(2)sparkstreaming滚动窗口和滑动窗口演示

一、滚动窗口(Tumbling Windows) 滚动窗口有固定的大小,是一种对数据进行均匀切片的划分方式。窗口之间没有重叠,也不会有间隔,是“首尾相接”的状态。滚动窗口可以基于时间定义,也可以基于数据个数定义;需要的参数只有一个,就是窗口的大小(window size)。
(2)sparkstreaming滚动窗口和滑动窗口演示_第1张图片
在sparkstreaming中,滚动窗口需要设置窗口大小和滑动间隔,窗口大小和滑动间隔都是StreamingContext的间隔时间的倍数,同时窗口大小和滑动间隔相等,如:
.window(Seconds(10),Seconds(10)) 10秒的窗口大小和10秒的滑动大小,不存在重叠部分

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.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.JavaReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;

/**
 * Created by lj on 2022-07-12.
 */
public class SparkSql_Socket_Tumble {
    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));

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

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

        JavaDStream<WaterSensor> mapDStream = lines.map(new Function<String, WaterSensor>() {
            private static final long serialVersionUID = 1L;

            public WaterSensor call(String s) throws Exception {
                String[] cols = s.split(",");
                WaterSensor waterSensor = new WaterSensor(cols[0], Long.parseLong(cols[1]), Integer.parseInt(cols[2]));
                return waterSensor;
            }
        }).window(Durations.minutes(3), Durations.minutes(3));      //滚动窗口:需要设置窗口大小和滑动间隔,窗口大小和滑动间隔都是StreamingContext的间隔时间的倍数,同时窗口大小和滑动间隔相等。

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

                Dataset<Row> dataFrame = spark.createDataFrame(waterSensorJavaRDD, 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();
        }
    }
}

代码中定义了一个3分钟的时间窗口和3分钟的滑动大小,运行结果可以看出数据没有出现重叠,实现了滚动窗口的效果:
(2)sparkstreaming滚动窗口和滑动窗口演示_第2张图片
二、滑动窗口(Sliding Windows)与滚动窗口类似,滑动窗口的大小也是固定的。区别在于,窗口之间并不是首尾相接的,而是可以“错开”一定的位置。如果看作一个窗口的运动,那么就像是向前小步“滑动”一样。定义滑动窗口的参数有两个:除去窗口大小(window size)之外,还有一个滑动步长(window slide),代表窗口计算的频率。
(2)sparkstreaming滚动窗口和滑动窗口演示_第3张图片在sparkstreaming中,滑动窗口需要设置窗口大小和滑动间隔,窗口大小和滑动间隔都是StreamingContext的间隔时间的倍数,同时窗口大小和滑动间隔不相等,如:
.window(Seconds(10),Seconds(5)) 10秒的窗口大小和5秒的活动大小,存在重叠部分

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.*;
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.JavaReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;

import java.util.ArrayList;
import java.util.List;

/**
 * Created by lj on 2022-07-12.
 */
public class SparkSql_Socket {
    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));

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

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

        JavaDStream<WaterSensor> mapDStream = lines.map(new Function<String, WaterSensor>() {
            private static final long serialVersionUID = 1L;

            public WaterSensor call(String s) throws Exception {
                String[] cols = s.split(",");
                WaterSensor waterSensor = new WaterSensor(cols[0], Long.parseLong(cols[1]), Integer.parseInt(cols[2]));
                return waterSensor;
            }
        }).window(Durations.minutes(4), Durations.minutes(2));      //滑动窗口: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<JavaRDD<WaterSensor>, Time>() {
            @Override
            public void call(JavaRDD<WaterSensor> waterSensorJavaRDD, Time time) throws Exception {
                SparkSession spark = JavaSparkSessionSingleton.getInstance(waterSensorJavaRDD.context().getConf());

                Dataset<Row> dataFrame = spark.createDataFrame(waterSensorJavaRDD, 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();
        }
    }
}

(2)sparkstreaming滚动窗口和滑动窗口演示_第4张图片

数据演进过程解释:
(2)sparkstreaming滚动窗口和滑动窗口演示_第5张图片

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