一、滚动窗口（Tumbling Windows） 滚动窗口有固定的大小，是一种对数据进行平均切片的划分形式。窗口之间没有重叠，也不会有距离，是“首尾相接”的状态。滚动窗口能够基于工夫定义，也能够基于数据个数定义；须要的参数只有一个，就是窗口的大小（window size）。
在sparkstreaming中，滚动窗口须要设置窗口大小和滑动距离，窗口大小和滑动距离都是StreamingContext的间隔时间的倍数，同时窗口大小和滑动距离相等，如：.window(Seconds(10),Seconds(10)) 10秒的窗口大小和10秒的滑动大小，不存在重叠局部
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); //取得JavaStreamingContext JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.minutes(1)); /** * 设置日志的级别： 防止日志反复 */ 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：须要设置窗口大小和滑动距离，窗口大小和滑动距离都是StreamingContext的间隔时间的倍数，同时窗口大小和滑动距离相等。 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(); } }}代码中定义了一个3分钟的工夫窗口和3分钟的滑动大小，运行后果能够看出数据没有呈现重叠，实现了滚动窗口的成果：
二、滑动窗口（Sliding Windows）与滚动窗口相似，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，而是能够“错开”肯定的地位。如果看作一个窗口的静止，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。
在sparkstreaming中，滑动窗口须要设置窗口大小和滑动距离，窗口大小和滑动距离都是StreamingContext的间隔时间的倍数，同时窗口大小和滑动距离不相等，如：.window(Seconds(10),Seconds(5)) 10秒的窗口大小和5秒的流动大小，存在重叠局部
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); //取得JavaStreamingContext JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.minutes(1)); /** * 设置日志的级别： 防止日志反复 */ 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 必须是批处理工夫的整数倍 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(); } }}
数据演进过程解释：