RANK_PERCENTILE (window)

Syntax

RANK_PERCENTILE( value [ , direction ] )
RANK_PERCENTILE( value [ , direction ]
                 [ TOTAL | WITHIN ... | AMONG ... ]
                 [ BEFORE FILTER BY ... ]
               )

More info:

Description

Returns the relative rank (from 0 to 1) of the current row if ordered by the given argument. Calculated as (RANK(...) - 1) / (row count) .

If direction is "desc" or omitted, then ranking is done from greatest to least, if "asc", then from least to greatest.

See also RANK, RANK_DENSE, RANK_UNIQUE.

Argument types:

  • valueBoolean | Date | Datetime | Fractional number | Integer | String | UUID
  • directionString

Return type: Fractional number

Note

Only constant values are accepted for the arguments (direction).

Examples

Example with two arguments

Source data

Date City Category Orders Profit
'2019-03-01' 'London' 'Office Supplies' 8 120.80
'2019-03-04' 'London' 'Office Supplies' 2 100.00
'2019-03-05' 'London' 'Furniture' 1 750.00
'2019-03-02' 'Moscow' 'Furniture' 2 1250.50
'2019-03-03' 'Moscow' 'Office Supplies' 4 85.00
'2019-03-01' 'San Francisco' 'Office Supplies' 23 723.00
'2019-03-01' 'San Francisco' 'Furniture' 1 1000.00
'2019-03-03' 'San Francisco' 'Furniture' 4 4000.00
'2019-03-02' 'Detroit' 'Furniture' 5 3700.00
'2019-03-04' 'Detroit' 'Office Supplies' 25 1200.00
'2019-03-04' 'Detroit' 'Furniture' 2 3500.00

Grouped by [City].

Sorted by [City].

Result

[City] SUM([Orders]) RANK_PERCENTILE(SUM([Orders]), "desc") RANK_PERCENTILE(SUM([Orders]), "asc")
'Detroit' 32 0.00 1.00
'London' 11 0.67 0.33
'Moscow' 6 1.00 0.00
'San Francisco' 28 0.33 0.67
Example with grouping

Source data

Date City Category Orders Profit
'2019-03-01' 'London' 'Office Supplies' 8 120.80
'2019-03-04' 'London' 'Office Supplies' 2 100.00
'2019-03-05' 'London' 'Furniture' 1 750.00
'2019-03-02' 'Moscow' 'Furniture' 2 1250.50
'2019-03-03' 'Moscow' 'Office Supplies' 4 85.00
'2019-03-01' 'San Francisco' 'Office Supplies' 23 723.00
'2019-03-01' 'San Francisco' 'Furniture' 1 1000.00
'2019-03-03' 'San Francisco' 'Furniture' 4 4000.00
'2019-03-02' 'Detroit' 'Furniture' 5 3700.00
'2019-03-04' 'Detroit' 'Office Supplies' 25 1200.00
'2019-03-04' 'Detroit' 'Furniture' 2 3500.00

Grouped by [City], [Category].

Sorted by [City], [Category].

Result

[City] [Category] SUM([Orders]) RANK_PERCENTILE(SUM([Orders]) TOTAL) RANK_PERCENTILE(SUM([Orders]) WITHIN [City]) RANK_PERCENTILE(SUM([Orders]) AMONG [City])
'Detroit' 'Furniture' 7 0.43 1.00 0.00
'Detroit' 'Office Supplies' 25 0.00 0.00 0.00
'London' 'Furniture' 1 1.00 1.00 1.00
'London' 'Office Supplies' 10 0.29 0.00 0.67
'Moscow' 'Furniture' 2 0.86 1.00 0.67
'Moscow' 'Office Supplies' 4 0.71 0.00 1.00
'San Francisco' 'Furniture' 5 0.57 1.00 0.33
'San Francisco' 'Office Supplies' 23 0.14 0.00 0.33

Data source support

ClickHouse 19.13, Microsoft SQL Server 2017 (14.0), MySQL 5.6, PostgreSQL 9.3.