Window Functions

column values vs dense_rank vs rank vs row_number

here you can find the functions.

With the table wf_example created in previous example, run:

select i
  , dense_rank() over (order by i)
  , row_number() over ()
  , rank() over (order by i)
from wf_example

The result is:

 i | dense_rank | row_number | rank
---+------------+------------+------
 1 |          1 |          1 |    1
 1 |          1 |          2 |    1
 1 |          1 |          3 |    1
 2 |          2 |          4 |    4
 2 |          2 |          5 |    4
 3 |          3 |          6 |    6
 4 |          4 |          7 |    7
 5 |          5 |          8 |    8
  • dense_rank orders VALUES of i by appearance in window. i=1 appears, so first row has dense_rank, next and third i value does not change, so it is dense_rank shows 1 - FIRST value not changed. fourth row i=2, it is second value of i met, so dense_rank shows 2, andso for the next row. Then it meets value i=3 at 6th row, so it show 3. Same for the rest two values of i. So the last value of dense_rank is the number of distinct values of i.

  • row_number orders ROWS as they are listed.

  • rank Not to confuse with dense_rank this function orders ROW NUMBER of i values. So it starts same with three ones, but has next value 4, which means i=2 (new value) was met at row 4. Same i=3 was met at row 6. Etc..

generic example

Preparing data:

create table wf_example(i int, t text,ts timestamptz,b boolean);
insert into wf_example select 1,'a','1970.01.01',true;
insert into wf_example select 1,'a','1970.01.01',false;
insert into wf_example select 1,'b','1970.01.01',false;
insert into wf_example select 2,'b','1970.01.01',false;
insert into wf_example select 3,'b','1970.01.01',false;
insert into wf_example select 4,'b','1970.02.01',false;
insert into wf_example select 5,'b','1970.03.01',false;
insert into wf_example select 2,'c','1970.03.01',true;

Running:

select *
  , dense_rank() over (order by i) dist_by_i 
  , lag(t) over () prev_t 
  , nth_value(i, 6) over () nth
  , count(true) over (partition by i) num_by_i 
  , count(true) over () num_all
  , ntile(3) over() ntile
from wf_example
;

Result:

 i | t |           ts           | b | dist_by_i | prev_t | nth | num_by_i | num_all | ntile
---+---+------------------------+---+-----------+--------+-----+----------+---------+-------
 1 | a | 1970-01-01 00:00:00+01 | f |         1 |        |   3 |        3 |       8 |     1
 1 | a | 1970-01-01 00:00:00+01 | t |         1 | a      |   3 |        3 |       8 |     1
 1 | b | 1970-01-01 00:00:00+01 | f |         1 | a      |   3 |        3 |       8 |     1
 2 | c | 1970-03-01 00:00:00+01 | t |         2 | b      |   3 |        2 |       8 |     2
 2 | b | 1970-01-01 00:00:00+01 | f |         2 | c      |   3 |        2 |       8 |     2
 3 | b | 1970-01-01 00:00:00+01 | f |         3 | b      |   3 |        1 |       8 |     2
 4 | b | 1970-02-01 00:00:00+01 | f |         4 | b      |   3 |        1 |       8 |     3
 5 | b | 1970-03-01 00:00:00+01 | f |         5 | b      |   3 |        1 |       8 |     3
(8 rows)

Explanation:

dist_by_i: dense_rank() over (order by i) is like a row_number per distinct values. Can be used for the number of distinct values of i (count(DISTINCT i) wold not work). Just use the maximum value.

prev_t: lag(t) over () is a previous value of t over the whole window. mind that it is null for the first row.

nth: nth_value(i, 6) over () is the value of sixth rows column i over the whole window

num_by_i: count(true) over (partition by i) is an amount of rows for each value of i

num_all: count(true) over () is an amount of rows over a whole window

ntile: ntile(3) over() splits the whole window to 3 (as much as possible) equal in quantity parts