如何在 PostgreSQL window 分区中执行过滤查询？

Question

我正在尝试更好地理解 PostgreSQL (9.3) window 功能。假设我有一个简单的 table 作为：

SimpleTable
    id int,
    tservice timestamp

并希望：

Select id, tservice , count(*) OVER (PARTITION BY id ....) as counter
from SimpleTable

SimpleTable 中的记录有 40 年前的 tservice 时间，但是计数需要仅限于每条记录的 tservice 时间戳之前的三年。

如何为 SimpleTable 中的每条记录生成计数？

推论问题：如何更改同一个查询以添加今天日期之前三年发生的所有记录的计数？

编辑#1：现在我明白这个问题哪里太模糊了（我学到了一些东西:)）。使用以下答案，我想获得 3 年的计数和当前日期的计数，例如：

                             3yrs prior   current date
1, 100, '2001-01-01 00:00:00', 0             0
2, 100, '2002-01-01 00:00:00', 1             0
3, 100, '2003-01-01 00:00:00', 2             0 
4, 100, '2004-01-01 00:00:00', 3             0
5, 100, '2005-01-01 00:00:00', 3             0              
6, 100, '2006-01-01 00:00:00', 3             0
7, 100, '2007-01-01 00:00:00', 3             0 
8, 100, '2008-01-01 00:00:00', 3             0
9, 100, '2009-01-01 00:00:00', 3             0
10, 100, '2010-01-01 00:00:00',3             0
11, 100, '2011-01-01 00:00:00',3             0
12, 100, '2012-01-01 00:00:00',3             0
13, 100, '2013-01-01 00:00:00',3             0
14, 100, '2014-01-01 00:00:00',3             1
15, 100, '2015-01-01 00:00:00',3             2
16, 100, '2016-01-01 00:00:00',3             3  (today is 2016-01-06)

编辑 #2：这可以得到我需要的答案，但不使用 window 分区。我在想 PostgreSQL 没有实现 RANGE 间隔 - 这就是我认为这个问题所需要的。

     select s1.recid, s1.tservice, s1.client_recid, 
    (select count(*) from simpletable  s2 
        where (s1.tservice - s2.tservice)::INTERVAL <= interval '3 years' and
        s2.tservice < s1.tservice  and
        s2.client_recid = s1.client_recid)
from simpletable s1
order by client_recid, tservice

在几十万条记录上，这在我的笔记本电脑上大约需要 10 秒。有没有更快的方法？

附录注意：如 Erwin 所述，使用带有游标的函数式方法可将执行时间缩短至 146 毫秒。感谢大家的精彩教程。

Answer 1

不太确定你的目标是什么，通常不会按 ID 分区（假设每行 ID 是唯一的）。通常，您按多个行共享的某个值进行分区。一个例子可能会有所帮助：SQL Fiddle

PostgreSQL 9.3 架构设置:

CREATE TABLE SimpleTable
    ("id" int, "client_id" int, "tservice" timestamp)
;

INSERT INTO SimpleTable
    ("id", "client_id", "tservice")
VALUES
    (1, 100, '2001-01-01 00:00:00'),
    (2, 100, '2002-01-01 00:00:00'),
    (3, 100, '2003-01-01 00:00:00'),
    (4, 100, '2004-01-01 00:00:00'),
    (5, 100, '2005-01-01 00:00:00'),
    (6, 100, '2006-01-01 00:00:00'),
    (7, 100, '2007-01-01 00:00:00'),
    (8, 100, '2008-01-01 00:00:00'),
    (9, 100, '2009-01-01 00:00:00'),
    (10, 100, '2010-01-01 00:00:00'),
    (11, 100, '2011-01-01 00:00:00'),
    (12, 100, '2012-01-01 00:00:00'),
    (13, 100, '2013-01-01 00:00:00'),
    (14, 100, '2014-01-01 00:00:00'),
    (15, 100, '2015-01-01 00:00:00'),
    (16, 100, '2016-01-01 00:00:00')
;

查询 1:

SELECT
      id
    , tservice
    , COUNT(*) OVER (PARTITION BY client_id) AS C1
    , COUNT(CASE WHEN tservice >= (CURRENT_DATE - INTERVAL '3 years') THEN 1 ELSE NULL END)
          OVER (PARTITION BY client_id) AS C3

FROM SimpleTable

Results:

| id |                  tservice | c1 | c3 |
|----|---------------------------|----|----|
|  1 | January, 01 2001 00:00:00 | 16 |  3 |
|  2 | January, 01 2002 00:00:00 | 16 |  3 |
|  3 | January, 01 2003 00:00:00 | 16 |  3 |
|  4 | January, 01 2004 00:00:00 | 16 |  3 |
|  5 | January, 01 2005 00:00:00 | 16 |  3 |
|  6 | January, 01 2006 00:00:00 | 16 |  3 |
|  7 | January, 01 2007 00:00:00 | 16 |  3 |
|  8 | January, 01 2008 00:00:00 | 16 |  3 |
|  9 | January, 01 2009 00:00:00 | 16 |  3 |
| 10 | January, 01 2010 00:00:00 | 16 |  3 |
| 11 | January, 01 2011 00:00:00 | 16 |  3 |
| 12 | January, 01 2012 00:00:00 | 16 |  3 |
| 13 | January, 01 2013 00:00:00 | 16 |  3 |
| 14 | January, 01 2014 00:00:00 | 16 |  3 |
| 15 | January, 01 2015 00:00:00 | 16 |  3 |
| 16 | January, 01 2016 00:00:00 | 16 |  3 |

Answer 2

您的想法只是 不可能 使用 window 函数的 frame definition。（您已经开始怀疑了。） RANGE 或 ROWS 子句对不同的值或行进行计数，并且没有值含义的概念。

您想要计算特定时间段内的所有行，并且需要采用不同的方法。您可以运行相关子查询或 LATERAL 子查询来计算每一行，但这很昂贵。

更聪明的方法是运行并行通过两个游标并保持运行ning 计数。对于一个非常相似的问题，我完全实现了这一点：

Window Functions or Common Table Expressions: count previous rows within range

更好。我在那里添加了详细的基准测试。

Answer 3

这里是 "a way"，上面提到，使用 LATERAL 来获取使用窗口函数无法实现的动态计数。

SQL Fiddle

PostgreSQL 9.3 架构设置:

CREATE TABLE SimpleTable
    ("id" int, "client_id" int, "tservice" timestamp)
;

INSERT INTO SimpleTable
    ("id", "client_id", "tservice")
VALUES
    (1, 100, '2001-01-01 00:00:00'),
    (2, 100, '2002-01-01 00:00:00'),
    (3, 100, '2003-01-01 00:00:00'),
    (4, 100, '2004-01-01 00:00:00'),
    (5, 100, '2005-01-01 00:00:00'),
    (6, 100, '2006-01-01 00:00:00'),
    (7, 100, '2007-01-01 00:00:00'),
    (8, 100, '2008-01-01 00:00:00'),
    (9, 100, '2009-01-01 00:00:00'),
    (10, 100, '2010-01-01 00:00:00'),
    (11, 100, '2011-01-01 00:00:00'),
    (12, 100, '2012-01-01 00:00:00'),
    (13, 100, '2013-01-01 00:00:00'),
    (14, 100, '2014-01-01 00:00:00'),
    (15, 100, '2015-01-01 00:00:00'),
    (16, 100, '2016-01-01 00:00:00')
;

查询 1:

select
*
from SimpleTable
cross join lateral (
                    select count(*) as countLT3yrs
                    from SimpleTable st 
                    where st.client_id = SimpleTable.client_id
                    and st.tservice >= (SimpleTable.tservice - INTERVAL '3 years')
                    and st.tservice < SimpleTable.tservice
                    ) x

Results:

| id | client_id |                  tservice | countlt3yrs |
|----|-----------|---------------------------|-------------|
|  1 |       100 | January, 01 2001 00:00:00 |           0 |
|  2 |       100 | January, 01 2002 00:00:00 |           1 |
|  3 |       100 | January, 01 2003 00:00:00 |           2 |
|  4 |       100 | January, 01 2004 00:00:00 |           3 |
|  5 |       100 | January, 01 2005 00:00:00 |           3 |
|  6 |       100 | January, 01 2006 00:00:00 |           3 |
|  7 |       100 | January, 01 2007 00:00:00 |           3 |
|  8 |       100 | January, 01 2008 00:00:00 |           3 |
|  9 |       100 | January, 01 2009 00:00:00 |           3 |
| 10 |       100 | January, 01 2010 00:00:00 |           3 |
| 11 |       100 | January, 01 2011 00:00:00 |           3 |
| 12 |       100 | January, 01 2012 00:00:00 |           3 |
| 13 |       100 | January, 01 2013 00:00:00 |           3 |
| 14 |       100 | January, 01 2014 00:00:00 |           3 |
| 15 |       100 | January, 01 2015 00:00:00 |           3 |
| 16 |       100 | January, 01 2016 00:00:00 |           3 |

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