For many years I've been collecting data and I'm interested in knowing the historic counts of IDs that appeared in the last 30 days. The source looks like this
id
dates
1
2002-01-01
2
2002-01-01
3
2002-01-01
...
...
3
2023-01-10
If I wanted to know the historic count of ids that appeared in the last 30 days I would do something like this
with total_counter as (
select id, count(id) counts
from source
group by id
),
unique_obs as (
select id
from source
where dates >= DATEADD(Day ,-30, current_date)
group by id
)
select count(distinct(id))
from unique_obs
left join total_counter
on total_counter.id = unique_obs.id;
The problem is that this results would return a single result for today's count as provided by current_date.
I would like to see a table with such counts as if for example I had ran this analysis yesterday, and the day before and so on. So the expected result would be something like
counts
date
1235
2023-01-10
1234
2023-01-09
1265
2023-01-08
...
...
7383
2022-12-11
so for example, let's say that if the current_date was 2023-01-10, my query would've returned 1235.
If you need a distinct count of Ids from the 30 days up to and including each date the below should work
WITH CTE_DATES
AS
(
--Create a list of anchor dates
SELECT DISTINCT
dates
FROM source
)
SELECT COUNT(DISTINCT s.id) AS "counts"
,D.dates AS "date"
FROM CTE_DATES D
LEFT JOIN source S ON S.dates BETWEEN DATEADD(DAY,-29,D.dates) AND D.dates --30 DAYS INCLUSIVE
GROUP BY D.dates
ORDER BY D.dates DESC
;
If the distinct count didnt matter you could likely simplify with a rolling sum, only hitting the source table once:
SELECT S.dates AS "date"
,COUNT(1) AS "count_daily"
,SUM("count_daily") OVER(ORDER BY S.dates DESC ROWS BETWEEN CURRENT ROW AND 29 FOLLOWING) AS "count_rolling" --assumes there is at least one row for every day.
FROM source S
GROUP BY S.dates
ORDER BY S.dates DESC;
;
This wont work though if you have gaps in your list of dates as it'll just include the latest 30 days available. In which case the first example without distinct in the count will do the trick.
SELECT count(*) AS Counts
dates AS Date
FROM source
WHERE dates >= DATEADD(DAY, -30, CURRENT_DATE)
GROUP BY dates
ORDER BY dates DESC
Related
The question I have is very similar to the question here, but I am using Presto SQL (on aws athena) and couldn't find information on loops in presto.
To reiterate the issue, I want the query that:
Given table that contains: Day, Number of Items for this Day
I want: Day, Average Items for Last 7 Days before "Day"
So if I have a table that has data from Dec 25th to Jan 25th, my output table should have data from Jan 1st to Jan 25th. And for each day from Jan 1-25th, it will be the average number of items from last 7 days.
Is it possible to do this with presto?
maybe you can try this one
calendar Common Table Expression (CTE) is used to generate dates between two dates range.
with calendar as (
select date_generated
from (
values (sequence(date'2021-12-25', date'2022-01-25', interval '1' day))
) as t1(date_array)
cross join unnest(date_array) as t2(date_generated)),
temp CTE is basically used to make a date group which contains last 7 days for each date group.
temp as (select c1.date_generated as date_groups
, format_datetime(c2.date_generated, 'yyyy-MM-dd') as dates
from calendar c1, calendar c2
where c2.date_generated between c1.date_generated - interval '6' day and c1.date_generated
and c1.date_generated >= date'2021-12-25' + interval '6' day)
Output for this part:
date_groups
dates
2022-01-01
2021-12-26
2022-01-01
2021-12-27
2022-01-01
2021-12-28
2022-01-01
2021-12-29
2022-01-01
2021-12-30
2022-01-01
2021-12-31
2022-01-01
2022-01-01
last part is joining day column from your table with each date and then group it by the date group
select temp.date_groups as day
, avg(your_table.num_of_items) avg_last_7_days
from your_table
join temp on your_table.day = temp.dates
group by 1
You want a running average (AVG OVER)
select
day, amount,
avg(amount) over (order by day rows between 6 preceding and current row) as avg_amount
from mytable
order by day
offset 6;
I tried many different variations of getting the "running average" (which I now know is what I was looking for thanks to Thorsten's answer), but couldn't get the output I wanted exactly with my other columns (that weren't included in my original question) in the table, but this ended up working:
SELECT day, <other columns>, avg(amount) OVER (
PARTITION BY <other columns>
ORDER BY date(day) ASC
ROWS 6 PRECEDING) as avg_7_days_amount FROM table ORDER BY date(day) ASC
How to find if an id which was present in previous weeks but not available in current week on a rolling basis. For e.g
Week1 has id 1,2,3,4,5
Week2 has id 3,4,5,7,8
Week3 has id 1,3,5,10,11
So I found out that id 1 and 2 are missing in week 2 and id 2,4,7,8 are missing in week 3 from previous 2 weeks But how to do this on a rolling window for a large amount of data distributed over a period of 20+ years
Please find the sample dataset and expected output. I am expecting the output to be partitioned based on the week_end Date
Dataset
ID|WEEK_START|WEEK_END|APPEARING_DATE
7152|2015-12-27|2016-01-02|2015-12-27
8350|2015-12-27|2016-01-02|2015-12-27
7152|2015-12-27|2016-01-02|2015-12-29
4697|2015-12-27|2016-01-02|2015-12-30
7187|2015-12-27|2016-01-02|2015-01-01
8005|2015-12-27|2016-01-02|2015-12-27
8005|2015-12-27|2016-01-02|2015-12-29
6254|2016-01-03|2016-01-09|2016-01-03
7962|2016-01-03|2016-01-09|2016-01-04
3339|2016-01-03|2016-01-09|2016-01-06
7834|2016-01-03|2016-01-09|2016-01-03
7962|2016-01-03|2016-01-09|2016-01-05
7152|2016-01-03|2016-01-09|2016-01-07
8350|2016-01-03|2016-01-09|2016-01-09
2403|2016-01-10|2016-01-16|2016-01-10
0157|2016-01-10|2016-01-16|2016-01-11
2228|2016-01-10|2016-01-16|2016-01-14
4697|2016-01-10|2016-01-16|2016-01-14
Excepted Output
Partition1: WEEK_END=2016-01-02
ID|MAX(LAST_APPEARING_DATE)
7152|2015-12-29
8350|2015-12-27
4697|2015-12-30
7187|2015-01-01
8005|2015-12-29
Partition1: WEEK_END=2016-01-09
ID|MAX(LAST_APPEARING_DATE)
7152|2016-01-07
8350|2016-01-09
4697|2015-12-30
7187|2015-01-01
8005|2015-12-29
6254|2016-01-03
7962|2016-01-05
3339|2016-01-06
7834|2016-01-03
Partition3: WEEK_END=2016-01-10
ID|MAX(LAST_APPEARING_DATE)
7152|2016-01-07
8350|2016-01-09
4697|2016-01-14
7187|2015-01-01
8005|2015-12-29
6254|2016-01-03
7962|2016-01-05
3339|2016-01-06
7834|2016-01-03
2403|2016-01-10
0157|2016-01-11
2228|2016-01-14
Please use below query,
select ID, MAX(APPEARING_DATE) from table_name
group by ID, WEEK_END;
Or, including WEEK)END,
select ID, WEEK_END, MAX(APPEARING_DATE) from table_name
group by ID, WEEK_END;
You can use aggregation:
select t.*, max(week_end)
from t
group by id
having max(week_end) < '2016-01-02';
Adjust the date in the having clause for the week end that you want.
Actually, your question is a bit unclear. I'm not sure if a later week end would keep the row or not. If you want "as of" data, then include a where clause:
select t.id, max(week_end)
from t
where week_end < '2016-01-02'
group by id
having max(week_end) < '2016-01-02';
If you want this for a range of dates, then you can use a derived table:
select we.the_week_end, t.id, max(week_end)
from (select '2016-01-02' as the_week_end union all
select '2016-01-09' as the_week_end
) we cross join
t
where t.week_end < we.the_week_end
group by id, we.the_week_end
having max(t.week_end) < we.the_week_end;
I have user logins by date. My requirement is to track the number of users that have been logged in during the past 90 days window.
I am new to both SQL in general and Teradata specifically and I can't get the window functionality to work as I need.
I need the following result, where ACTIVE is a count of the unique USER_IDs that appear in the previous 90 day window the DATE.
DATES ACTIVE_IN_WINDOW
12/06/2018 20
13/06/2018 45
14/06/2018 65
15/06/2018 73
17/06/2018 24
18/06/2018 87
19/06/2018 34
20/06/2018 51
Currently my script is as follows.
It is this line here that I cant get right
COUNT ( USER_ID) OVER (PARTITION BY USER_ID ORDER BY EVT_DT ROWS BETWEEN 90 PRECEDING AND 0 FOLLOWING)
I suspect I need a different set of functions to make this work.
SELECT b.DATES , a.ACTIVE_IN_WINDOW
FROM
(
SELECT
CAST(CALENDAR_DATE AS DATE) AS DATES FROM SYS_CALENDAR.CALENDAR
WHERE DATES BETWEEN ADD_MONTHS(CURRENT_DATE, - 10) AND CURRENT_DATE
) b
LEFT JOIN
(
SELECT USER_ID , EVT_DT
, COUNT ( USER_ID) OVER (PARTITION BY USER_ID ORDER BY EVT_DT ROWS BETWEEN 90 PRECEDING AND 0 FOLLOWING) AS ACTIVE_IN_WINDOW
FROM ENV0.R_ONBOARDING
) a
ON a.EVT_DT = b.DATES
ORDER BY b.DATES
Thank you for any assistance.
The logic is similar to Gordon', but a non-equi-Join instead of a Correlated Scalar Subquery is usually more efficient on Teradata:
SELECT b.DATES , Count(DISTINCT USER_ID)
FROM
(
SELECT CALENDAR_DATE AS DATES
FROM SYS_CALENDAR.CALENDAR
WHERE DATES BETWEEN Add_Months(Current_Date, - 10) AND Current_Date
) b
LEFT JOIN
( -- apply DISTINCT before aggregation to reduce intermediate spool
SELECT DISTINCT USER_ID, EVT_DT
FROM ENV0.R_ONBOARDING
) AS a
ON a.EVT_DT BETWEEN Add_Months(b.DATES,-3) AND b.DATES
GROUP BY 1
ORDER BY 1
Of course this will require a large spool and much CPU.
Edit:
Switching to weeks reduces the overhead, I'm using dates instead of week numbers (it's easier to modify for other ranges):
SELECT b.Week , Count(DISTINCT USER_ID)
FROM
( -- Return only Mondays instead of DISTINCT over all days
SELECT calendar_date AS Week
FROM SYS_CALENDAR.CALENDAR
WHERE CALENDAR_DATE BETWEEN Add_Months(Current_Date, -9) AND Current_Date
AND day_of_week = 2 -- 2 = Monday
) b
LEFT JOIN
(
SELECT DISTINCT USER_ID,
-- td_monday returns the previous Monday, but we need the following monday
-- covers the previous Tuesday up to the current Monday
Td_Monday(EVT_DT+6) AS PERIOD_WEEK
FROM ENV0.R_ONBOARDING
-- You should add another condition to limit the actually covered date range, e.g.
-- where EVT_DT BETWEEN Add_Months(b.DATES,-13) AND b.DATES
) AS a
ON a.PERIOD_WEEK BETWEEN b.Week-(12*7) AND b.Week
GROUP BY 1
ORDER BY 1
Explain should duplicate the calendar as preparation for the product join, if not you might need to materialize the dates in a Volatile Table. Better don't use sys_calendar, there are no statistics, e.g. optimizer doesn't know about how many days per week/month/year, etc. Check your system, there should be a calendar table designed for you company needs (with stats on all columns)
If your data is not too big, a subquery might be the simplest method:
SELECT c.dte,
(SELECT COUNT(DISTINCT o.USER_ID)
FROM ENV0.R_ONBOARDING o
WHERE o.EVT_DT > ADD_MONTHS(dte, -3) AND
o.EVT_DT <= dte
) as three_month_count
FROM (SELECT CAST(CALENDAR_DATE AS DATE) AS dte
FROM SYS_CALENDAR.CALENDAR
WHERE CALENDAR_DATE BETWEEN ADD_MONTHS(CURRENT_DATE, - 10) AND CURRENT_DATE
) c;
You might want to start on a shorter timeframe then 3 months to see how the query performs.
I need help in business days calculation.
I've two tables
1) One table ACTUAL_TABLE containing order date and contact date with timestamp datatypes.
2) The second table BUSINESS_DATES has each of the calendar dates listed and has a flag to indicate weekend days.
using these two tables, I need to ensure business days and not calendar days (which is the current logic) is calculated between these two fields.
My thought process was to first get a range of dates by comparing ORDER_DATE with TABLE_DATE field and then do a similar comparison of CONTACT_DATE to TABLE_DATE field. This would get me a range from the BUSINESS_DATES table which I can then use to calculate count of days, sum(Holiday_WKND_Flag) fields making the result look like:
Order# | Count(*) As DAYS | SUM(WEEKEND DATES)
100 | 25 | 8
However this only works when I use a specific order number and cant' bring all order numbers in a sub query.
My Query:
SELECT SUM(Holiday_WKND_Flag), COUNT(*) FROM
(
SELECT
* FROM
BUSINESS_DATES
WHERE BUSINESS.Business BETWEEN (SELECT ORDER_DATE FROM ACTUAL_TABLE
WHERE ORDER# = '100'
)
AND
(SELECT CONTACT_DATE FROM ACTUAL_TABLE
WHERE ORDER# = '100'
)
TEMP
Uploading the table structure for your reference.
SELECT ORDER#, SUM(Holiday_WKND_Flag), COUNT(*)
FROM business_dates bd
INNER JOIN actual_table at ON bd.table_date BETWEEN at.order_date AND at.contact_date
GROUP BY ORDER#
Instead of joining on a BETWEEN (which always results in a bad Product Join) followed by a COUNT you better assign a bussines day number to each date (in best case this is calculated only once and added as a column to your calendar table). Then it's two Equi-Joins and no aggregation needed:
WITH cte AS
(
SELECT
Cast(table_date AS DATE) AS table_date,
-- assign a consecutive number to each busines day, i.e. not increased during weekends, etc.
Sum(CASE WHEN Holiday_WKND_Flag = 1 THEN 0 ELSE 1 end)
Over (ORDER BY table_date
ROWS Unbounded Preceding) AS business_day_nbr
FROM business_dates
)
SELECT ORDER#,
Cast(t.contact_date AS DATE) - Cast(t.order_date AS DATE) AS #_of_days
b2.business_day_nbr - b1.business_day_nbr AS #_of_business_days
FROM actual_table AS t
JOIN cte AS b1
ON Cast(t.order_date AS DATE) = b1.table_date
JOIN cte AS b2
ON Cast(t.contact_date AS DATE) = b2.table_date
Btw, why are table_date and order_date timestamp instead of a date?
Porting from Oracle?
You can use this query. Hope it helps
select order#,
order_date,
contact_date,
(select count(1)
from business_dates_table
where table_date between a.order_date and a.contact_date
and holiday_wknd_flag = 0
) business_days
from actual_table a
Using SQL I need to return a smooth set of results (i.e. one per day) from a dataset that contains 0-N records per day.
The result per day should be the most recent previous value even if that is not from the same day. For example:
Starting data:
Date: Time: Value
19/3/2014 10:01 5
19/3/2014 11:08 3
19/3/2014 17:19 6
20/3/2014 09:11 4
22/3/2014 14:01 5
Required output:
Date: Value
19/3/2014 6
20/3/2014 4
21/3/2014 4
22/3/2014 5
First you need to complete the date range and fill in the missing dates (21/3/2014 in you example). This can be done by either joining a calendar table if you have one, or by using a recursive common table expression to generate the complete sequence on the fly.
When you have the complete sequence of dates finding the max value for the date, or from the latest previous non-null row becomes easy. In this query I use a correlated subquery to do it.
with cte as (
select min(date) date, max(date) max_date from your_table
union all
select dateadd(day, 1, date) date, max_date
from cte
where date < max_date
)
select
c.date,
(
select top 1 max(value) from your_table
where date <= c.date group by date order by date desc
) value
from cte c
order by c.date;
May be this works but try and let me know
select date, value from test where (time,date) in (select max(time),date from test group by date);