FlinkSQL实现滚动窗口和滑动窗口

实现滚动窗口

package cn._51doit.flink.day11;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.streaming.api.datastream.DataStream;
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.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

import static org.apache.flink.table.api.Expressions.$;

/**
 * 使用SQL的方式，实现滚动窗口聚合
 */
public class SqlTumblingEventTimeWindow {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        //为了触发方便，并行度设置为1
        env.setParallelism(1);

        //如果希望能够容错，要开启checkpoint
        env.enableCheckpointing(10000);

        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

        //1000,u1,p1,5
        //2000,u1,p1,5
        //2000,u2,p1,3
        //2000,u2,p2,8
        //9999,u1,p1,5
        //18888,u2,p1,3
        //19999,u2,p1,3
        DataStreamSource socketTextStream = env.socketTextStream("localhost", 8888);

        SingleOutputStreamOperator rowDataStream = socketTextStream.map(
                new MapFunction() {
                    @Override
                    public Row map(String line) throws Exception {
                        String[] fields = line.split(",");
                        Long time = Long.parseLong(fields[0]);
                        String uid = fields[1];
                        String pid = fields[2];
                        Double money = Double.parseDouble(fields[3]);
                        return Row.of(time, uid, pid, money);

                    }
                }).returns(Types.ROW(Types.LONG, Types.STRING, Types.STRING, Types.DOUBLE));

        //提取数据中的EventTime并生成WaterMark
        DataStream waterMarksRow = rowDataStream.assignTimestampsAndWatermarks(
                new BoundedOutOfOrdernessTimestampExtractor(Time.seconds(0)) {
                    @Override
                    public long extractTimestamp(Row row) {
                        return (long) row.getField(0);
                    }
                });
        //将DataStream注册成表并指定schema信息
        //$("etime").rowtime()，将数据流中的EVENT Time取名为etime
        tableEnv.createTemporaryView("t_orders", waterMarksRow, $("time"), $("uid"), $("pid"), $("money"), $("etime").rowtime());//最后一个字段叫什么都可以，rowtime()方法用来提取eventTime，相对的，proctime()方法用来拿到processingTime；而第一个time其实也是eventTime，不要也没有关系


        //将同一个滚动窗口内，相同用户id的money进行sum操作(没有取出窗口的起始时间、结束时间)
        //String sql = "SELECT uid, SUM(money) total_money FROM t_orders GROUP BY TUMBLE(etime, INTERVAL '10' SECOND), uid";


        //使用SQL实现按照EventTime划分滚动窗口聚合
        String sql = "SELECT uid, SUM(money) total_money, TUMBLE_START(etime, INTERVAL '10' SECOND) as win_start, " +
                "TUMBLE_END(etime, INTERVAL '10' SECOND) as win_end " +
                "FROM t_orders GROUP BY TUMBLE(etime, INTERVAL '10' SECOND), uid";//GROUPBY这两个字段表示只会将同一窗口同一用户id的金额聚合


        //waterMarksRow.keyBy(t -> t.getField(1)).window(TumblingEventTimeWindows.of(Time.seconds(10))).sum(3)
        Table table = tableEnv.sqlQuery(sql);

        //使用TableEnv将table转成AppendStream
        DataStream result = tableEnv.toAppendStream(table, Row.class);
        result.print();
        env.execute();
    }

}

//in:
//1000,u1,p1,5 
//2000,u1,p1,5 
//2000,u2,p1,3 
//2000,u2,p2,8 
//9999,u1,p1,5 

//out
//+I[U1,15.0,1970-01-01T00:00,1970-01-01T00:00:10]
//+I[U2,11.0,1970-01-01T00:00,1970-01-01T00:00:10]

//in 
//18888,u2,p1,3 
//19999,u2,p1,3

//out
//+I[u2,6.0,1970-01-01T00:00:10,1970-01-01T00:00:20]

 

实现滑动窗口

package cn._51doit.flink.day11;

import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

import java.time.Duration;

import static org.apache.flink.table.api.Expressions.$;

public class SqlSlidingEventTimeWindows {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        env.setParallelism(1);

        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

        //1000,u1,p1,5
        //2000,u1,p1,5
        //2000,u2,p1,3
        //3000,u1,p1,5
        //4000,u1,p1,5
        //9999,u2,p1,3
        DataStreamSource lines = env.socketTextStream("localhost", 8888);

        DataStream rowDataStream = lines.map(new MapFunction() {
            @Override
            public Row map(String line) throws Exception {
                String[] fields = line.split(",");
                Long time = Long.parseLong(fields[0]);
                String uid = fields[1];
                String pid = fields[2];
                Double money = Double.parseDouble(fields[3]);
                return Row.of(time, uid, pid, money);

            }
        }).returns(Types.ROW(Types.LONG, Types.STRING, Types.STRING, Types.DOUBLE));

        //提取数据中的EventTime并生成WaterMark
        DataStream waterMarksRow = rowDataStream.assignTimestampsAndWatermarks(WatermarkStrategy.forBoundedOutOfOrderness(Duration.ofMillis(0)).withTimestampAssigner((r, t) -> (long) r.getField(0)));
        //将DataStream注册成表并指定schema信息
        //tableEnv.registerDataStream("t_orders", waterMarksRow, "time, uid, pid, money, rowtime.rowtime");

        tableEnv.createTemporaryView("t_orders", waterMarksRow, $("time"), $("uid"), $("pid"), $("money"), $("rowtime").rowtime());
        //使用SQL实现按照EventTime划分滑动窗口聚合，长度是10s，2s滑动一次
        String sql = "SELECT uid, SUM(money) total_money, HOP_START(rowtime, INTERVAL '2' SECOND, INTERVAL '10' SECOND) as winStart, HOP_END(rowtime, INTERVAL '2' SECOND, INTERVAL '10' SECOND) as widEnd" +
                " FROM t_orders GROUP BY HOP(rowtime, INTERVAL '2' SECOND, INTERVAL '10' SECOND), uid";
        Table table = tableEnv.sqlQuery(sql);
        //使用TableEnv将table转成AppendStream
        DataStream result = tableEnv.toAppendStream(table, Row.class);
        result.print();
        env.execute();
    }
}

I[U1,5.0,1969-12-31T23:59:52,1970-01-01T00:00:02]
I[U1,10.0,1969-12-31T23:59:54,1970-01-01T00:00:04]
I[U2,11.0,1969-12-31T23:59:54,1970-01-01T00:00:04]
I[U2,11.0,1969-12-31T23:59:56,1970-01-01T00:00:06]
I[U1,10.0,1969-12-31T23:59:56,1970-01-01T00:00:06]
I[U2,11.0,1969-12-31T23:59:58,1970-01-01T00:00:08]
I[U1,10.0,1969-12-31T23:59:58,1970-01-01T00:00:08]