MIN (window)
Written by
Updated at May 30, 2024
Function MIN
is also found in the following categories: Aggregate functions.
Syntax
Standard
Extended
MIN( value
TOTAL | WITHIN ... | AMONG ...
[ BEFORE FILTER BY ... ]
)
More info:
Description
Returns the minimum value.
If value
:
- number — Returns the smallest number.
- date — Returns the earliest date.
- string — Returns the first value in the alphabetic order.
Argument types:
value
—Boolean | Date | Datetime | Fractional number | Integer | String | UUID
Return type: Same type as (value
)
Example
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]
.
Formulas:
- City:
[City]
; - Category:
[Category]
; - Order Sum:
SUM([Orders])
; - MIN TOTAL:
MIN(SUM([Orders]) TOTAL)
; - MIN WITHIN:
MIN(SUM([Orders]) WITHIN [City])
; - MIN AMONG:
MIN(SUM([Orders]) AMONG [City])
.
Result
City | Category | Order Sum | MIN TOTAL | MIN WITHIN | MIN AMONG |
---|---|---|---|---|---|
'Detroit' |
'Furniture' |
7 |
1 |
7 |
1 |
'Detroit' |
'Office Supplies' |
25 |
1 |
7 |
4 |
'London' |
'Furniture' |
1 |
1 |
1 |
1 |
'London' |
'Office Supplies' |
10 |
1 |
1 |
4 |
'Moscow' |
'Furniture' |
2 |
1 |
2 |
1 |
'Moscow' |
'Office Supplies' |
4 |
1 |
2 |
4 |
'San Francisco' |
'Furniture' |
5 |
1 |
5 |
1 |
'San Francisco' |
'Office Supplies' |
23 |
1 |
5 |
4 |
Data source support
ClickHouse 21.8
, Microsoft SQL Server 2017 (14.0)
, MySQL 5.7
, Oracle Database 12c (12.1)
, PostgreSQL 9.3
.