percent_rank(), cume_dist() and ntile()
These three window functions bear a strong family resemblance to each other. The explanation of what they achieve rests on the notion of a centile. A centile is a measure used in statistics to denote the value below which a given fraction of the values in a set falls. The term percentile is often preferred by those who like to express fractions as percentages. For example, the 70th percentile is the value below which 70% of the values fall.
percent_rank()assigns the centile fraction to each value in the input population
cume_dist()function returns, for a specified value within a population, the number of values that are less than or equal to the specified value divided by the total number of values—in other words, the relative position of a value within the population
ntileassigns the bucket number, in an equiheight histogram (see below) to each value in the population by specifying the required number of buckets as the function's actual argument.
Suppose that you want to divide a set of values into N subsets, where each subset contains the same number of values. The result is referred to as an equiheight histogram. This implies unequal value-axis bucket widths. Compare this with the
width_bucket() regular built-in function that produces an equiwidth histogram—that is it marks off the value axis into equal width buckets that, in general each contains a different number of values.
If the required number of unequal width buckets for the equal height histogram is 5, then your goal is equivalently stated by saying that you want to split the population at the values that fall at the 20th, 40th, 60th, and 80th percentiles. In this use case, and if and only if the values are unique and the population size is an exact multiple of the required number of buckets, then any one of
ntile() can be used to meet the goal. But using
ntile() needs noticeably simpler SQL than using the other two because it's tailored for this, but only this, purpose. Moreover, if the set contains duplicate values, then only
ntile() meets the goal (to the extent that this is feasible) because it randomly assigns ties at the boundaries between the buckets so that some go to the higher-value set and some go to the lower-value set. The dedicated section Analyzing a normal distribution with percent_rank(), cume_dist() and ntile() demonstrates this with two large populations of approximately normally distributed values—one with no duplicates and one with lots of duplicates. And it shows that if the population has ties, or when the population size is not an exact multiple of the required number of buckets, then
cume_dist() produce different results from each other, and each divides the population differently than
ntile(), because they handle ties differently and because of other subtle differences.
If you want to split the population into N subsets of unequal population sizes, then you must use one of
This documentation assumes that you already understand the subtle difference between the percentile notion and the cumulative distribution notion, and know how to define your goal with respect to splitting populations of values into subsets—and that you will therefore know how to choose which of the functions
ntile() that you should use.
input value: <no formal parameter> return value: double precision
Purpose: Return the percentile rank of each row within the window , with respect to the argument of the window_definition 's window
ORDER BY clause. The value p returned by
percent_rank() is a number in the range 0 <= p <= 1. It is calculated like this:
percentile_rank = (rank - 1) / ("no. of rows in window" - 1)
Arguably, then, this function is wrongly named because to deserve its name it would return a value in the range 0 through 100.
The lowest value of
percent_rank() within the window will always be 0.0, even when there is a tie between the lowest-ranking rows. The highest possible value of
percent_rank() within the window is 1.0, but this value is seen only when the highest-ranking row has no ties. If the highest-ranking row does have ties, then the highest vale of
percent_rank() within the window will be correspondingly less than 1.0 according to how many rows ties for the top rank.
For example, the query computes the percentile rank for a table "t" with primary key "k" and "score" column, over all the rows:
select k, score, 100*(`percent_rank()` over (order by score)) as "percent_rank()" from t;
percent_rank() for a particular row has a value of, say,
0.42, it means this score is higher than 42% of the other rows.
input value: <no formal parameter> return value: double precision
Purpose: Return a value that represents the number of rows with values less than or equal to the current row’s value divided by the total number of rows—in other words, the relative position of a value in a set of values. The graph of all values of
cume_dist() within the window is known as the cumulative distribution of the argument of the window_definition 's window
ORDER BY clause. The value c returned by
cume_dist() is a number in the range 0 <= c <= 1. It is calculated like this:
cume_dist() = "no of rows with a value <= the current row's value" / "no. of rows in window"
Note: the formula suggests that The return value c will lie in the open-closed range 0 < c <= 1. But the results shown in the study described in the section Analyzing a normal distribution with
ntile() show that the range is indeed the closed-closed 0 <= c <= 1. This is doubtless due to rounding errors in the function's implementation.
You can use
cume_dist() to answer questions like this:
Show me the rows whose score is within the top x% of the window 's population, ranked by score.
prepare top_few_percent(double precision) as with v as ( select score, 100*cume_dist() over (order by dp_score) as cd, k from t) select score, to_char(cd, '999.9') as "cume_dist()", k from v where cd > $1 order by cd desc, k;
input value: no_of_buckets int return value: int
Purpose: Return an integer value for each row that maps it to a corresponding percentile. For example, if you wanted to mark the boundaries between the highest-ranking 20% of rows, the next-ranking 20% of rows, and so on, then you would use
ntile(5). The top 20% of rows would be marked with 1, the next-to-top 20% of rows would be marked with 2, and so on so that the bottom 20% of rows would be marked with 5.
Note: If the number of rows in the window , N, is a multiple of the actual value with which you invoke
n, then each percentile set would have exactly N/n rows. This is achieved, if there are ties right at the boundary between two percentile sets, by randomly assigning some to one set and some to the other. If N is not a multiple of n, then
ntile() assigns the rows to the percentile sets so that the numbers assigned to each are as close as possible to being the same.
Comparing the effect of percent_rank(), cume_dist(), and ntile() on the same input
If you haven't yet installed the tables that the code examples use, then go to the section The data sets used by the code examples.
The query that this section presents shows that the results produced by
cume_dist() are consistent with the formulas for these values that are given in the accounts, above, of these two functions.
Create a data set using the
ysqlsh script that table t2 presents. This has been designed:
- so that the scores for the rows with "class=1" are unique so that the ordering produces no ties
- and so that the scores for the rows with "class=2" have duplicate values so that the ordering does produce ties. The first tie group has three rows; and the second has two rows.
Now do this:
with v1 as ( select class, k, score, (rank() over w1) as r, (percent_rank() over w1) as pr, (cume_dist() over w1) as cd, (ntile(4) over w1) as nt, (count(*) over w2) as n_tot, (count(*) over w3) as n_thru_curr from t2 window w1 as (partition by class order by score), w2 as (w1 range between unbounded preceding and unbounded following), w3 as (w1 range between unbounded preceding and current row)), v2 as ( select class, k, score, n_tot, n_thru_curr, r, (r::numeric - 1.0)/(n_tot::numeric - 1.0) as pr_calc, (n_thru_curr::numeric)/(n_tot::numeric) as cd_calc, pr, cd, nt from v1) select class, k, score, n_tot, n_thru_curr, (pr = pr_calc)::text as "pr check", (cd = cd_calc)::text as "cd check", r as "rank()", to_char(pr*100, '990.9') as "percent_rank()", to_char(cd*100, '990.9') as "cume_dist", nt as "ntile()" from v2 order by class, "rank()", k;
This is the result. To make it easier to see the pattern, several blank lines have been manually inserted here between each successive set of rows with the same value for "class". And in the second set, which has ties, one blank line has been inserted between each tie group.
class | k | score | n_tot | n_thru_curr | pr check | cd check | rank() | percent_rank() | cume_dist | ntile() -------+----+-------+-------+-------------+----------+----------+--------+----------------+-----------+--------- 1 | 1 | 1 | 9 | 1 | true | true | 1 | 0.0 | 11.1 | 1 1 | 2 | 2 | 9 | 2 | true | true | 2 | 12.5 | 22.2 | 1 1 | 3 | 3 | 9 | 3 | true | true | 3 | 25.0 | 33.3 | 1 1 | 4 | 4 | 9 | 4 | true | true | 4 | 37.5 | 44.4 | 2 1 | 5 | 5 | 9 | 5 | true | true | 5 | 50.0 | 55.6 | 2 1 | 6 | 6 | 9 | 6 | true | true | 6 | 62.5 | 66.7 | 3 1 | 7 | 7 | 9 | 7 | true | true | 7 | 75.0 | 77.8 | 3 1 | 8 | 8 | 9 | 8 | true | true | 8 | 87.5 | 88.9 | 4 1 | 9 | 9 | 9 | 9 | true | true | 9 | 100.0 | 100.0 | 4 2 | 10 | 2 | 9 | 3 | true | true | 1 | 0.0 | 33.3 | 1 2 | 11 | 2 | 9 | 3 | true | true | 1 | 0.0 | 33.3 | 1 2 | 12 | 2 | 9 | 3 | true | true | 1 | 0.0 | 33.3 | 1 2 | 13 | 4 | 9 | 4 | true | true | 4 | 37.5 | 44.4 | 2 2 | 14 | 5 | 9 | 5 | true | true | 5 | 50.0 | 55.6 | 2 2 | 15 | 6 | 9 | 6 | true | true | 6 | 62.5 | 66.7 | 3 2 | 16 | 7 | 9 | 8 | true | true | 7 | 75.0 | 88.9 | 4 2 | 17 | 7 | 9 | 8 | true | true | 7 | 75.0 | 88.9 | 3 2 | 18 | 9 | 9 | 9 | true | true | 9 | 100.0 | 100.0 | 4
Notice that in this example, the number of rows per window , 9, is not a multiple of the actual value, 4 with which the function is invoked. This means that the number of rows assigned to each bucket can't be the same. Here, as promised,
ntile() makes a best effort to get the numbers as close to each other as is possible. You can confirm, visually, that the populations are three for one of the four buckets and two for the other three.
Notice, too, what the outcomes are for the tie groups. Each of
cume_dist() produces a different value for each row where "class=1", which has no ties. For "class=2", when there are ties, these functions each produce the same value for all the rows in each of the two tie groups. In contrast,
ntile() assigns the two rows in the second tie group (with "score=7") to different percentile groups.