postgresql 使用Tablefunc透视多列

r6l8ljro  于 2023-04-29  发布在  PostgreSQL
关注(0)|答案(3)|浏览(112)

有没有人使用tablefunc来透视多个变量，而不是只使用 row name？The documentation notes：
对于具有相同row_name值的所有行，“额外”列应该相同。
我不知道如何在不结合我想要旋转的列的情况下完成这一任务（我非常怀疑这会给予我所需要的速度）。一种可能的方法是将实体设为数字，并将其添加到localalt中，以毫秒为单位，但这似乎是一种不稳定的方式。
我编辑了回答这个问题时使用的数据：PostgreSQL Crosstab Query。

CREATE TEMP TABLE t4 (
  timeof   timestamp
 ,entity    character
 ,status    integer
 ,ct        integer);

 INSERT INTO t4 VALUES 
  ('2012-01-01', 'a', 1, 1)
 ,('2012-01-01', 'a', 0, 2)
 ,('2012-01-02', 'b', 1, 3)
 ,('2012-01-02', 'c', 0, 4);

 SELECT * FROM crosstab(
     'SELECT timeof, entity, status, ct
      FROM   t4
      ORDER  BY 1,2,3'
     ,$$VALUES (1::text), (0::text)$$)
 AS ct ("Section" timestamp, "Attribute" character, "1" int, "0" int);

退货：

Section                   | Attribute | 1 | 0
---------------------------+-----------+---+---
 2012-01-01 00:00:00       |     a     | 1 | 2
 2012-01-02 00:00:00       |     **b**     | 3 | 4

因此，如文档所述，假设 extra 列（又名“Attribute”）对于每个 * 行名称 （又名“Section”）都是相同的。因此，它报告第二行的B，即使'entity'也具有用于'timeof'值的*'c'**值。
预期输出：

Section                   | Attribute | 1 | 0
--------------------------+-----------+---+---
2012-01-01 00:00:00       |     a     | 1 | 2
2012-01-02 00:00:00       |     b     | 3 |  
2012-01-02 00:00:00       |     c     |   | 4

有什么想法或参考资料吗？
更多背景：我可能需要对数十亿行执行此操作，我正在测试以long和wide格式存储此数据，并查看是否可以使用tablefunc从long格式到wide格式，比使用常规聚合函数更有效。
我每分钟会对大约300个实体进行大约100次测量。通常，我们需要比较给定实体在给定秒内进行的不同测量，因此我们需要经常使用宽格式。此外，对特定实体进行的测量是高度可变的。
编辑：我找到了一个关于这个的资源：http://www.postgresonline.com/journal/categories/24-tablefunc。

postgresql

3条答案

按热度按时间
3j86kqsm

3j86kqsm1#

查询的问题在于**b和c共享相同的时间戳2012-01-02 00:00:00，并且在查询中timestamp列timeof排在第一位，因此-即使您添加了粗体强调-b和c只是属于同一组2012-01-02 00:00:00的额外列。仅返回第一个（b），因为（引用手册）：
row_name列必须是第一列。category和value列必须是最后两列。row_name和category之间的任何列都被视为“extra”。对于具有相同row_name值的所有行，“额外”列预期是相同的**。
大胆强调我的。
只需还原前两列的顺序，使entity作为行名称，它就可以按需要工作：

SELECT *
FROM   crosstab(
   'SELECT entity, timeof, status, ct
    FROM   t4
    ORDER  BY 1'
 , 'VALUES (1), (0)'
   ) AS ct (
      "Attribute" character
    , "Section" timestamp
    , "status_1" int
    , "status_0" int
      );

entity必须是唯一的。

重申

***row_name***第一个 *

  • （可选）**extra*列 * 下一个 *
    category（由第二个参数定义）和    value**last。

额外的列从每个row_name分区的 * 第一 * 行开始填充。忽略其他行的值，每个row_name只有一列要填充。通常情况下，row_name的每一行都是相同的，但这取决于您。
基础知识：

对于不同的设置in your answer：

SELECT localt, entity
     , msrmnt01, msrmnt02, msrmnt03, msrmnt04, msrmnt05  -- , more?
FROM   crosstab(
  'SELECT dense_rank() OVER (ORDER BY localt, entity)::int AS row_name
        , localt, entity -- additional columns
        , msrmnt, val
   FROM   test
-- WHERE  ???   -- instead of LIMIT at the end
   ORDER  BY localt, entity, msrmnt
-- LIMIT ???'   -- instead of LIMIT at the end
, 'SELECT generate_series(1,5)'  -- more?
   ) AS ct (row_name int, localt timestamp, entity int
          , msrmnt01 float8, msrmnt02 float8, msrmnt03 float8, msrmnt04 float8, msrmnt05 float8 -- , more?
            )
LIMIT 1000  -- ?!

难怪你的测试中的查询表现糟糕。您的测试设置有1400万行，在使用LIMIT 1000丢弃大多数行之前，您可以处理所有行。对于精简的结果集，向源查询添加WHERE条件或LIMIT！
此外，您使用的阵列在此基础上不必要地昂贵。我用dense_rank()生成了一个代理行名称。
db〈〉fiddle here-具有更简单的测试设置和更少的行。

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tpxzln5u

tpxzln5u2#

在我最初的问题中，我应该将这个用于我的样本数据：

CREATE TEMP TABLE t4 (
 timeof    date
,entity    integer
,status    integer
,ct        integer);
INSERT INTO t4 VALUES 
 ('2012-01-01', 1, 1, 1)
,('2012-01-01', 1, 0, 2)
,('2012-01-01', 3, 0, 3)
,('2012-01-02', 2, 1, 4)
,('2012-01-02', 3, 1, 5)
,('2012-01-02', 3, 0, 6);

有了这个，我必须同时关注时间和实体。由于tablefunc只使用一列进行旋转，所以需要找到一种方法将两个维度都填充到该列中。（http://www.postgresonline.com/journal/categories/24-tablefunc）。我使用了数组，就像那个链接中的例子一样。

SELECT (timestamp 'epoch' + row_name[1] * INTERVAL '1 second')::date 
           as localt, 
           row_name[2] As entity, status1, status0
FROM crosstab('SELECT ARRAY[extract(epoch from timeof), entity] as row_name,
                    status, ct
               FROM t4 
               ORDER BY timeof, entity, status'
     ,$$VALUES (1::text), (0::text)$$) 
          as ct (row_name integer[], status1 int, status0 int)

FWIW，我尝试使用字符数组，到目前为止，它看起来这是更快的我的设置;9.2.3 PostgreSQL。
这是结果和期望的输出。

localt           | entity | status1 | status0
--------------------------+---------+--------
2012-01-01       |   1    |    1    |   2
2012-01-01       |   3    |         |   3
2012-01-02       |   2    |    4    |  
2012-01-02       |   3    |    5    |   6

我很好奇这在一个更大的数据集上的表现如何，并将在稍后的日期报告。

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fdbelqdn

fdbelqdn3#

好的，我在一个离我的用例更近的table上运行了这个。要么我做错了，要么交叉表不适合我使用。
首先，我做了一些类似的数据：

CREATE TABLE public.test (
    id serial primary key,
    msrmnt integer,
    entity integer,
    localt timestamp,
    val    double precision
);
CREATE INDEX ix_test_msrmnt
   ON public.test (msrmnt);
 CREATE INDEX ix_public_test_201201_entity
   ON public.test (entity);
CREATE INDEX ix_public_test_201201_localt
  ON public.test (localt);
insert into public.test (msrmnt, entity, localt, val)
select *
from(
SELECT msrmnt, entity, localt, random() as val 
FROM generate_series('2012-01-01'::timestamp, '2012-01-01 23:59:00'::timestamp, interval '1 minutes') as localt
join 
(select *
FROM generate_series(1, 50, 1) as msrmnt) as msrmnt
on 1=1
join 
(select *
FROM generate_series(1, 200, 1) as entity) as entity
on 1=1) as data;

然后我运行了几次交叉表代码：

explain analyze
SELECT (timestamp 'epoch' + row_name[1] * INTERVAL '1 second')::date As localt, row_name[2] as entity
    ,msrmnt01,msrmnt02,msrmnt03,msrmnt04,msrmnt05,msrmnt06,msrmnt07,msrmnt08,msrmnt09,msrmnt10
    ,msrmnt11,msrmnt12,msrmnt13,msrmnt14,msrmnt15,msrmnt16,msrmnt17,msrmnt18,msrmnt19,msrmnt20
    ,msrmnt21,msrmnt22,msrmnt23,msrmnt24,msrmnt25,msrmnt26,msrmnt27,msrmnt28,msrmnt29,msrmnt30
    ,msrmnt31,msrmnt32,msrmnt33,msrmnt34,msrmnt35,msrmnt36,msrmnt37,msrmnt38,msrmnt39,msrmnt40
    ,msrmnt41,msrmnt42,msrmnt43,msrmnt44,msrmnt45,msrmnt46,msrmnt47,msrmnt48,msrmnt49,msrmnt50
    FROM crosstab('SELECT ARRAY[extract(epoch from localt), entity] as row_name, msrmnt, val
               FROM public.test
               ORDER BY localt, entity, msrmnt',$$VALUES  ( 1::text),( 2::text),( 3::text),( 4::text),( 5::text),( 6::text),( 7::text),( 8::text),( 9::text),(10::text)
                                                         ,(11::text),(12::text),(13::text),(14::text),(15::text),(16::text),(17::text),(18::text),(19::text),(20::text)
                                                         ,(21::text),(22::text),(23::text),(24::text),(25::text),(26::text),(27::text),(28::text),(29::text),(30::text)
                                                         ,(31::text),(32::text),(33::text),(34::text),(35::text),(36::text),(37::text),(38::text),(39::text),(40::text)
                                                         ,(41::text),(42::text),(43::text),(44::text),(45::text),(46::text),(47::text),(48::text),(49::text),(50::text)$$)
        as ct (row_name integer[],msrmnt01 double precision, msrmnt02 double precision,msrmnt03 double precision, msrmnt04 double precision,msrmnt05 double precision, 
                    msrmnt06 double precision,msrmnt07 double precision, msrmnt08 double precision,msrmnt09 double precision, msrmnt10 double precision
                 ,msrmnt11 double precision, msrmnt12 double precision,msrmnt13 double precision, msrmnt14 double precision,msrmnt15 double precision, 
                    msrmnt16 double precision,msrmnt17 double precision, msrmnt18 double precision,msrmnt19 double precision, msrmnt20 double precision
                 ,msrmnt21 double precision, msrmnt22 double precision,msrmnt23 double precision, msrmnt24 double precision,msrmnt25 double precision, 
                    msrmnt26 double precision,msrmnt27 double precision, msrmnt28 double precision,msrmnt29 double precision, msrmnt30 double precision
                 ,msrmnt31 double precision, msrmnt32 double precision,msrmnt33 double precision, msrmnt34 double precision,msrmnt35 double precision, 
                    msrmnt36 double precision,msrmnt37 double precision, msrmnt38 double precision,msrmnt39 double precision, msrmnt40 double precision
                 ,msrmnt41 double precision, msrmnt42 double precision,msrmnt43 double precision, msrmnt44 double precision,msrmnt45 double precision, 
                    msrmnt46 double precision,msrmnt47 double precision, msrmnt48 double precision,msrmnt49 double precision, msrmnt50 double precision)
limit 1000

在第三次尝试时获得：

QUERY PLAN
Limit  (cost=0.00..20.00 rows=1000 width=432) (actual time=110236.673..110237.667 rows=1000 loops=1)
  ->  Function Scan on crosstab ct  (cost=0.00..20.00 rows=1000 width=432) (actual time=110236.672..110237.598 rows=1000 loops=1)
Total runtime: 110699.598 ms

然后我运行了几次标准溶液：

explain analyze
select localt, entity, 
 max(case when msrmnt =  1 then val else null end) as msrmnt01
,max(case when msrmnt =  2 then val else null end) as msrmnt02
,max(case when msrmnt =  3 then val else null end) as msrmnt03
,max(case when msrmnt =  4 then val else null end) as msrmnt04
,max(case when msrmnt =  5 then val else null end) as msrmnt05
,max(case when msrmnt =  6 then val else null end) as msrmnt06
,max(case when msrmnt =  7 then val else null end) as msrmnt07
,max(case when msrmnt =  8 then val else null end) as msrmnt08
,max(case when msrmnt =  9 then val else null end) as msrmnt09
,max(case when msrmnt = 10 then val else null end) as msrmnt10
,max(case when msrmnt = 11 then val else null end) as msrmnt11
,max(case when msrmnt = 12 then val else null end) as msrmnt12
,max(case when msrmnt = 13 then val else null end) as msrmnt13
,max(case when msrmnt = 14 then val else null end) as msrmnt14
,max(case when msrmnt = 15 then val else null end) as msrmnt15
,max(case when msrmnt = 16 then val else null end) as msrmnt16
,max(case when msrmnt = 17 then val else null end) as msrmnt17
,max(case when msrmnt = 18 then val else null end) as msrmnt18
,max(case when msrmnt = 19 then val else null end) as msrmnt19
,max(case when msrmnt = 20 then val else null end) as msrmnt20
,max(case when msrmnt = 21 then val else null end) as msrmnt21
,max(case when msrmnt = 22 then val else null end) as msrmnt22
,max(case when msrmnt = 23 then val else null end) as msrmnt23
,max(case when msrmnt = 24 then val else null end) as msrmnt24
,max(case when msrmnt = 25 then val else null end) as msrmnt25
,max(case when msrmnt = 26 then val else null end) as msrmnt26
,max(case when msrmnt = 27 then val else null end) as msrmnt27
,max(case when msrmnt = 28 then val else null end) as msrmnt28
,max(case when msrmnt = 29 then val else null end) as msrmnt29
,max(case when msrmnt = 30 then val else null end) as msrmnt30
,max(case when msrmnt = 31 then val else null end) as msrmnt31
,max(case when msrmnt = 32 then val else null end) as msrmnt32
,max(case when msrmnt = 33 then val else null end) as msrmnt33
,max(case when msrmnt = 34 then val else null end) as msrmnt34
,max(case when msrmnt = 35 then val else null end) as msrmnt35
,max(case when msrmnt = 36 then val else null end) as msrmnt36
,max(case when msrmnt = 37 then val else null end) as msrmnt37
,max(case when msrmnt = 38 then val else null end) as msrmnt38
,max(case when msrmnt = 39 then val else null end) as msrmnt39
,max(case when msrmnt = 40 then val else null end) as msrmnt40
,max(case when msrmnt = 41 then val else null end) as msrmnt41
,max(case when msrmnt = 42 then val else null end) as msrmnt42
,max(case when msrmnt = 43 then val else null end) as msrmnt43
,max(case when msrmnt = 44 then val else null end) as msrmnt44
,max(case when msrmnt = 45 then val else null end) as msrmnt45
,max(case when msrmnt = 46 then val else null end) as msrmnt46
,max(case when msrmnt = 47 then val else null end) as msrmnt47
,max(case when msrmnt = 48 then val else null end) as msrmnt48
,max(case when msrmnt = 49 then val else null end) as msrmnt49
,max(case when msrmnt = 50 then val else null end) as msrmnt50
from sample
group by localt, entity
limit 1000

在第三次尝试时获得：

QUERY PLAN
Limit  (cost=2257339.69..2270224.77 rows=1000 width=24) (actual time=19795.984..20090.626 rows=1000 loops=1)
  ->  GroupAggregate  (cost=2257339.69..5968242.35 rows=288000 width=24) (actual time=19795.983..20090.496 rows=1000 loops=1)
        ->  Sort  (cost=2257339.69..2293339.91 rows=14400088 width=24) (actual time=19795.626..19808.820 rows=50001 loops=1)
              Sort Key: localt
              Sort Method: external merge  Disk: 478568kB
              ->  Seq Scan on sample  (cost=0.00..249883.88 rows=14400088 width=24) (actual time=0.013..2245.247 rows=14400000 loops=1)
Total runtime: 20197.565 ms

因此，对于我的情况，到目前为止，交叉表似乎不是一个解决方案。而这只是一天，我会有很多年。事实上，我可能不得不使用宽格式（非规范化）表，尽管为实体进行的测量是可变的，并且会引入新的测量，但我不会在这里深入讨论。
以下是我使用Postgres 9的一些设置。2.3：

name                    setting
max_connections             100
shared_buffers          2097152
effective_cache_size    6291456
maintenance_work_mem    1048576
work_mem                 262144
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