flinksql-Queries查询相关实战

1. 分组聚合

--分组集

--GROUPING SETS() 允许你定义特定的分组方式，这样你可以选择只对感兴趣的分组进行计算。

--通过手动指定不同的分组组合，你能够灵活地控制数据的聚合结果。

--与 ROLLUP 和 CUBE 不同，GROUPING SETS 不会自动生成所有子集组合，而是只生成你指定的那些。

SELECT supplier_id, rating, COUNT(*) AS total

FROM (VALUES

('supplier1', 'product1', 4),

('supplier1', 'product2', 3),

('supplier2', 'product3', 3),

('supplier2', 'product4', 4))

AS Products(supplier_id, product_id, rating)

GROUP BY GROUPING SETS ((supplier_id, rating), (supplier_id), ());

--ROLLUP

--ROLLUP() 用于执行分层级别的聚合，主要用于需要按顺序逐层汇总数据的场景。

--与 CUBE() 不同，ROLLUP() 只生成按从左到右逐步减少维度的组合，而不是所有可能的子集组合。

--例如，ROLLUP(a, b, c) 会生成 (a, b, c), (a, b), (a), 和 ()，而不会像 CUBE() 那样生成所有的可能组--合。

SELECT supplier_id, rating, COUNT(*)

FROM (VALUES

('supplier1', 'product1', 4),

('supplier1', 'product2', 3),

('supplier2', 'product3', 3),

('supplier2', 'product4', 4))

AS Products(supplier_id, product_id, rating)

GROUP BY ROLLUP (supplier_id, rating);

--立方体

--CUBE() 是一种扩展的 GROUP BY 操作，允许你针对多列进行分组聚合，并生成每种可能的维度组合的聚合结果。

--如果使用了 CUBE(a, b, c)，Flink 会计算出所有 a, b, c 及其子集的组合的聚合结果。

--在数据分析和 OLAP（在线分析处理）场景中，CUBE 常用来计算多维数据的统计值。

SELECT supplier_id, rating, COUNT(*)

FROM (VALUES

('supplier1', 'product1', 4),

('supplier1', 'product2', 3),

('supplier2', 'product3', 3),

('supplier2', 'product4', 4))

AS Products(supplier_id, product_id, rating)

GROUP BY CUBE (supplier_id, rating);

1. 窗口函数TVF

--注：不支持cdc模式，因为窗口函数只支持追加模式的，不支持update与delete操作

--模拟表

CREATE TABLE bid (

`id` string,

bidtime TIMESTAMP(3),

price DECIMAL(10, 2),

item string,

ts as bidtime,

WATERMARK FOR ts AS ts - INTERVAL '5' SECOND,

--proc_time AS PROCTIME(),  

PRIMARY KEY (`id`) NOT ENFORCED

)

WITH

(

'connector' = 'jdbc',

${36},

'table-name' = 'bid'

);

--滚动窗口

-- SELECT cast(window_start as string) AS window_start, cast(window_end as string) AS window_end , SUM(price) AS total_price

-- FROM TABLE(

-- TUMBLE(TABLE bid, DESCRIPTOR(ts), INTERVAL '10' MINUTES))

-- GROUP BY window_start, window_end;

--滑动窗口

-- SELECT cast(window_start as string) AS window_start, cast(window_end as string) AS window_end , SUM(price) AS total_price

-- FROM TABLE(

-- HOP(TABLE bid, DESCRIPTOR(ts), INTERVAL '5' MINUTES, INTERVAL '10' MINUTES))

-- GROUP BY window_start, window_end;

--累计窗口

-- SELECT cast(window_start as string) AS window_start, cast(window_end as string) AS window_end , SUM(price) AS total_price

-- FROM TABLE(

-- CUMULATE(TABLE bid, DESCRIPTOR(ts), INTERVAL '5' MINUTES, INTERVAL '20' MINUTES))

-- GROUP BY window_start, window_end;

--会话窗口（不支持批处理）

SELECT window_start, window_end, item, SUM(price) AS total_price

FROM TABLE(

  SESSION(TABLE bid PARTITION BY item, DESCRIPTOR(ts), INTERVAL '5' MINUTES))

GROUP BY item, window_start, window_end;

1. 窗口聚合

CREATE TABLE bid (

`id` string,

bidtime TIMESTAMP(3),

price DECIMAL(10, 2),

item string,

supplier_id string,

ts as bidtime,

WATERMARK FOR ts AS ts - INTERVAL '5' SECOND,

--proc_time AS PROCTIME(),  

PRIMARY KEY (`id`) NOT ENFORCED

)

WITH

(

'connector' = 'jdbc',

${36},

'table-name' = 'bid'

);

--分组集

-- SELECT cast(window_start as string) AS window_start, cast(window_end as string) AS window_end , supplier_id, SUM(price) AS total_price

-- FROM TABLE(

-- TUMBLE(TABLE bid, DESCRIPTOR(ts), INTERVAL '10' MINUTES))

-- GROUP BY window_start, window_end, GROUPING SETS ((supplier_id), ());

--ROLLUP

-- SELECT cast(window_start as string) AS window_start, cast(window_end as string) AS window_end , supplier_id, SUM(price) AS total_price

-- FROM TABLE(

-- TUMBLE(TABLE bid, DESCRIPTOR(ts), INTERVAL '10' MINUTES))

-- GROUP BY window_start, window_end, ROLLUP (supplier_id);

--立方体

SELECT cast(window_start as string) AS window_start, cast(window_end as string) AS window_end , supplier_id, item, SUM(price) AS total_price

FROM TABLE(

TUMBLE(TABLE bid, DESCRIPTOR(ts), INTERVAL '10' MINUTES))

GROUP BY window_start, window_end, CUBE (supplier_id, item);

1. Over聚合(FLINK-CDC不支持)

CREATE TABLE bid (

`id` string,

bidtime TIMESTAMP(3),

price DECIMAL(10, 2),

item string,

supplier_id string,

--proc_time AS PROCTIME(),  

WATERMARK FOR bidtime AS bidtime - INTERVAL '5' SECOND,

PRIMARY KEY (`id`) NOT ENFORCED

)

WITH

(

'connector' = 'jdbc',

${36},

'table-name' = 'bid'

);

-- SELECT

-- agg_func(agg_col) OVER (

-- [PARTITION BY col1[, col2, ...]]

-- ORDER BY time_col

-- range_definition),

-- ...

-- FROM ...

--OVER 窗口需要数据是有序的。因为表没有固定的排序，所以 ORDER BY 子句是强制的。对于流式查询，Flink 目前只支持 OVER 窗口定义在升序（asc）的 时间属性 上。其他的排序不支持。

--ORDER BY：必须是时间戳列（事件时间、处理时间），只能升序

--range_definition：这个标识聚合窗口的聚合数据范围，在 Flink 中有两种指定数据范围的方式。第一种为按照行数聚合，第二种为按照时间区间聚合

--不指定 range_definition 时：Flink 默认使用 RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW。

-- SELECT supplier_id, cast(bidtime as string) as bidtime, price,

-- SUM(price) OVER (

-- PARTITION BY supplier_id

-- ORDER BY bidtime

-- ) AS sum_pri

-- FROM bid

-- ;

--WINDOW 子句可用于在 SELECT 子句之外定义 OVER 窗口。它让查询可读性更好，也允许多个聚合共用一个窗口定义。

SELECT supplier_id, cast(bidtime as string) as bidtime, price,

SUM(price) OVER w AS sum_pri,

avg(price) OVER w AS avg_pri

FROM bid

WINDOW w as ( PARTITION BY supplier_id

ORDER BY bidtime )

;