RANGE Framing is now available in Snowflake. Here as some practical examples of using RANGE Framing in the Window Functions.

Example 1. Running sum of acquitions in the last 28 daysa

Input Table: Acquisitions by Date

article qty date_acquired
A 3 2019-10-11
A 5 2019-10-08
A 10 2019-10-05
A 2 2019-09-15
A 1 2019-09-09
A 1 2019-09-01
B 3 2019-10-11
B 2 2019-10-08
B 3 2019-10-05
B 1 2019-09-15
B 4 2019-09-09
C 1 2019-10-11
C 2 2019-10-08
C 1 2019-10-05
C 1 2019-09-15
C 0 2019-09-09
C 4 2019-09-01
C 1 2019-08-28

RANGE based Window query to get running sum of acquitions in the last 28 days

select 
  *
  , sum(qty) over (partition by article
      order by date_acquired
      range between interval '28 day' preceding and current row
    ) as sum_qty_28_days
from acquisitions;

Query Output:

article qty date_acquired sum_qty_28_days
A 1 2019-09-01 1
A 1 2019-09-09 2
A 2 2019-09-15 4
A 10 2019-10-05 13
A 5 2019-10-08 17
A 3 2019-10-11 20
C 1 2019-08-28 1
C 4 2019-09-01 5
C 0 2019-09-09 5
C 1 2019-09-15 6
C 1 2019-10-05 2
C 2 2019-10-08 4
C 1 2019-10-11 5
B 4 2019-09-09 4
B 1 2019-09-15 5
B 3 2019-10-05 8
B 2 2019-10-08 6
B 3 2019-10-11 9

Example 2. Count of website visits by visitor_id in a 90 day window

Input Table: Visitor Data (visitor_data)

VISITOR_ID DATE_VISITED
1 2022-04-14
3 2022-01-13
3 2022-03-13
3 2022-05-13
5 2022-01-01
5 2022-02-01
5 2022-05-01
5 2022-06-01
5 2022-08-01

SQL Query

select 
  *
  , count(*) over (partition by visitor_id
      order by date_visited
      range between interval '90 day' preceding and current row
    ) as count_90_days
from visitor_data;

Query Output:

VISITOR_ID DATE_VISITED COUNT_90_DAYS
3 2022-01-13 1
3 2022-03-13 2
3 2022-05-13 2
1 2022-04-14 1
5 2022-01-01 1
5 2022-02-01 2
5 2022-05-01 2
5 2022-06-01 2
5 2022-08-01 2

Example 3. Webpage Views. Running sum of pageviews by customer in the last 60 days

Input data

DATE_VIEWED VIEWS CUSTOMER_ID
2020-01-01 1 a
2020-01-15 2 b
2020-01-20 1 a
2020-01-25 20 b
2020-02-15 1 a
2020-03-15 2 b
2020-04-15 1 a
2020-05-15 2 b

SQL Query

select 
  *
  , sum(views) over (partition by customer_id
order by date_viewed 
range between interval '59 day' preceding and current row)
from page_views
;

Query Output

DATE_VIEWED VIEWS CUSTOMER_ID ROLLING_SUM_OF_VIEWS_LAST_60_DAYS
2020-01-01 1 a 1
2020-01-20 1 a 2
2020-02-15 1 a 3
2020-04-15 1 a 1
2020-01-15 2 b 2
2020-01-25 20 b 22
2020-03-15 2 b 22
2020-05-15 2 b 2

Example 4. Moving Average (MA) of the 3 weeks Windows of data.

Analysis Goal

Take all the items in a brand and looking back 3 weeks including current (so 3 rows for each item) and calculate the average of the cycle length column.

Input data

report_date item_id brand cycle_length
2023-09-13 123 Apple 6
2023-09-13 500 Apple 5
2023-09-20 123 Apple 6
2023-09-20 500 Apple 5
2023-09-27 123 Apple 6
2023-09-27 500 Apple 4
2023-10-04 123 Apple 6
2023-10-04 500 Apple 4
2023-09-13 325 Samsung 7
2023-09-13 862 Samsung 3
2023-09-13 455 Samsung 5
2023-09-20 325 Samsung 7
2023-09-20 862 Samsung 3
2023-09-27 455 Samsung 5
2023-10-04 325 Samsung 7
2023-09-27 862 Samsung 4
2023-10-04 455 Samsung 7
2023-10-11 325 Samsung 7
2023-10-04 862 Samsung 4
2023-10-11 455 Samsung 7

SQL Query

select 
  *
  , avg(cycle_length) over (partition by brand
    order by report_date
    range between interval '3 weeks' preceding and current row
  ) as brand_avg_cycle_length_3_weeks
from average_cycle
order by brand, report_date;

Query Output

report_date item_id brand cycle_length brand_avg_cycle_length_3_weeks
2023-09-13 500 Apple 5 5.500
2023-09-13 123 Apple 6 5.500
2023-09-20 123 Apple 6 5.500
2023-09-20 500 Apple 5 5.500
2023-09-27 123 Apple 6 5.333
2023-09-27 500 Apple 4 5.333
2023-10-04 123 Apple 6 5.166
2023-10-04 500 Apple 4 5.166
2023-09-13 455 Samsung 5 5.000
2023-09-13 862 Samsung 3 5.000
2023-09-13 325 Samsung 7 5.000
2023-09-20 325 Samsung 7 5.000
2023-09-20 862 Samsung 3 5.000
2023-09-27 455 Samsung 5 4.857
2023-09-27 862 Samsung 4 4.857
2023-10-04 325 Samsung 7 5.285
2023-10-04 455 Samsung 7 5.285
2023-10-04 862 Samsung 4 5.285
2023-10-11 325 Samsung 7 5.857
2023-10-11 455 Samsung 7 5.857

Example 5. Rolling Average of the 3 days Window of data.

Analysis Goal

Get the rolling average of three days as per sales of the item.

Input Table

sales_date daily_sales salesman items
2021-12-12 12000.30 Max KCR
2021-12-12 32.30 Max Crux
2021-12-12 13000.30 Max Xray
2021-12-13 14000.30 Kyle KCR
2021-12-13 14000.30 Kyle Crux
2021-12-13 99000.30 Kyle XRay
2021-12-14 2340.30 Peter XRay
2021-12-14 1200.30 Peter Crux
2021-12-14 22000.30 Peter KCR
2021-12-15 132000.30 Remo Crux
2021-12-15 124000.30 Rexy KCR
2021-12-15 120500.30 Tom Xray
2021-12-16 122000.30 Felis Crux
2021-12-16 120300.30 Felis KCR
2021-12-16 120040.30 Max Xray
2021-12-17 120005.30 Rubert KCR
2021-12-17 120.30 Travis Crux
2021-12-18 200.30 Peter XRay
2021-12-18 200.30 Peter Crux
2021-12-18 200.30 Peter KCR
2021-12-19 200.30 Peter XRay
2021-12-19 500.30 Peter KCR
2021-12-19 500.30 Peter CRUX
2021-12-20 200.30 Peter XRay
2021-12-20 500.30 Peter KCR
2021-12-20 500.30 Peter CRUX

SQL Query

select 
  items
  , sales_date
  , avg(daily_sales) over (partition by items 
      order by sales_date
      range between interval '2 days' preceding and current row
  ) as three_day_moving_average
from sales_info

Query Output

items sales_date three_day_rolling_average
XRay 2021-12-13 99000.30000
XRay 2021-12-14 50670.30000
XRay 2021-12-18 200.30000
XRay 2021-12-19 200.30000
XRay 2021-12-20 200.30000
Xray 2021-12-12 13000.30000
Xray 2021-12-15 120500.30000
Xray 2021-12-16 120270.30000
CRUX 2021-12-19 500.30000
CRUX 2021-12-20 500.30000
KCR 2021-12-12 12000.30000
KCR 2021-12-13 13000.30000
KCR 2021-12-14 16000.30000
KCR 2021-12-15 53333.63333
KCR 2021-12-16 88766.96666
KCR 2021-12-17 121435.30000
KCR 2021-12-18 80168.63333
KCR 2021-12-19 40235.30000
KCR 2021-12-20 400.30000
Crux 2021-12-12 32.30000
Crux 2021-12-13 7016.30000
Crux 2021-12-14 5077.63333
Crux 2021-12-15 49066.96666
Crux 2021-12-16 85066.96666
Crux 2021-12-17 84706.96666
Crux 2021-12-18 40773.63333