: Tags PostgreSQL , aggregation , filter , order , within group Background PostgreSQL's analysis functions are still relatively powerful, such as support for multi-dimensional analysis, support for 4 types of aggregation, support for window query, and support for recursive query.
Label
PostgreSQL , 聚合 , filter , order , within group
background
PostgreSQL's analysis functions are still relatively powerful, such as support for multi-dimensional analysis, support for four types of aggregation, support for window queries, and support for recursive queries.
Please refer to the usage of 4 types of aggregation
《PostgreSQL aggregate function 1 : General-Purpose Aggregate Functions》
《PostgreSQL aggregate function 2 : Aggregate Functions for Statistics》
《PostgreSQL aggregate function 3 : Aggregate Functions for Ordered-Set》
《PostgreSQL aggregate function 4 : Hypothetical-Set Aggregate Functions》
For multidimensional analysis, please refer to
《PostgreSQL 9.5 new feature - Support GROUPING SETS, CUBE and ROLLUP.》
For window inquiries, please refer to
"Quick Start PostgreSQL Application Development and Management - 4 Advanced SQL Usage"
For recursive query, please refer to
"Quick Start PostgreSQL Application Development and Management - 3 Accessing Data"
本文主要介绍一下聚合表达式的高级用法
aggregate_name (expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ]
aggregate_name (ALL expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ]
aggregate_name (DISTINCT expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ]
aggregate_name ( * ) [ FILTER ( WHERE filter_clause ) ]
aggregate_name ( [ expression [ , ... ] ] ) WITHIN GROUP ( order_by_clause ) [ FILTER ( WHERE filter_clause ) ]
例子
1. 我们在分组后，需要查出分组中复合条件的count，以及分组的count。
postgres=# create table test(id int, c1 int);
CREATE TABLE
postgres=# insert into test select generate_series(1,10000), random()*10;
INSERT 0 10000
postgres=# select * from test limit 10;
id | c1
----+----
1 | 10
2 | 4
3 | 6
4 | 1
5 | 4
6 | 9
7 | 9
8 | 7
9 | 5
10 | 4
(10 rows)
postgres=# select count(*), count(*) filter (where id<1000) from test group by c1;
count | count
-------+-------
1059 | 118
998 | 109
999 | 101
1010 | 95
468 | 48
544 | 43
964 | 107
956 | 103
1021 | 87
977 | 101
1004 | 87
(11 rows)
2. 我们需要按顺序，将多条记录，聚合为一个字符串或数组，同样也可以加filter，只聚合复合条件的记录
postgres=# select string_agg(id::text, '-' order by id) filter (where id<100) from test group by c1;
string_agg
-------------------------------------------
35-65-74-97
4-12-19-31-36-40-85-89-90-98-99
17-18-22-42-43-44-58-59-64-70-75-83-84
11-14-15-16-21-30-41-54-62-67-73-80-81-94
2-5-10-51-79-93-96
9-26-45-46-47-61
3-27-28-37-48-55-56-68-69-77-92
8-20-24-33-34-49-50-60-63-66-78-91
25-39-53-57-71-76-82-87-95
6-7-29-32-38-72-86-88
1-13-23-52
(11 rows)
3. 我们需要去每个分组，某个字段中值
postgres=# select percentile_cont(0.5) within group (order by id) from test group by c1;
percentile_cont
-----------------
4911.5
5210
4698
4699.5
4955
5061.5
5115
5176
4897.5
5087
4973
(11 rows)
4. 去过滤条件后的中值
postgres=# select percentile_cont(0.5) within group (order by id) filter (where id<100) from test group by c1;
percentile_cont
-----------------
69.5
40
58
47.5
51
45.5
55
49.5
71
35
18
(11 rows)
小结
PostgreSQL 的分析方法很全面，建议用户可以多了解一下我在开始时给出的链接，帮助提高生产力。
参考
https://www.postgresql.org/docs/9.6/static/sql-expressions.html#SYNTAX-AGGREGATES