Flink学习21---window和Time（四）会话窗口SessionWindow

输入样例数据如下，SessionWinow会把1588510000-1588515000划分为一个窗口，1588526100-1588535000作为第二个窗口，然后分别对每个窗口中的数据进行计算。

 

package flink.review.datastream.E_Window;


import com.demo.flink.countWindow.CountWindow;
import com.demo.flink.timeWindow.SessionWindow;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.assigners.EventTimeSessionWindows;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;


/**
 * 业务场景：指定时间窗口内，统计事件/词汇的次数（热点更新等）
 */
public class SessionWindowReview {
    public static void main(String[] args) throws Exception{
        //1.创建一个 flink steam 程序的执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);  // 设置使用EventTime划分窗口,默认使用ProcessingTime

        // 2. 创建数据源
        DataStreamSource socketTextStream = env.socketTextStream("192.168.***。***", 8888);

        // 指定字段为EventTime, 抽取timestamp
        // AscendingTimestampExtractor适用于elements的时间在每个parallel task里头是单调递增(timestamp monotony)的场景
        SingleOutputStreamOperator lines = socketTextStream.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor(Time.seconds(0)) {
            @Override
            public long extractTimestamp(String element) {
                return Long.parseLong(element.split(",")[0]);
            }
        });

        // 3. Transformation
        SingleOutputStreamOperator> wordAndOne = lines.map(new MapFunction>() {
            @Override
            public Tuple2 map(String s) throws Exception {
                String[] fileds = s.split(",");
                return Tuple2.of(fileds[1], 1);
            }
        });


        SingleOutputStreamOperator> summed =
                wordAndOne.keyBy(0)
                .window(EventTimeSessionWindows.withGap(Time.seconds(10)))
                .sum(1);

        //4. sink
        summed.print();

        //5.执行
        env.execute("TimeTumblingWindowReview");
    }
}