（1）sparkstreaming结合sparksql读取socket实时数据流

Spark Streaming是构建在Spark Core的RDD基础之上的，与此同时Spark Streaming引入了一个新的概念：DStream（Discretized Stream，离散化数据流)，表示连续不断的数据流。DStream抽象是Spark Streaming的流处理模型，在内部实现上，Spark Streaming会对输入数据按照时间间隔（如1秒）分段，每一段数据转换为Spark中的RDD，这些分段就是Dstream，并且对DStream的操作都最终转变为对相应的RDD的操作。

Spark SQL 是 Spark 用于结构化数据(structured data)处理的 Spark 模块。Spark SQL 的前身是Shark，Shark是基于 Hive 所开发的工具，它修改了下图所示的右下角的内存管理、物理计划、执行三个模块，并使之能运行在 Spark 引擎上。

（1）pom依赖：

 

<dependencies>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_${scala.version}</artifactId>
        <version>${spark.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming_${scala.version}</artifactId>
        <version>${spark.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql_${scala.version}</artifactId>
        <version>${spark.version}</version>
    </dependency>
    <dependency>
        <groupId>org.scala-lang</groupId>
        <artifactId>scala-library</artifactId>
        <version>2.11.11</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
        <version>2.3.1</version>
    </dependency>
    <dependency>
        <groupId>org.apache.kafka</groupId>
        <artifactId>kafka-clients</artifactId>
        <version>2.3.1</version>
    </dependency>
    <dependency>
        <groupId>com.alibaba</groupId>
        <artifactId>fastjson</artifactId>
        <version>1.2.66</version>
    </dependency>
</dependencies>

（2）定义消息对象

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;
    }
}

（3）构建数据生产者

package com.producers;

import java.io.BufferedWriter;
import java.io.IOException;
import java.io.OutputStreamWriter;
import java.net.ServerSocket;
import java.net.Socket;
import java.util.Random;

/**
 * Created by lj on 2022-07-12.
 */
public class Socket_Producer {
    public static void main(String[] args) throws IOException {

        try {
            ServerSocket ss = new ServerSocket(9999);
            System.out.println("启动 server ....");
            Socket s = ss.accept();
            BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(s.getOutputStream()));
            String response = "java,1,2";

            //每 2s 发送一次消息
            int i = 0;
            Random r=new Random();   //不传入种子
            String[] lang = {"flink","spark","hadoop","hive","hbase","impala","presto","superset","nbi"};

            while(true){
                response= lang[r.nextInt(lang.length)]+ i + "," + i + "," + i+"\n";
                System.out.println(response);
                try{
                    bw.write(response);
                    bw.flush();
                    i++;
                }catch (Exception ex){
                    System.out.println(ex.getMessage());
                }
                Thread.sleep(1000 * 30);
            }
        } catch (IOException | InterruptedException e) {
            e.printStackTrace();
        }
    }
}

（4）通过sparkstreaming接入socket数据源，sparksql计算结果打印输出：

package com.examples;

import com.pojo.WaterSensor;
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.Function2;
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.JavaReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;

/**
 * Created by lj on 2022-07-16.
 */
public class SparkSql_Socket1 {
    private static String appName = "spark.streaming.demo";
    private static String master = "local[*]";
    private static String host = "localhost";
    private static int port = 9999;

    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(1));

        //从socket源获取数据
        JavaReceiverInputDStream<String> lines = ssc.socketTextStream(host, port);

        //将 DStream 转换成 DataFrame 并且运行sql查询
        lines.foreachRDD(new VoidFunction2<JavaRDD<String>, Time>() {
            @Override
            public void call(JavaRDD<String> rdd, Time time) {
                SparkSession spark = JavaSparkSessionSingleton.getInstance(rdd.context().getConf());

                //通过反射将RDD转换为DataFrame
                JavaRDD<WaterSensor> rowRDD = rdd.map(new Function<String, WaterSensor>() {
                    @Override
                    public WaterSensor call(String line) {
                        String[] cols = line.split(",");
                        WaterSensor waterSensor = new WaterSensor(cols[0],Long.parseLong(cols[1]),Integer.parseInt(cols[2]));
                        return waterSensor;
                    }
                });

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

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

（5）效果演示：

 

代码中定义的是1分钟的批处理间隔，所以每1分钟会触发一次计算：