flink重温笔记（十九）： flinkSQL 顶层 API ——FlinkSQL 窗口（解决动态累积数据业务需求）

Flink学习笔记

前言：今天是学习 flink 的第 19 天啦！学习了 flinkSQL 中窗口的应用，包括滚动窗口，滑动窗口，会话窗口，累计窗口，学会了如何计算累计值（类似于中视频计划中的累计播放量业务需求），多维数据分析等大数据热点问题，总结了很多自己的理解和想法，希望和大家多多交流，希望对大家有帮助！

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文章目录

六、FlinkSQL 窗口

1. 窗口表值函数（tvfs）

将流变成特殊的“批”处理，常用的窗口：

滑动窗口
滚动窗口
会话窗口（flink 1.14 版本支持）
累计窗口（flink 1.13 版本新增）

在 flink 1.13 之前，是一个特殊的 GroupWindowFunction

SELECT
    TUMBLE_START( bidtime, INTERVAL '10' MINUTE),
    TUMBLE_END( bidtime, INTERVAL '10' MINUTE),
    TUMBLE_ROWTIME( bidtime, INTERVAL '10' MINUTE),
    SUM(price)
FROM MyTable
GROUP BY TUMBLE( bidtime, INTERVAL '10' MINUTE),

在 flink 1.13 之后，用 Table-Value Function 进行语法标准化

SELECT window_start, window_end, window_time,SUM(price)FROMTABLE(
    TUMBLE(TABLE MyTable, DESCRIPTOR(bidtime),INTERVAL'10' MINUTES))GROUPBY window_start, window_end;

2. 窗口分类函数及聚合操作

2.1 滚动窗口（Tumble Windows）

语法：

TUMBLE(TABLE data,DESCRIPTOR(timecol), size)

data：一个表名。
timecol：是一个列描述符，指示应将数据的哪个时间属性列映射到翻转窗口。
size：是指定滚动窗口宽度的持续时间。

数据：

2021-04-1508:05:00,4.00,C2021-04-1508:07:00,2.00,A2021-04-1508:09:00,5.00,D2021-04-1508:11:00,3.00,B2021-04-1508:13:00,1.00,E2021-04-1508:17:00,6.00,F

需求：现在有一个实时数据看板，需要计算当前每10分钟GMV的总和

packagecn.itcast.day02.Window;importorg.apache.flink.streaming.api.environment.StreamExecutionEnvironment;importorg.apache.flink.table.api.EnvironmentSettings;importorg.apache.flink.table.api.Table;importorg.apache.flink.table.api.bridge.java.StreamTableEnvironment;importorg.apache.flink.types.Row;/**
 * @author lql
 * @time 2024-03-16 17:33:47
 * @description TODO
 */publicclassGroupWindowsSqlTumbleExample{publicstaticvoidmain(String[] args)throwsException{//todo 1）构建flink流处理的运行环境StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();//todo 2）设置并行度
        env.setParallelism(1);//todo 3）构建flink的表的运行环境EnvironmentSettings settings =EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();StreamTableEnvironment tabEnv =StreamTableEnvironment.create(env, settings);String filePath =GroupWindowsSqlTumbleExample.class.getClassLoader().getResource("bid.csv").getPath();
        tabEnv.executeSql("create table Bid("+"bidtime TIMESTAMP(3),"+"price DECIMAL(10, 2), "+"item string,"+"watermark for bidtime as bidtime - interval '1' second) "+"with("+"'connector' = 'filesystem',"+"'path' = 'file:///"+filePath+"',"+"'format' = 'csv'"+")");Table table = tabEnv.sqlQuery(""+"select window_start,window_end,sum(price) as sum_price "+" from table("+"  tumble(table Bid, DESCRIPTOR(bidtime), interval '10' MINUTES))"+"  group by window_start,window_end");

        tabEnv.toAppendStream(table,Row.class).print();
        env.execute();}}

结果：

+I[2021-04-15T08:00,2021-04-15T08:10,11.00]+I[2021-04-15T08:10,2021-04-15T08:20,10.00]

2.2 滑动窗口（Hop Windows）

语法：

HOP(TABLE data,DESCRIPTOR(timecol), slide, size [, offset ])

data：是一个表名。
timecol：是一个列描述符，指示应将数据的哪个时间属性列映射到滑动窗口。
slide：是一个持续时间，指定了连续跳跃窗口开始之间的持续时间
size：是指定跳变窗口宽度的持续时间

需求：每隔 5 分钟，统计 10 分钟的数据

package cn.itcast.day02.Window;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

/**
 * @author lql
 * @time 2024-03-16 19:28:30
 * @description TODO
 */
public class GroupWindowsSqlHopExample {
    public static void main(String[] args) throws Exception {
        //todo 1）构建flink流处理的运行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        //todo 2）设置并行度
        env.setParallelism(1);

        //todo 3）构建flink的表的运行环境
        EnvironmentSettings settings = EnvironmentSettings.newInstance()
                .useBlinkPlanner()
                .inStreamingMode()
                .build();
        StreamTableEnvironment tabEnv = StreamTableEnvironment.create(env, settings);

        String filePath = GroupWindowsSqlHopExample.class.getClassLoader().getResource("bid.csv").getPath();
        tabEnv.executeSql("create table Bid(" +
                "bidtime TIMESTAMP(3)," +
                "price DECIMAL(10, 2), " +
                "item string," +
                "watermark for bidtime as bidtime - interval '1' second) " +
                "with("
                + "'connector' = 'filesystem',"
                + "'path' = 'file:///"+filePath+"',"
                + "'format' = 'csv'"
                + ")");

        Table table = tabEnv.sqlQuery("" +
                "select window_start,window_end,sum(price) as sum_price " +
                " from table(" +
                "  hop(table Bid, DESCRIPTOR(bidtime), interval '5' MINUTES, interval '10' MINUTES))" +
                "  group by window_start,window_end");

        tabEnv.toAppendStream(table, Row.class).print();
        env.execute();

    }
}

结果：

+I[2021-04-15T08:00,2021-04-15T08:10,11.00]+I[2021-04-15T08:05,2021-04-15T08:15,15.00]+I[2021-04-15T08:10,2021-04-15T08:20,10.00]+I[2021-04-15T08:15,2021-04-15T08:25,6.00]

2.3 会话窗口（Session Windows，暂不支持 Window TVF）

Flink1.13 版本中不支持 Window TVF，预计在 flink1.14 版本中支持；

需求：用老版本实现，定义 Session Gap 为3分钟，一个窗口最后一条数据之后的三分钟内没有新数据出现，则该窗口关闭，再之后的数据被归为下一个窗口

packagecn.itcast.day02.Window;importorg.apache.flink.streaming.api.environment.StreamExecutionEnvironment;importorg.apache.flink.table.api.bridge.java.StreamTableEnvironment;/**
 * @author lql
 * @time 2024-03-16 19:37:20
 * @description TODO
 */publicclassGroupWindowsSqlSessionExample{publicstaticvoidmain(String[] args){StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);StreamTableEnvironment tEnv =StreamTableEnvironment.create(env);String filePath =GroupWindowsSqlSessionExample.class.getClassLoader().getResource("bid.csv").getPath();// 作为事件时间的字段必须是 timestamp 类型, 所以根据 long 类型的 ts 计算出来一个 t
        tEnv.executeSql("create table Bid("+"bidtime TIMESTAMP(3),"+"price DECIMAL(10, 2), "+"item string,"+"watermark for bidtime as bidtime - interval '1' second) "+"with("+"'connector' = 'filesystem',"+"'path' = 'file:///"+filePath+"',"+"'format' = 'csv'"+")");

        tEnv.sqlQuery("SELECT "+"  SESSION_START(bidtime, INTERVAL '3' minute) as wStart,  "+"  SESSION_END(bidtime, INTERVAL '3' minute) as wEnd,  "+"  SUM(price) sum_price "+"FROM Bid "+"GROUP BY SESSION(bidtime, INTERVAL '3' minute)").execute().print();}}

结果：

+----+-------------------------+-------------------------+-----------+| op |                  wStart |                    wEnd | sum_price |+----+-------------------------+-------------------------+-----------+|+I|2021-04-1508:05:00.000|2021-04-1508:16:00.000|15.00||+I|2021-04-1508:17:00.000|2021-04-1508:20:00.000|6.00|+----+-------------------------+-------------------------+-----------+2 rows in set

2.4 累计窗口（Comulate Windows flink1.13 版本新特性）

语法：

CUMULATE(TABLE data,DESCRIPTOR(timecol), step, size)TABLE 表名称
DESCRIPTOR 表中作为开窗的时间字段名称
step 大窗口的分割长度
size 指定最大的那个时间窗口

需求：10 分钟作为窗口，统计每隔两分钟的累计数（类似于

中视频计划

计算播放量完美累计曲线！）

packagecn.itcast.day02.Window;importorg.apache.flink.streaming.api.environment.StreamExecutionEnvironment;importorg.apache.flink.table.api.bridge.java.StreamTableEnvironment;/**
 * @author lql
 * @time 2024-03-16 19:45:02
 * @description TODO
 */publicclassGroupWindowsSqlCumulateExample{publicstaticvoidmain(String[] args){StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);StreamTableEnvironment tEnv =StreamTableEnvironment.create(env);String filePath =GroupWindowsSqlCumulateExample.class.getClassLoader().getResource("bid.csv").getPath();// 作为事件时间的字段必须是 timestamp 类型, 所以根据 long 类型的 ts 计算出来一个 t
        tEnv.executeSql("create table Bid("+"bidtime TIMESTAMP(3),"+"price DECIMAL(10, 2), "+"item string,"+"watermark for bidtime as bidtime - interval '1' second) "+"with("+"'connector' = 'filesystem',"+"'path' = 'file:///"+filePath+"',"+"'format' = 'csv'"+")");

        tEnv.sqlQuery("SELECT window_start, window_end, SUM(price) as sum_price\n"+"  FROM TABLE(\n"+"    CUMULATE(TABLE Bid, DESCRIPTOR(bidtime), INTERVAL '2' MINUTES, INTERVAL '10' MINUTES))\n"+"  GROUP BY window_start, window_end").execute().print();}}

结果：

+----+-------------------------+-------------------------+-----------+| op |            window_start |              window_end | sum_price |+----+-------------------------+-------------------------+-----------+|+I|2021-04-1508:00:00.000|2021-04-1508:06:00.000|4.00||+I|2021-04-1508:00:00.000|2021-04-1508:08:00.000|6.00||+I|2021-04-1508:00:00.000|2021-04-1508:10:00.000|11.00||+I|2021-04-1508:10:00.000|2021-04-1508:12:00.000|3.00||+I|2021-04-1508:10:00.000|2021-04-1508:14:00.000|4.00||+I|2021-04-1508:10:00.000|2021-04-1508:16:00.000|4.00||+I|2021-04-1508:10:00.000|2021-04-1508:18:00.000|10.00||+I|2021-04-1508:10:00.000|2021-04-1508:20:00.000|10.00|+----+-------------------------+-------------------------+-----------+8 rows in set

3. 多维数据分析

3.1 GROUPING SETS

当前效果：

SELECT window_start, 
        window_end,
        userId,
        category,sum(price) as sum_price
FROMTABLE(TUMBLE(TABLE orders,DESCRIPTOR(t),INTERVAL'5'SECONDS))GROUPBY window_start, window_end,GROUPINGSETS((userId, category),(userId),())

以前效果：

// ()
SELECT window_start, window_end, 'NULL' as userId, 'NULL' as category, sum(price) as sum_price
FROM TABLE(
TUMBLE(TABLE orders, DESCRIPTOR(t), INTERVAL '5' SECONDS))
GROUP BY window_start, window_end
UNION ALL
// (userId)
SELECT window_start, window_end, userId as userId, 'NULL' as category, sum(price) as sum_price
FROM TABLE(
TUMBLE(TABLE orders, DESCRIPTOR(t), INTERVAL '5' SECONDS))
GROUP BY window_start, window_end, userId
UNION ALL
// (userId, category)
SELECT window_start, window_end,userId, category, sum(price) as sum_price
FROM TABLE(
TUMBLE(TABLE orders, DESCRIPTOR(t), INTERVAL '5' SECONDS))
GROUP BY window_start, window_end, userId, category

3.2 ROLLUP

速记：从右往左，全面到稀缺！

GROUP BY ROLLUP(a, b, c)
--等价于以下语句。
GROUPING SETS((a,b,c),(a,b),(a), ())

GROUP BY ROLLUP ( a, (b, c), d )
--等价于以下语句。
GROUPING SETS (
    ( a, b, c, d ),
    ( a, b, c    ),
    ( a          ),
    (            )
)

3.3 CUBE

速记：排列组合

GROUPBYCUBE(a, b, c)--等价于以下语句。
GROUPINGSETS((a,b,c),(a,b),(a,c),(b,c),(a),(b),(c),())GROUPBYCUBE((a, b),(c, d))--等价于以下语句。
GROUPINGSETS(( a, b, c, d ),( a, b       ),(       c, d ),())// CUBE 和 GROUPING SETS 组合，相当于排列组合基础上加上元素GROUPBY a,CUBE(b, c),GROUPINGSETS((d),(e))--等价于以下语句。
GROUPBYGROUPINGSETS((a, b, c, d),(a, b, c, e),(a, b, d),(a, b, e),(a, c, d),(a, c, e),(a, d),(a, e))

3.4 GROUPING 和 GROUPING_ID

背景：GROUPING SETS 结果中使用 NULL 充当占位符，导致无法区分占位符 NULL 与数据中真正的 NULL。

3.4.1 GROUPING 函数

接受一个列名作为参数
返回0，意味着无NULL / 来自输入数据（原本存在的空值）
返回1，意味着 NULL 是 GROUPING SETS 的占位符。

实例：

SELECT  window_start, window_end, userId, category, 
        GROUPING(category)as categoryFlag,sum(price)as sum_price,IF(GROUPING(category)=0, category,'ALL')as`all`FROMTABLE(
           TUMBLE(TABLE orders, DESCRIPTOR(t),INTERVAL'5' SECONDS))GROUPBY window_start, window_end, GROUPING SETS((userId, category),(userId))

结果：
window_startwindow_enduserIdcategorysum_priceflagall2021-05-23 05:16:35.0002021-05-23 05:16:40.000NULLNULL10.11ALL2021-05-23 05:16:40.0002021-05-23 05:16:45.000NULLNULL96.61ALL2021-05-23 05:16:45.0002021-05-23 05:16:50.000NULLNULL15.61ALL2021-05-23 05:16:35.0002021-05-23 05:16:40.000user_001电脑10.10电脑2021-05-23 05:16:40.0002021-05-23 05:16:45.000user_001手机14.10手机2021-05-23 05:16:40.0002021-05-23 05:16:45.000user_002手机82.50手机2021-05-23 05:16:45.0002021-05-23 05:16:50.000user_001电脑15.60电脑2021-05-23 05:16:35.0002021-05-23 05:16:40.000user_001NULL10.11ALL2021-05-23 05:16:40.0002021-05-23 05:16:45.000user_001NULL14.11ALL2021-05-23 05:16:40.0002021-05-23 05:16:45.000user_002NULL82.51ALL2021-05-23 05:16:45.0002021-05-23 05:16:50.000user_001NULL15.61ALL

3.4.2 GROUPING_ID（兼容 Hive）

MaxCompute还提供了无参数的 GROUPING__ID 函数，用于兼容Hive查询。

结果是将参数列的GROUPING结果按照BitMap的方式组成整数

MaxCompute 和 Hive 2.3.0 及以上版本兼容该函数，在Hive 2.3.0以下版本中该函数输出不一致，因此并不推荐使用此函数。

SELECT
a,b,c ,COUNT(*),GROUPING_IDFROMVALUES(1,2,3) as t(a,b,c)GROUPBY a, b, c GROUPINGSETS((a,b,c),(a));GROUPING_ID既无输入参数，也无括号。此表达方式在 MaxCompute 中等价于 GROUPING_ID(a,b,c)，参数与 GROUPBY 的顺序一致。

3.5 Window Top-N

模板：计算每10分钟营业时间窗内销售额最高的前3名供应商。

SELECT *
  FROM (
    SELECT *, ROW_NUMBER() OVER (PARTITION BY window_start, window_end ORDER BY price DESC) as rownum
    FROM (
      SELECT window_start, window_end, supplier_id, SUM(price) as price, COUNT(*) as cnt
      FROM TABLE(
        TUMBLE(TABLE Bid, DESCRIPTOR(bidtime), INTERVAL '10' MINUTES))
      GROUP BY window_start, window_end, supplier_id
    )
) WHERE rownum <= 3;

思路：先算滚动时间 10 分钟，按照窗口时间，id 分组求和，再排序函数取前三。

4. Over Windows

4.1 ROWS OVER WINDOW

按照行进行划分：BETWEEN (UNBOUNDED | rowCount) PRECEDING AND CURRENT ROW

注解：如果不加 rowCount 相当于从以前到现在，加上 rowCount 相当于从前 n 行到现在！

数据源：
itemIDitemTypeonSellTime****priceITEM001Electronic2021-05-11 10:01:00.00020ITEM002Electronic2021-05-11 10:02:00.00050ITEM003Electronic2021-05-11 10:03:00.00030ITEM004Electronic2021-05-11 10:03:00.00060ITEM005Electronic2021-05-11 10:05:00.00040ITEM006Electronic2021-05-11 10:06:00.00020ITEM007Electronic2021-05-11 10:07:00.00070ITEM008Clothes2021-05-11 10:08:00.00020ITEM009Clothes2021-05-11 10:09:00.00040ITEM010Clothes2021-05-11 10:11:00.00030
示例：按照 itemType 分组，onSellTime 升序，求从以前到现在总金额

select
    itemID,
    itemType,
    onSellTime,
    price,
    sum(price) over w as sumPrice
from tmall_item
    WINDOW w AS (
        PARTITION BY itemType 
        ORDER BY onSellTime 
        ROWS  BETWEEN UNBOUNDED preceding AND CURRENT ROW
    )

结果：
itemIDitemTypeonSellTimepricesumPriceITEM001Electronic2021-05-11 10:01:00.00020.020.0ITEM002Electronic2021-05-11 10:02:00.00050.070.0ITEM003Electronic2021-05-11 10:03:00.00030.0100.0ITEM004Electronic2021-05-11 10:03:00.00060.0160.0ITEM005Electronic2021-05-11 10:05:00.00040.0200.0ITEM006Electronic2021-05-11 10:06:00.00020.0220.0ITEM007Electronic2021-05-11 10:07:00.00070.0290.0ITEM008Clothes2021-05-11 10:08:00.00020.020.0ITEM009Clothes2021-05-11 10:09:00.00040.060.0ITEM010Clothes2021-05-11 10:11:00.00030.090.0

4.2 RANGE OVER WINDOW

按照时间进行划分：ROWS BETWEEN ( UNBOUNDED | rowCount ) preceding AND CURRENT ROW

例子：实时统计两分钟内金额

select
    itemID,
    itemType,
    onSellTime,
    price,sum(price) over w as sumPrice
from tmall_item
    WINDOW w AS(PARTITIONBY itemType
        ORDERBY onSellTime
        RANGEBETWEENINTERVAL'2'MINUTE preceding ANDCURRENTROW)

5. TableAPI 窗口的定义

5.1.1 滚动窗口

Tumble 类方法：

over：定义窗口长度
on：用来分组（按时间间隔）或者排序（按行数）的时间字段
as：别名，必须出现在后面的groupBy中

例子：每隔5秒钟统计一次每个商品类型的销售总额

publicclassGroupWindowsTableApiTumbleExample{publicstaticvoidmain(String[] args)throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);SingleOutputStreamOperator<OrderInfo> dataStream = env
                .fromElements(newOrderInfo("电脑",1000L,100D),newOrderInfo("手机",2000L,200D),newOrderInfo("电脑",3000L,300D),newOrderInfo("手机",4000L,400D),newOrderInfo("手机",5000L,500D),newOrderInfo("电脑",6000L,600D)).assignTimestampsAndWatermarks(WatermarkStrategy.<OrderInfo>forBoundedOutOfOrderness(Duration.ofSeconds(5)).withTimestampAssigner((element, recordTimestamp)-> element.getTimestamp()));StreamTableEnvironment tableEnv =StreamTableEnvironment.create(env);Table table = tableEnv
                .fromDataStream(dataStream, $("category"), $("timestamp").rowtime(), $("money"));

        table
                .window(Tumble.over(lit(5).second()).on($("timestamp")).as("w"))// 定义滚动窗口并给窗口起一个别名.groupBy($("category"), 
                         $("w"))// 窗口必须出现的分组字段中.select($("category"), 
                        $("w").start().as("window_start"), 
                        $("w").end().as("window_end"), 
                        $("money").sum().as("total_money")).execute().print();

        env.execute();}@Data@AllArgsConstructor@NoArgsConstructorpublicstaticclassOrderInfo{privateString category;privateLong timestamp;privateDouble money;}}

5.1.2 滑动窗口

Slide 类方法：

over：定义窗口长度
every：定义滑动步长
on：用来分组（按时间间隔）或者排序（按行数）的时间字段
as：别名，必须出现在后面的groupBy中

例子：每隔5秒钟统计过去10秒钟每个商品类型的销售总额

publicclassGroupWindowsTableApiTumbleExample{publicstaticvoidmain(String[] args)throwsException{StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);SingleOutputStreamOperator<OrderInfo> dataStream = env
                .fromElements(newOrderInfo("电脑",1000L,100D),newOrderInfo("手机",2000L,200D),newOrderInfo("电脑",3000L,300D),newOrderInfo("手机",4000L,400D),newOrderInfo("手机",5000L,500D),newOrderInfo("电脑",6000L,600D)).assignTimestampsAndWatermarks(WatermarkStrategy.<OrderInfo>forBoundedOutOfOrderness(Duration.ofSeconds(5)).withTimestampAssigner((element, recordTimestamp)-> element.getTimestamp()));StreamTableEnvironment tableEnv =StreamTableEnvironment.create(env);Table table = tableEnv
                .fromDataStream(dataStream, $("category"), $("timestamp").rowtime(), $("money"));

        table
                .window(Slide.over(lit(10).second()).every(lit(5).second()).on($("timestamp")).as("w"))// 定义滚动窗口并给窗口起一个别名.groupBy($("category"), 
                         $("w"))// 窗口必须出现的分组字段中.select($("category"), 
                        $("w").start().as("window_start"), 
                        $("w").end().as("window_end"), 
                        $("money").sum().as("total_money")).execute().print();

        env.execute();}@Data@AllArgsConstructor@NoArgsConstructorpublicstaticclassOrderInfo{privateString category;privateLong timestamp;privateDouble money;}}

5.1.3 会话窗口

Session 类方法：

withGap：会话时间间隔
on：用来分组（按时间间隔）或者排序（按行数）的时间字段
as：别名，必须出现在后面的groupBy中

例子：两次的时间间隔超过6秒的基础上，没有新的订单事件这个窗口就会关闭，然后处理这个窗口区间内所产生的订单数据计算

public class GroupWindowsTableApiTumbleExample {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        SingleOutputStreamOperator<OrderInfo> dataStream = env
                .fromElements(
                        new OrderInfo("电脑", 1000L, 100D),
                        new OrderInfo("手机", 2000L, 200D),
                        new OrderInfo("电脑", 3000L, 300D),
                        new OrderInfo("手机", 4000L, 400D),
                        new OrderInfo("手机", 5000L, 500D),
                        new OrderInfo("电脑", 6000L, 600D))
                .assignTimestampsAndWatermarks(
                        WatermarkStrategy
                                .<OrderInfo>forBoundedOutOfOrderness(Duration.ofSeconds(5))
                                .withTimestampAssigner((element, recordTimestamp) -> element.getTimestamp())
                );

        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
        Table table = tableEnv
                .fromDataStream(dataStream, $("category"), $("timestamp").rowtime(), $("money"));

        table
                .window(Session.withGap(lit(6).second())
                        .on($("timestamp"))
                        .as("w"))  // 定义滚动窗口并给窗口起一个别名
                .groupBy($("category"), 
                         $("w")) // 窗口必须出现的分组字段中
                .select($("category"), 
                        $("w").start().as("window_start"), 
                        $("w").end().as("window_end"), 
                        $("money").sum().as("total_money"))
                .execute()
                .print();

        env.execute();
    }

    @Data
    @AllArgsConstructor
    @NoArgsConstructor
    public static class OrderInfo {
        private String category;
        private Long timestamp;
        private Double money;
    }
}

标签： flink 笔记大数据

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