Flink中的事件时间和处理时间有什么区别？为什么事件时间在流计算中很重要？

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;

public class EventTimeExample {

    public static void main(String[] args) throws Exception {
        // 创建流处理环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 设置事件时间
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        // 创建DataStream，从Kafka中接收用户访问数据流
        DataStream<UserVisitEvent> visitStream = env.addSource(new KafkaSource<>())
                .assignTimestampsAndWatermarks(new UserVisitEventTimestampExtractor());

        // 使用事件时间计算每分钟的访问量
        DataStream<Tuple2<String, Long>> minuteCountStream = visitStream
                .keyBy(UserVisitEvent::getMinute)
                .window(TumblingEventTimeWindows.of(Time.minutes(1)))
                .apply(new MinuteCountFunction());

        // 打印每分钟的访问量
        minuteCountStream.print();

        // 执行流处理任务
        env.execute("Event Time Example");
    }
}

class UserVisitEvent {
    private String page;
    private String minute;

    // 省略构造函数、getter和setter
}

class UserVisitEventTimestampExtractor extends BoundedOutOfOrdernessTimestampExtractor<UserVisitEvent> {
    public UserVisitEventTimestampExtractor() {
        super(Time.seconds(10)); // 设置最大延迟时间为10秒
    }

    @Override
    public long extractTimestamp(UserVisitEvent event) {
        // 返回事件的时间戳
        return event.getTimestamp();
    }
}

class MinuteCountFunction implements WindowFunction<UserVisitEvent, Tuple2<String, Long>, String, TimeWindow> {
    @Override
    public void apply(String minute, TimeWindow window, Iterable<UserVisitEvent> events, Collector<Tuple2<String, Long>> out) {
        // 计算窗口中的访问量
        long count = 0;
        for (UserVisitEvent event : events) {
            count++;
        }

        // 输出结果
        out.collect(new Tuple2<>(minute, count));
    }
}