（3）代码演示：

定义一个kafka生产者，模拟数据源

package com.producers;

import com.alibaba.fastjson.JSONObject;
import com.pojo.WaterSensor;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;

import java.util.Properties;
import java.util.Random;

/**
 * Created by lj on 2022-07-18.
 */
public class Kafaka_Producer {
    public final static String bootstrapServers = "127.0.0.1:9092";

    public static void main(String[] args) {
        Properties props = new Properties();
        //设置Kafka服务器地址
        props.put("bootstrap.servers", bootstrapServers);
        //设置数据key的序列化处理类
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        //设置数据value的序列化处理类
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        KafkaProducer producer = new KafkaProducer<>(props);

        try {
            int i = 0;
            Random r=new Random();  
            String[] lang = {"flink","spark","hadoop","hive","hbase","impala","presto","superset","nbi"};

            while(true) {
                Thread.sleep(2000);
                WaterSensor waterSensor = new WaterSensor(lang[r.nextInt(lang.length)]+"_kafka",i,i);
                i++;

                String msg = JSONObject.toJSONString(waterSensor);
                System.out.println(msg);
                RecordMetadata recordMetadata = producer.send(new ProducerRecord<>("kafka_data_waterSensor", null, null,  msg)).get();
//                System.out.println("recordMetadata: {"+ recordMetadata +"}");
            }

        } catch (Exception e) {
            System.out.println(e.getMessage());
        }
    }
}

根据业务需要，定义各种消息对象

package com.pojo;

import java.io.Serializable;
import java.util.Date;

/**
 * Created by lj on 2022-07-13.
 */
public class WaterSensor implements Serializable {
    public String id;
    public long ts;
    public int vc;

    public WaterSensor(){

    }

    public WaterSensor(String id,long ts,int vc){
        this.id = id;
        this.ts = ts;
        this.vc = vc;
    }

    public int getVc() {
        return vc;
    }

    public void setVc(int vc) {
        this.vc = vc;
    }

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public long getTs() {
        return ts;
    }

    public void setTs(long ts) {
        this.ts = ts;
    }
}

sparkstreaming数据流计算

package com.examples;

import com.alibaba.fastjson.JSONObject;
import com.pojo.WaterSensor;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.common.TopicPartition;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.VoidFunction2;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.Time;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaInputDStream;
import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka010.ConsumerStrategies;
import org.apache.spark.streaming.kafka010.KafkaUtils;
import org.apache.spark.streaming.kafka010.LocationStrategies;

import java.util.*;

/**
 * Created by lj on 2022-07-18.
 */
public class SparkSql_Kafka {
    private static String appName = "spark.streaming.demo";
    private static String master = "local[*]";
    private static String topics = "kafka_data_waterSensor";
    private static String brokers = "127.0.0.1:9092";

    public static void main(String[] args) {
        //初始化sparkConf
        SparkConf sparkConf = new SparkConf().setMaster(master).setAppName(appName);

        //获得JavaStreamingContext
        JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.minutes(3));

        /**
         * 设置日志的级别： 避免日志重复
         */
        ssc.sparkContext().setLogLevel("ERROR");

        Collection topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
        //kafka相关参数，必要！缺了会报错
        Map kafkaParams = new HashMap<>();
        kafkaParams.put("metadata.broker.list", brokers) ;
        kafkaParams.put("bootstrap.servers", brokers);
        kafkaParams.put("group.id", "group1");
        kafkaParams.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        kafkaParams.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        kafkaParams.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        
        //通过KafkaUtils.createDirectStream(...)获得kafka数据，kafka相关参数由kafkaParams指定
        JavaInputDStream> lines = KafkaUtils.createDirectStream(
                ssc,
                LocationStrategies.PreferConsistent(),
                ConsumerStrategies.Subscribe(topicsSet, kafkaParams)
        );

        JavaDStream mapDStream = lines.map(new Function, WaterSensor>() {
            @Override
            public WaterSensor call(ConsumerRecord s) throws Exception {
                WaterSensor waterSensor = JSONObject.parseObject(s.value().toString(),WaterSensor.class);
                return waterSensor;
            }
        }).window(Durations.minutes(9), Durations.minutes(6));      //指定窗口大小 和 滑动频率 必须是批处理时间的整数倍;

        mapDStream.foreachRDD(new VoidFunction2, Time>() {
            @Override
            public void call(JavaRDD waterSensorJavaRDD, Time time) throws Exception {
                SparkSession spark = JavaSparkSessionSingleton.getInstance(waterSensorJavaRDD.context().getConf());

                Dataset dataFrame = spark.createDataFrame(waterSensorJavaRDD, WaterSensor.class);
                // 创建临时表
                dataFrame.createOrReplaceTempView("log");
                Dataset result = spark.sql("select * from log");
                System.out.println("========= " + time + "=========");
                //输出前20条数据
                result.show();
                
                //数据写入mysql
                writeDataToMysql(result);
            }
        });

        //开始作业
        ssc.start();
        try {
            ssc.awaitTermination();
        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            ssc.close();
        }
    }
}

NBI大数据可视化构建平台对接mysql，构建数据应用：

NBI可视化