Question

我正在尝试使用表字段和值

返回一个函数

我有这样的功能：

CREATE FUNCTION books(
    id integer)
    RETURNS TABLE(
        id integer,        
        title text,        
        type text
    )
    LANGUAGE 'sql'    
AS $BODY$

WITH RECURSIVE chain(id, seq) AS (
        SELECT s.id, s.sequence
        FROM product s
        WHERE s.id = $1        
        UNION ALL
        SELECT s.id, s.sequence
        FROM product s, chain t
        WHERE s.id = t.id       
        )
        select * from chain     
$BODY$;

我的查询目前正在返回，如下所示

1, book title1, new
2, book title2, new
3, book title3, old

我希望返回类似

的内容

id: 1, title: book title1, type: new
id: 2, title: book title2, type: new
id: 3, title: book title3, type: old

所以我有关键来映射api方面的值。

有可能吗？

非常感谢！

更新

谢谢，递归后的实际select语句类似于：

SELECT c.id, c.title, cu.name
FROM customer cu, book b
WHERE cu.id = b.id
UNION
(
SELECT c.id, c.title, f.cost
FROM chain c, foo f
WHERE c.id = f.id
)

如何使用row_to_json来隐藏它们？

Answer 1

一种方法是将行转换为json：

In [1]: import sys
    ...: import numpy as np
    ...: 

In [2]: print(sys.version)
    ...: 
3.6.2 |Continuum Analytics, Inc.| (default, Jul 20 2017, 13:14:59) 
[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]

In [3]: print(np.__version__)
    ...: 
1.12.1

In [4]: # Method 1 (Original)
    ...: def transform1(matrix):
    ...:     mat = np.asarray(matrix)
    ...:     mat[np.logical_not(np.not_equal(mat, 0).cumprod(axis=0))] = 0
    ...:     return mat
    ...: 

In [5]: # Method 2:
    ...: def transform2(matrix):
    ...:     mat = np.asarray(matrix)
    ...:     mat *= (mat != 0).cumprod(axis=0, dtype=np.bool)
    ...:     return mat
    ...: 

In [6]: # @DSM method:
    ...: def transform_DSM(matrix):
    ...:     mat = np.asarray(matrix)
    ...:     mat *= np.minimum.accumulate(mat != 0)
    ...:     return mat
    ...: 

In [7]: # @DanielF method:
    ...: def transform_DanielF(matrix):
    ...:     mat = np.asarray(matrix)
    ...:     mat[~np.logical_and.accumulate(mat, axis = 0)] = 0
    ...:     return mat
    ...: 

In [8]: # Optimized @DanielF method:
    ...: def transform_DanielF_optimized(matrix):
    ...:     mat = np.asarray(matrix)
    ...:     mat *= np.logical_and.accumulate(mat, dtype=np.bool)
    ...:     return mat
    ...: 

In [9]: matrix = np.random.randint(0, 20000, (20000, 20000))

In [10]: %timeit -n1 transform1(matrix)
22.1 s ± 241 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [11]: %timeit -n1 transform2(matrix)
9.29 s ± 185 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [12]: %timeit -n1 transform3(matrix)
9.23 s ± 180 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [13]: %timeit -n1 transform_DSM(matrix)
9.24 s ± 195 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [14]: %timeit -n1 transform_DanielF(matrix)
10.3 s ± 219 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [15]: %timeit -n1 transform_DanielF_optimized(matrix)
9.27 s ± 187 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

如何从postgres函数

1 个答案: