Spark Streaming是构建在Spark Core的RDD根底之上的，与此同时Spark Streaming引入了一个新的概念：DStream（Discretized Stream，离散化数据流)，示意连续不断的数据流。DStream形象是Spark Streaming的流解决模型，在外部实现上，Spark Streaming会对输出数据依照工夫距离（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)解决的 Spark 模块。Spark SQL 的前身是Shark，Shark是基于 Hive 所开发的工具，它批改了下图所示的右下角的内存治理、物理打算、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）定义音讯对象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 发送一次音讯 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); //取得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查问 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); // 创立长期表 dataFrame.createOrReplaceTempView("log"); Dataset<Row> result = spark.sql("select * from log"); System.out.println("========= " + time + "========="); //输入前20条数据 result.show(); } }); //开始作业 ssc.start(); try { ssc.awaitTermination(); } catch (Exception e) { e.printStackTrace(); } finally { ssc.close(); }}}（5）成果演示：
代码中定义的是1分钟的批处理距离，所以每1分钟会触发一次计算：