PERCENTILE_CONT function
PERCENTILE_CONT is an inverse distribution function that assumes a continuous distribution model. It takes a percentile value and a sort specification, and returns an interpolated value that would fall into the given percentile value with respect to the sort specification.
PERCENTILE_CONT computes a linear interpolation between values after ordering them.
Using the percentile value (P)
and the number of not null rows
(N)
in the aggregation group, the function computes the row number after
ordering the rows according to the sort specification. This row number (RN)
is computed according to the formula RN = (1+ (P*(N1))
. The final result
of the aggregate function is computed by linear interpolation between the values from
rows at row numbers CRN = CEILING(RN)
and FRN = FLOOR(RN)
.
The final result will be as follows.
If (CRN = FRN = RN)
then the result is (value of expression from
row at RN)
Otherwise the result is as follows:
(CRN  RN) * (value of expression for row at FRN) + (RN  FRN) * (value of
expression for row at CRN)
.
Syntax
PERCENTILE_CONT(percentile) WITHIN GROUP(ORDER BY expr)
Arguments
 percentile

Numeric constant between 0 and 1.
NULL
values are ignored in the calculation.  expr

Specifies numeric or date/time values to sort and compute the percentile over.
Returns
The return type is determined by the data type of the ORDER BY expression in the WITHIN GROUP clause. The following table shows the return type for each ORDER BY expression data type.
Input type  Return type 

INT2 , INT4 , INT8 , NUMERIC , DECIMAL 
DECIMAL 
FLOAT , DOUBLE 
DOUBLE 
DATE 
DATE 
TIMESTAMP 
TIMESTAMP 
TIMESTAMPTZ 
TIMESTAMPTZ 
Usage notes
If the ORDER BY expression is a DECIMAL data type defined with the maximum precision of 38 digits, it is possible that PERCENTILE_CONT will return either an inaccurate result or an error. If the return value of the PERCENTILE_CONT function exceeds 38 digits, the result is truncated to fit, which causes a loss of precision.. If, during interpolation, an intermediate result exceeds the maximum precision, a numeric overflow occurs and the function returns an error. To avoid these conditions, we recommend either using a data type with lower precision or casting the ORDER BY expression to a lower precision.
If a statement includes multiple calls to sortbased aggregate functions (LISTAGG, PERCENTILE_CONT, or MEDIAN), they must all use the same ORDER BY values. Note that MEDIAN applies an implicit order by on the expression value.
For example, the following statement returns an error.
SELECT TOP 10 salesid, SUM(pricepaid), PERCENTILE_CONT(0.6) WITHIN GROUP(ORDER BY salesid), MEDIAN(pricepaid) FROM sales GROUP BY salesid, pricepaid;
An error occurred when executing the SQL command: SELECT TOP 10 salesid, SUM(pricepaid), PERCENTILE_CONT(0.6) WITHIN GROUP(ORDER BY salesid), MEDIAN(pricepaid) FROM sales GROUP BY salesid, pricepaid; ERROR: within group ORDER BY clauses for aggregate functions must be the same
The following statement runs successfully.
SELECT TOP 10 salesid, SUM(pricepaid), PERCENTILE_CONT(0.6) WITHIN GROUP(ORDER BY salesid), MEDIAN(salesid) FROM sales GROUP BY salesid, pricepaid;
Examples
The following examples use the TICKIT sample database. For more information, see Sample database.
The following example shows that PERCENTILE_CONT(0.5) produces the same results as MEDIAN.
SELECT TOP 10 DISTINCT sellerid, qtysold, PERCENTILE_CONT(0.5) WITHIN GROUP(ORDER BY qtysold), MEDIAN(qtysold) FROM sales GROUP BY sellerid, qtysold;
+++++  sellerid  qtysold  percentile_cont  median  +++++  2  2  2  2   26  1  1  1   33  1  1  1   38  1  1  1   43  1  1  1   48  2  2  2   48  3  3  3   77  4  4  4   85  4  4  4   95  2  2  2  +++++
The following example finds PERCENTILE_CONT(0.5) and PERCENTILE_CONT(0.75) for the quantity sold for each sellerid in the SALES table.
SELECT sellerid, PERCENTILE_CONT(0.5) WITHIN GROUP(ORDER BY qtysold) as pct_05, PERCENTILE_CONT(0.75) WITHIN GROUP(ORDER BY qtysold) as pct_075 FROM sales GROUP BY sellerid ORDER BY sellerid LIMIT 10;
++++  sellerid  pct_05  pct_075  ++++  1  1.5  1.75   2  2  2.25   3  2  3   4  2  2   5  1  1.5   6  1  1   7  1.5  1.75   8  1  1   9  4  4   12  2  3.25  ++++
To verify the results of the previous query for the first sellerid, use the following example.
SELECT qtysold FROM sales WHERE sellerid=1;
++  qtysold  ++  2   1  ++