Flink中的窗口操作是什么？请解释其作用和使用场景。

import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
public class RealTimeStatistics {
    public static void main(String[] args) throws Exception {
        // 创建流处理环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 创建DataStream，从Kafka中接收用户访问数据流
        DataStream<UserVisitEvent> visitStream = env.addSource(new KafkaSource<>());
        // 使用窗口操作进行实时统计
        DataStream<Tuple2<String, Long>> visitCountStream = visitStream
                .keyBy(UserVisitEvent::getPage)
                .timeWindow(Time.minutes(1))
                .apply(new VisitCountWindowFunction());
        // 打印实时统计结果
        visitCountStream.print();
        // 执行流处理任务
        env.execute("Real-time Statistics");
    }
}
class UserVisitEvent {
    private String page;
    private long timestamp;
    // 省略构造函数、getter和setter
}
class VisitCountWindowFunction implements WindowFunction<UserVisitEvent, Tuple2<String, Long>, String, TimeWindow> {
    @Override
    public void apply(String key, TimeWindow window, Iterable<UserVisitEvent> input, Collector<Tuple2<String, Long>> out) {
        long count = 0;
        for (UserVisitEvent event : input) {
            count++;
        }
        out.collect(new Tuple2<>(key, count));
    }
}