FlinkSQL窗口实例分析

2023-12-29 06:36
文章标签 分析 实例 窗口 flinksql

本文主要是介绍FlinkSQL窗口实例分析，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

Windowing TVFs

Windowing table-valued functions (Windowing TVFs)，即窗口表值函数
注意：窗口函数不可以单独使用，需要聚合函数，按照 window_start、window_end 分区，即存在：group by window_start,window_end

  • TUMBLE函数采用三个必需参数，一个可选参数：

    TUMBLE(TABLE data, DESCRIPTOR(timecol), size [, offset ])

    data：是一个表参数，可以是与时间属性列的任何关系。
    timecol：是一个列描述符，指示数据的哪些时间属性列应映射到滚动窗口。
    size：是指定翻滚窗口宽度的持续时间。
    offset: 是一个可选参数，用于指定窗口开始移动的偏移量。

  • HOP采用 4 个必需参数和 1 个可选参数：

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

    data：是一个表参数，可以是与时间属性列的任何关系。
    timecol：是一个列描述符，指示数据的哪些时间属性列应映射到跳跃窗口。
    slide：是指定连续跳跃窗口开始之间的持续时间的持续时间
    size：是指定跳跃窗口宽度的持续时间。
    offset: 是一个可选参数，用于指定窗口开始移动的偏移量。

  • CUMULATE采用 4 个必需参数和 1 个可选参数：

    CUMULATE(TABLE data, DESCRIPTOR(timecol), step, size)

    data：是一个表参数，可以是与时间属性列的任何关系。
    timecol：是一个列描述符，指示数据的哪些时间属性列应映射到累积窗口。
    step：是指定连续累积窗口末尾之间增加的窗口大小的持续时间。
    size：是指定累积窗口最大宽度的持续时间。size必须是 的整数倍step。
    offset: 是一个可选参数，用于指定窗口开始移动的偏移量。

滚动窗口

CREATE TABLE kafka_table(mid bigint,db string,sch string,tab string,opt string,ts bigint,ddl string,err string,src map < string, string >,cur map < string, string >,cus map < string, string >,event_time as cast(TO_TIMESTAMP_LTZ(ts,3) AS TIMESTAMP(3)), -- TIMESTAMP(3)/TIMESTAMP_LTZ(3)WATERMARK FOR event_time AS event_time - INTERVAL '2' MINUTE     --SECOND
) WITH ('connector' = 'kafka','topic' = 't0','properties.bootstrap.servers' = 'xx.xx.xx.xx:9092','scan.startup.mode' = 'earliest-offset',  --group-offsets/earliest-offset/latest-offset'format' = 'json'
);create view tmp as
selectCOALESCE(cur['group_name'], src['group_name']) group_name,COALESCE(cur['batch_number'], src['batch_number']) batch_number,event_time
from kafka_table
where UPPER(opt) <> 'DELETE';
--注意：窗口函数不可以单独使用，需要聚合函数，按照 window_start、window_end 分区select window_start,window_end,window_time,group_name,count(*) as cnt from
TABLE(TUMBLE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '10' MINUTES))
group by window_start,window_end,window_time,group_name

滑动窗口

CREATE TABLE kafka_table(mid bigint,db string,sch string,tab string,opt string,ts bigint,ddl string,err string,src map < string, string >,cur map < string, string >,cus map < string, string >,event_time as cast(TO_TIMESTAMP_LTZ(ts,3) AS TIMESTAMP(3)), -- TIMESTAMP(3)/TIMESTAMP_LTZ(3)WATERMARK FOR event_time AS event_time - INTERVAL '2' MINUTE     --SECOND
) WITH ('connector' = 'kafka','topic' = 't0','properties.bootstrap.servers' = 'xx.xx.xx.xx:9092','scan.startup.mode' = 'earliest-offset',  --group-offsets/earliest-offset/latest-offset'format' = 'json'
);create view tmp as
selectCOALESCE(cur['group_name'], src['group_name']) group_name,COALESCE(cur['batch_number'], src['batch_number']) batch_number,event_time
from kafka_table
where UPPER(opt) <> 'DELETE';
--注意：窗口函数不可以单独使用，需要聚合函数，按照 window_start、window_end 分区select window_start,window_end,window_time,group_name,count(*) as cnt from
TABLE(HOP(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '60' SECOND,INTERVAL '10' MINUTES))
group by window_start,window_end,window_time,group_name

累计窗口

CREATE TABLE kafka_table(mid bigint,db string,sch string,tab string,opt string,ts bigint,ddl string,err string,src map < string, string >,cur map < string, string >,cus map < string, string >,event_time as cast(TO_TIMESTAMP_LTZ(ts,3) AS TIMESTAMP(3)), -- TIMESTAMP(3)/TIMESTAMP_LTZ(3)WATERMARK FOR event_time AS event_time - INTERVAL '2' MINUTE     --SECOND
) WITH ('connector' = 'kafka','topic' = 't0','properties.bootstrap.servers' = 'xx.xx.xx.xx:9092','scan.startup.mode' = 'earliest-offset',  --group-offsets/earliest-offset/latest-offset'format' = 'json'
);create view tmp as
selectCOALESCE(cur['group_name'], src['group_name']) group_name,COALESCE(cur['batch_number'], src['batch_number']) batch_number,event_time
from kafka_table
where UPPER(opt) <> 'DELETE';
--注意：窗口函数不可以单独使用，需要聚合函数，按照 window_start、window_end 分区select window_start,window_end,window_time,group_name,count(*) as cnt from
TABLE(CUMULATE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '1' HOUR,INTERVAL '24' HOURS)) --从零点开始累计
TABLE(CUMULATE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '60' SECOND,INTERVAL '10' MINUTES))
TABLE(CUMULATE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '1' MINUTE,INTERVAL '1' HOURS))
group by window_start,window_end,window_time,group_name

窗口聚合-多维分析

CREATE TABLE kafka_table(mid bigint,db string,sch string,tab string,opt string,ts bigint,ddl string,err string,src map < string, string >,cur map < string, string >,cus map < string, string >,event_time as cast(TO_TIMESTAMP_LTZ(ts,3) AS TIMESTAMP(3)), -- TIMESTAMP(3)/TIMESTAMP_LTZ(3)WATERMARK FOR event_time AS event_time - INTERVAL '2' MINUTE     --SECOND
) WITH ('connector' = 'kafka','topic' = 't0','properties.bootstrap.servers' = 'xx.xx.xx.xx:9092','scan.startup.mode' = 'earliest-offset',  --group-offsets/earliest-offset/latest-offset'format' = 'json'
);create view tmp as
selectCOALESCE(cur['group_name'], src['group_name']) group_name,COALESCE(cur['batch_number'], src['batch_number']) batch_number,event_time
from kafka_table
where UPPER(opt) <> 'DELETE';
--注意：窗口函数不可以单独使用，需要聚合函数，按照 window_start、window_end 分区--实例1：整体聚合
select window_start,window_end,count(*) as cnt from
TABLE(TUMBLE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '10' MINUTES))
group by window_start,window_end--实例2：根据字段聚合,n个维度
select window_start,window_end,group_name,count(*) as cnt from
TABLE(TUMBLE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '10' MINUTES))
group by window_start,window_end,group_name--实例3：多维分析GROUPING SETS
select window_start,window_end,group_name,count(*) as cnt from
TABLE(TUMBLE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '10' MINUTES))
group by window_start,window_end,GROUPING SETS((group_name)) --等同于 实例2
group by window_start,window_end,GROUPING SETS((group_name), ()) --等同于 实例1 union all 实例2--实例4：多维分析GROUPING SETS,多个字段
select window_start,window_end,group_name,batch_number,count(*) as cnt from
TABLE(TUMBLE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '10' MINUTES))
group by window_start,window_end,GROUPING SETS((group_name,batch_number),(group_name),(batch_number),())--实例5：多维分析CUBE 2^n个维度
select window_start,window_end,group_name,batch_number,count(*) as cnt from
TABLE(TUMBLE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '10' MINUTES))
group by window_start,window_end,CUBE(group_name) --等同于group by window_start,window_end,GROUPING SETS((group_name), ())
group by window_start,window_end,CUBE(group_name,batch_number) --等同于实例4--实例6：多维分析ROLLUP  n+1个维度
select window_start,window_end,group_name,batch_number,count(*) as cnt from
TABLE(TUMBLE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '10' MINUTES))
group by window_start,window_end,ROLLUP(group_name) --等同于 实例1 union all 实例2
group by window_start,window_end,ROLLUP(group_name,batch_number) --等同于GROUPING SETS((group_name,batch_number),(group_name),())

窗口topN

Window Top-N 语句的语法：

SELECT [column_list]
FROM (SELECT [column_list],ROW_NUMBER() OVER (PARTITION BY window_start, window_end [, col_key1...]ORDER BY col1 [asc|desc][, col2 [asc|desc]...]) AS rownumFROM table_name) -- relation applied windowing TVF
WHERE rownum <= N [AND conditions]

CREATE TABLE kafka_table(mid bigint,db string,sch string,tab string,opt string,ts bigint,ddl string,err string,src map < string, string >,cur map < string, string >,cus map < string, string >,event_time as cast(TO_TIMESTAMP_LTZ(ts,3) AS TIMESTAMP(3)), -- TIMESTAMP(3)/TIMESTAMP_LTZ(3)WATERMARK FOR event_time AS event_time - INTERVAL '2' MINUTE     --SECOND
) WITH ('connector' = 'kafka','topic' = 't0','properties.bootstrap.servers' = 'xx.xx.xx.xx:9092','scan.startup.mode' = 'earliest-offset',  --group-offsets/earliest-offset/latest-offset'format' = 'json'
);create view tmp as
selectCOALESCE(cur['group_name'], src['group_name']) group_name,COALESCE(cur['batch_number'], src['batch_number']) batch_number,event_time
from kafka_table
where UPPER(opt) <> 'DELETE';
--注意：窗口函数不可以单独使用，需要聚合函数，按照 window_start、window_end 分区--方式1：窗口 Top-N 紧随窗口聚合之后
create view tmp_window as
select window_start,window_end,window_time,group_name,count(*) as cnt from
TABLE(TUMBLE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '24' HOURS))
group by window_start,window_end,window_time,group_name;--计算每个翻滚 24小时窗口内pv最高的前 3 名机构(即每天PV最高的前三名)
select * from(select * ,ROW_NUMBER() OVER (PARTITION BY window_start, window_end ORDER BY cnt DESC) as rnfrom tmp_window) t
where rn <=3--计算每个机构pv最高的前 3天
select * from(select * ,ROW_NUMBER() OVER (PARTITION BY group_name ORDER BY cnt DESC) as rnfrom tmp_window) t
where rn <=3--方式2：窗口 Top-N 紧随窗口 TVF 之后
select *
from(selectwindow_start,window_end,window_time,group_name,ts,ROW_NUMBER() OVER (PARTITION BY window_start, window_end ORDER BY ts DESC) AS rnfrom TABLE(TUMBLE(TABLE tmp, DESCRIPTOR(event_time), INTERVAL '24' HOURS)))
where rn <=3

窗口去重

Flink使用去重的方式，就像Window Top-N查询ROW_NUMBER()的方式一样。理论上，
窗口重复数据删除是窗口 Top-N 的一种特殊情况，其中 N 为 1，并且按处理时间或事件时间排序
Window Deduplication 语句的语法：

SELECT [column_list]
FROM (SELECT [column_list],ROW_NUMBER() OVER (PARTITION BY window_start, window_end [, col_key1...]ORDER BY time_attr [asc|desc]) AS rownumFROM table_name) -- relation applied windowing TVF
WHERE (rownum = 1 | rownum <=1 | rownum < 2) [AND conditions]

CREATE TABLE kafka_table(mid bigint,db string,sch string,tab string,opt string,ts bigint,ddl string,err string,src map < string, string >,cur map < string, string >,cus map < string, string >,group_name as COALESCE(cur['group_name'], src['group_name']),batch_number as COALESCE(cur['batch_number'], src['batch_number']),event_time as cast(TO_TIMESTAMP_LTZ(ts,3) AS TIMESTAMP(3)), -- TIMESTAMP(3)/TIMESTAMP_LTZ(3)WATERMARK FOR event_time AS event_time - INTERVAL '2' MINUTE     --SECOND
) WITH ('connector' = 'kafka','topic' = 't0','properties.bootstrap.servers' = 'xx.xx.xx.xx:9092','scan.startup.mode' = 'earliest-offset',  --group-offsets/earliest-offset/latest-offset'format' = 'json'
);select *
from(selectwindow_start,window_end,group_name,event_time,ROW_NUMBER() OVER (PARTITION BY window_start, window_end ORDER BY event_time DESC) AS rnfrom TABLE(TUMBLE(TABLE kafka_table, DESCRIPTOR(event_time), INTERVAL '24' HOURS)))
where rn =1

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