I need a daily average for an entire month, but the trick is that all of the clients have different start and end dates. For example, some clients are only enrolled for part of the month. Assume client A is enrolled from 4/3/13-4/8/13, client B from 4/6-4/30, client C from 4/1-5/1, etc. How can I achieve this? Here is my current code which returns super low counts because it assumes all clients are enrolled the entire month:
if exists (
select * from tempdb.dbo.sysobjects o where o.xtype in ('U') and o.id = object_id(N'tempdb..#enrollments_PreviousMonth2')
) DROP TABLE #enrollments_PreviousMonth2;
Select
people_id,
program_modifier,
program_modifier_id,
DATEADD(dd, 0, DATEDIFF(dd, 0, actual_date)) as enroll_midnight_date,
actual_date as enroll_start_date,
end_date as enroll_end_date
INTO #enrollments_PreviousMonth2
From
program_modifier_enrollment_view pmev with(nolock)
Where
program_modifier_id = 'E1AA7A36-0500-4BAE-A0AA-D9E0BC91A6F3' and
actual_date <= '4/30/13' and (end_date >= '4/1/13' or end_date is null)
;with cte as (
select cast(enroll_start_date as date) as actual_date,
count(people_id) cnt
From #enrollments_PreviousMonth2 en
left join Calendar c on en.enroll_midnight_date = c.dt
where program_modifier_id = 'E1AA7A36-0500-4BAE-A0AA-D9E0BC91A6F3'
AND enroll_start_date <= '4/30/13' and (enroll_end_date >= '4/1/13' or enroll_end_date is null)
Group by enroll_start_date--, enroll_end_date, program_modifier_id, program_modifier
)
select
sum(cnt*1.0)
from cte
I prefer to not use a CURSOR for the solution, however.
Related
I have a table with:
ID (id client), date_start (subscription of SaaS), date_end (could be a date value or be NULL).
So I need a cumulative count of active clients month by month.
any idea on how to write that in Postgres and achieve this result?
Starting from this, but I don't know how to proceed
select
date_trunc('month', c.date_start)::date,
count(*)
from customer
Please check next solution:
select
subscrubed_date,
subscrubed_customers,
unsubscrubed_customers,
coalesce(subscrubed_customers, 0) - coalesce(unsubscrubed_customers, 0) cumulative
from (
select distinct
date_trunc('month', c.date_start)::date subscrubed_date,
sum(1) over (order by date_trunc('month', c.date_start)) subscrubed_customers
from customer c
order by subscrubed_date
) subscribed
left join (
select distinct
date_trunc('month', c.date_end)::date unsubscrubed_date,
sum(1) over (order by date_trunc('month', c.date_end)) unsubscrubed_customers
from customer c
where date_end is not null
order by unsubscrubed_date
) unsubscribed on subscribed.subscrubed_date = unsubscribed.unsubscrubed_date;
share SQL query
You have a table of customers. With a start date and sometimes an end date. As you want to group by date, but there are two dates in the table, you need to split these first.
Then, you may have months where only customers came and others where only customers left. So, you'll want a full outer join of the two sets.
For a cumulative sum (also called a running total), use SUM OVER.
with came as
(
select date_trunc('month', date_start) as month, count(*) as cnt
from customer
group by date_trunc('month', date_start)
)
, went as
(
select date_trunc('month', date_end) as month, count(*) as cnt
from customer
where date_end is not null
group by date_trunc('month', date_end)
)
select
month,
came.cnt as cust_new,
went.cnt as cust_gone,
sum(came.cnt - went.cnt) over (order by month) as cust_active
from came full outer join went using (month)
order by month;
I am working with members data. I have the responsible Coach, the coachee entry, exit status and date. Because some coachees might graduate/leave during a month I want to calculate a daily number and then get a monthly average of active members for each coach. That means that I need to take in the account all coachees from previous months, that are still active that current month. This is my data:
I am thinking of creating a variable first where I can get the daily active member count for each coach. This is my first approach:
with all_years as (
select y.year, m.month, d.day
from generate_series(2019, 2022) as y(year)
cross join generate_series(1, 12) as m(month)
cross join generate_series(1, 31) as d(day) --<<*not sure how to adjust for days with less than 31 days??*
select ay.*, coach, coachee, entry_status, entry_date, exit_reason, exit_date, sum(count) over (partition by ay.coach order by ay.year, ay.month, ay.day)
from all_years ay
left join table t
on --.... *not sure what I can join on in this case*;
I am open to an easier approach, this logic is just an idea.
You can cross join the list of distinct coaches with all dates to generat combinations, then bring the table with a left join:
select d.dt, c.coach, count(t.coach) no_coachees
from (select distinct coach from mytable) c
cross join generate_series('2019-01-01'::date, '2022-12-31'::date, '1 day':: interval) d(dt)
left join mytable t on t.coach = c.coach and t.entry_date <= d.dt and t.exit_date > d.dt
group by d.dt, c.coach
Then you can use another level of aggregation to get the monthly average:
select date_trunc('month', d.dt) d_month, coach, avg(no_coachees) avg_coaches
from (
select d.dt, c.coach, count(t.coach) no_coachees
from (select distinct coach from mytable) c
cross join generate_series('2019-01-01'::date, '2022-12-31'::date, '1 day':: interval) d(dt)
left join mytable t on t.coach = c.coach and t.entry_date <= d.dt and t.exit_date > d.dt
group by d.dt, c.coach
) t
group by date_trunc('month', d.dt), coach
I have to calculate the ARPU (Revenue / # users) but I got this error:
subquery uses ungrouped column "usage_records.date" from outer query
LINE 7: WHERE created_at <= date_trunc('day', usage_records.d... ^
Expected results:
Revenue(day) = SUM(quantity_eur) for that day
Users Count (day) = Total signed up users before that day
Postgresql (Query)
SELECT
date_trunc('day', usage_records.date) AS day,
SUM(usage_records.quantity_eur) as Revenue,
( SELECT
COUNT(users.id)
FROM users
WHERE created_at <= date_trunc('day', usage_records.date)
) as users_count
FROM users
INNER JOIN ownerships ON (ownerships.user_id = users.id)
INNER JOIN profiles ON (profiles.id = ownerships.profile_id)
INNER JOIN usage_records ON (usage_records.profile_id = profiles.id)
GROUP BY DAY
ORDER BY DAY asc
your subquery (executed for each row ) cointain a column nont mentioned in group by but not involeved in aggregation ..
this produce error
but you could refactor your query using a contional also for this value
SELECT
date_trunc('day', usage_records.date) AS day,
SUM(usage_records.quantity_eur) as Revenue,
sum( case when created_at <= date_trunc('day', usage_records.date)
AND users.id is not null
then 1 else 0 end ) users_count
FROM users
INNER JOIN ownerships ON (ownerships.user_id = users.id)
INNER JOIN profiles ON (profiles.id = ownerships.profile_id)
INNER JOIN usage_records ON (usage_records.profile_id = profiles.id)
GROUP BY DAY
ORDER BY DAY asc
I found in our database a cursor statement and I would like to replace it.
Declare #max_date datetime
Select #max_date = max(finished) From Payments
Declare #begin_date datetime = '2015-02-01'
Declare #end_of_last_month datetime
While #begin_date <= #max_date
Begin
SELECT #end_of_last_month = CAST(DATEADD(DAY, -1 , DATEFROMPARTS(YEAR(#begin_date),MONTH(#begin_date),1)) AS DATE) --AS end_of_last_month
Insert Into #table(Customer, ArticleTypeID, ArticleType, end_of_month, month, year)
Select Count(distinct (customerId)), prod.ArticleTypeID, at.ArticleType, #end_of_last_month, datepart(month, #end_of_last_month), datepart(year, #end_of_last_month)
From Customer cust
Inner join Payments pay ON pay.member_id = m.member_id
Inner Join Products prod ON prod.product_id = pay.product_id
Inner Join ArticleType at ON at.ArticleTypeID = prod.ArticleTypeID
Where #end_of_last_month between begin_date and expire_date
and completed = 1
Group by prod.ArticleTypeID, at.ArticleType
order by prod.ArticleTypeID, at.ArticleType
Set #begin_date = DATEADD(month, 1, #begin_date)
End
It groups all User per Month where the begin- and expire date in the actual Cursormonth.
Notes:
The user has different payment types, for e.g. 1 Month, 6 Month and so on.
Is it possible to rewrite the code - my problem is only the identification at the where clause (#end_of_last_month between begin_date and expire_date)
How can I handle this with joins or cte's?
What you need first, if not already is a numbers table
Using said Numbers table you can create a dynamic list of dates for "end_of_Last_Month" like so
;WITH ctexAllDates
AS (
SELECT end_of_last_month = DATEADD(DAY, -1, DATEADD(MONTH, N.N -1, #begin_date))
FROM
dbo.Numbers N
WHERE
N.N <= DATEDIFF(MONTH, #begin_date, #max_date) + 1
)
select * FROM ctexAllDates
Then combine with your query like so
;WITH ctexAllDates
AS (
SELECT end_of_last_month = DATEADD(DAY, -1, DATEADD(MONTH, N.N -1, #begin_date))
FROM
dbo.Numbers N
WHERE
N.N <= DATEDIFF(MONTH, #begin_date, #max_date) + 1
)
INSERT INTO #table
(
Customer
, ArticleTypeID
, ArticleType
, end_of_month
, month
, year
)
SELECT
COUNT(DISTINCT (customerId))
, prod.ArticleTypeID
, at.ArticleType
, A.end_of_last_month
, DATEPART(MONTH, A.end_of_last_month)
, DATEPART(YEAR, A.end_of_last_month)
FROM
Customer cust
INNER JOIN Payments pay ON pay.member_id = m.member_id
INNER JOIN Products prod ON prod.product_id = pay.product_id
INNER JOIN ArticleType at ON at.ArticleTypeID = prod.ArticleTypeID
LEFT JOIN ctexAllDates A ON A.end_of_last_month BETWEEN begin_date AND expire_date
WHERE completed = 1
GROUP BY
prod.ArticleTypeID
, at.ArticleType
, A.end_of_last_month
ORDER BY
prod.ArticleTypeID
, at.ArticleType;
I would like to get those customers from a table 'transactions' which haven't created any transactions in the last 6 Months.
Table:
'transactions'
id, email, state, paid_at
To visualise:
|------------------------ time period with all transactions --------------------|
|-- period before month transactions > 0) ---|---- curr month transactions = 0 -|
I guess this is doable with a join showing only those that didn't have any transactions on the right side.
Example:
Month = November
The conditions for the left side should be:
COUNT(l.id) > 0
l.paid_at < '2013-05-01 00:00:00'
Conditions for the right side:
COUNT(r.id) = 0
r.paid_at BETWEEN '2013-05-01 00:00:00' AND '2013-11-30 23:59:59'
Is join the right approach?
Answer
SELECT
C .email
FROM
transactions C
WHERE
(
C .email NOT IN (
SELECT DISTINCT
email
FROM
transactions
WHERE
paid_at >= '2013-05-01 00:00:00'
AND paid_at <= '2013-11-30 23:59:59'
)
AND
C .email IN (
SELECT DISTINCT
email
FROM
transactions
WHERE
paid_at <= '2013-05-01 00:00:00'
)
)
AND c.paid_at <= '2013-11-30 23:59:59'
There are a couple of ways you could do this. Use a subquery to get distinct customer ids for transactions in the last 6 months, and then select customers where their id isn't in the subquery.
select c.id, c.name
from customer c
where c.id not in (select distinct customer_id from transaction where dt between <start> and <end>);
Or, use a left join from customer to transaction, and filter the results to have transaction id null. A left join includes all rows from the left-hand table, even when there are no matching rows in the right-hand table. Explanation of left joins here: http://www.codinghorror.com/blog/2007/10/a-visual-explanation-of-sql-joins.html
select c.id, c.name
from customer c
left join transaction t on c.id = t.customer_id
and t.dt between <start> and <end>
where t.id is null;
The left join approach is likely to be faster.