MSUM (window)
Syntax
MSUM( value, rows_1 [ , rows_2 ] )
MSUM( value, rows_1 [ , rows_2 ]
[ TOTAL | WITHIN ... | AMONG ... ]
[ ORDER BY ... ]
[ BEFORE FILTER BY ... ]
)
More info:
Description
Warning
The sorting order is based on the fields listed in the sorting section of the chart and in the ORDER BY
clause. First, ORDER BY
fields are used, and then they are complemented by the fields from the chart.
Returns the moving sum of values in a fixed-size window defined by the sort order and arguments:
rows_1 |
rows_2 |
Window |
---|---|---|
positive | - | The current row and rows_1 preceding rows. |
negative | - | The current row and -rows_1 following rows. |
any sign | any sign | rows_1 preceding rows, the current row and rows_2 following rows. |
Window functions with a similar behavior: MCOUNT, MMIN, MMAX, MAVG.
Argument types:
value
—Fractional number | Integer
rows_1
—Integer
rows_2
—Integer
Return type: Same type as (value
)
Note
Only constant values are accepted for the arguments (rows_1
, rows_2
).
Examples
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]
.
Formulas:
- City:
[City]
; - Order Sum:
SUM([Orders])
; - MSUM 1:
MSUM(SUM([Orders]), 1)
; - MSUM 2:
MSUM(SUM([Orders]), -2)
; - MSUM 3:
MSUM(SUM([Orders]) 1, 1)
.
Result
City | Order Sum | MSUM 1 | MSUM 2 | MSUM 3 |
---|---|---|---|---|
'Detroit' |
32 |
32 |
49 |
43 |
'London' |
11 |
43 |
45 |
49 |
'Moscow' |
6 |
17 |
34 |
45 |
'San Francisco' |
28 |
34 |
28 |
34 |
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]
.
Formulas:
- City:
[City]
; - Order Sum:
SUM([Orders])
; - MSUM 1:
MSUM(SUM([Orders]), 1 ORDER BY [City] DESC)
; - MSUM 2:
MSUM(SUM([Orders]), 1 ORDER BY [Order Sum])
.
Result
City | Order Sum | MSUM 1 | MSUM 2 |
---|---|---|---|
'Detroit' |
32 |
43 |
60 |
'London' |
11 |
17 |
17 |
'Moscow' |
6 |
34 |
6 |
'San Francisco' |
28 |
28 |
39 |
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])
; - MSUM 1:
MSUM(SUM([Orders]), 1 TOTAL ORDER BY [City], [Category])
; - MSUM 2:
MSUM(SUM([Orders]), 1 WITHIN [City] ORDER BY [Category])
; - MSUM 3:
MSUM(SUM([Orders]), 1 AMONG [City] ORDER BY [City])
.
Result
City | Category | Order Sum | MSUM 1 | MSUM 2 | MSUM 3 |
---|---|---|---|---|---|
'Detroit' |
'Furniture' |
7 |
7 |
7 |
7 |
'Detroit' |
'Office Supplies' |
25 |
32 |
32 |
25 |
'London' |
'Furniture' |
1 |
26 |
1 |
8 |
'London' |
'Office Supplies' |
10 |
11 |
11 |
35 |
'Moscow' |
'Furniture' |
2 |
12 |
2 |
3 |
'Moscow' |
'Office Supplies' |
4 |
6 |
6 |
14 |
'San Francisco' |
'Furniture' |
5 |
9 |
5 |
7 |
'San Francisco' |
'Office Supplies' |
23 |
28 |
28 |
27 |
Data source support
ClickHouse 21.8
, Microsoft SQL Server 2017 (14.0)
, MySQL 5.7
, Oracle Database 12c (12.1)
, PostgreSQL 9.3
.