I'm doing some cohort analysis and want to see for a group of customers in November, how many transact weekly, fortnightly, and monthly; and for how long
I have this for the week and month (weekly example):
WITH weekly_users AS (
SELECT user_fk
, DATE_TRUNC('week',created_at) AS week
, (DATE_PART('year', created_at) - 2016) * 52 + DATE_PART('week', created_at) - 45 AS weeks_between
FROM transactions
WHERE created_at >= '2016-11-01' AND created_at < '2017-12-01'
GROUP BY user_fk, week, weeks_between
),
t2 AS (
SELECT weekly_users.*
, COUNT(*) OVER (PARTITION BY user_fk
ORDER BY week ROWS BETWEEN UNBOUNDED PRECEDING
AND 1 PRECEDING) AS prev_rec_cnt
FROM weekly_users
)
SELECT week
, COUNT(*)
FROM t2
WHERE weeks_between = prev_rec_cnt
GROUP BY week
ORDER BY week;
But weekly is too little of an interval, and monthly too much. So I want fortnight. Has anyone done this before? From Googling it seems like a challenge
Thanks in advance
Just worked it out, this is how you'd do it:
WITH fortnightly_users AS (
SELECT user_fk
, EXTRACT(YEAR FROM created_at) * 100 + CEIL(EXTRACT(WEEK FROM created_at)/2) AS fortnight
, (EXTRACT(YEAR FROM created_at) - 2016) * 26 + CEIL(EXTRACT(WEEK FROM created_at)/2) - 23 AS fortnights_between
FROM transactions
WHERE created_at >= '2016-11-01' AND created_at < '2017-12-01'
GROUP BY user_fk, fortnight, fortnights_between
),
t2 AS (
SELECT fortnightly_users.*
, COUNT(*) OVER (PARTITION BY user_fk
ORDER BY fortnight ROWS BETWEEN UNBOUNDED PRECEDING
AND 1 PRECEDING) AS prev_rec_cnt
FROM fortnightly_users
)
SELECT fortnight
, COUNT(*)
FROM t2
WHERE fortnights_between = prev_rec_cnt
GROUP BY fortnight
ORDER BY fortnight;
So you get the week number, then divide by 2. Rounding up to avoid fractional numbers for fortnights
Related
I have a dataset of sales. To summarize, the structure is
client_id
date_purchase
There might be several purchases done by the same customer on different dates. There can also be several purchases done on the same date (by different or the same customer).
My goal is to get the number of customers, for any given day, that made 2 or more purchases between that day and 90 days prior.
That is, the expected output is
date_purchase
number_of_customers
2022-12-19
200
2022-12-18
194
(...)
Please note this calculates, for any given date, the number of customer with 2+ purchases between that date and 90 days prior.
I know it has something to do with a window function. But so far I have not found a way to calculate, for every window of 90 days, how many customers have done 2+ purchases.
I've tried several window functions with no success:
partition by date_purchase
range between interval '90 days' preceding and current row
So far I can't get to calculate correctly the number for each date.
Window function doesn't seem to be relevant here because there is no relationship between the rows of the same window. A simple query or a self-join query should provide the expected result.
Assuming that client_id and date_purchase are two columns of my_table :
1. Query for a given date reference_date :
SELECT a.reference_date AS date_purchase, count(*) AS number_of_customers
FROM ( SELECT reference_date , client_id
FROM my_table
WHERE date_purchase <= reference_date AND date_purchase >= reference_date - INTERVAL '90 days'
GROUP BY client_id
HAVING count(*) >= 2
) AS a
2. Query for a given interval of dates reference_date => reference_date + INTERVAL '20 days' :
SELECT a.date AS date_purchase, count(*) AS number_of_customers
FROM ( SELECT ref.date, t.client_id
FROM my_table AS t
INNER JOIN generate_series(reference_date, reference_date + INTERVAL '20 days', '1 day') AS ref(date)
ON t.date_purchase <= ref.date AND t.date_purchase >= ref.date - INTERVAL '90 days'
GROUP BY ref.date, t.client_id
HAVING count(*) >= 2
) AS a
GROUP BY a.date
ORDER BY a.date
3. Query for all the date_purchase in mytable :
SELECT a.date AS date_purchase, count(*) AS number_of_customers
FROM ( SELECT ref.date, t.client_id
FROM my_table AS t
INNER JOIN (SELECT DISTINCT date_purchase AS date FROM my_table) AS ref
ON t.date_purchase <= ref.date AND t.date_purchase >= ref.date - INTERVAL '90 days'
GROUP BY ref.date, t.client_id
HAVING count(*) >= 2
) AS a
GROUP BY a.date
ORDER BY a.date
Here is a complex query where i need to pass some dates as dynamic to this, As of now i have hardcoded this '2021-08-01' AND '2022-07-31' these 2 dates.
But i have to pass this dates dynamically in such a way that next dates ie, 2022-06 month , thew dates passed will be '2021-07-01' and '2022-06-30' , basically 12 months behind data.
if we take 2022-05 then the passed date should be '2021-06-01' and '2022-05-31'.
How can we achieve this ? Any suggestions or help will be much appreciated.
below is the query for reference
WITH base as
(
SELECT created_at as period ,order_number, TRIM(email) as email ,is_first_order
FROM orders
WHERE created_at::DATE BETWEEN '2021-08-01' AND '2022-07-31'
)
,base_agg as
(
select TO_CHAR(period,'YYYY-MM') as period
,COUNT(DISTINCT email)FILTER(WHERE is_first_order IS TRUE) as new_users
,COUNT(DISTINCT order_number)FILTER(WHERE is_first_order IS FALSE) as returning_orders
FROM base
GROUP BY 1
)
,base_cumulative as
(
SELECT ROW_NUMBER() OVER(ORDER BY PERIOD DESC ) as rno
,period
,new_users
,returning_orders
,sum("new_users")over (order by "period" asc rows between unbounded preceding and current row) as "cumulative_total"
from base_agg
)
SELECT
(SELECT period FROM base_cumulative WHERE rno=1) period
,(SELECT cumulative_total FROM base_cumulative WHERE rno=1) as cumulated_customers
,SUM(returning_orders) as returning_orders
,SUM(returning_orders)/NULLIF((SELECT cumulative_total FROM base_cumulative WHERE rno=1),0) as rate
FROM base_cumulative
You can calculate the end of current month based on NOW() and some logic, the same can be applied with the rest of the calculation
select date_trunc('month', now())::date + interval '1 month - 1 day' end_of_this_month,
date_trunc('month', now())::date + interval '1 month - 1 day'::interval - '1 year'::interval + '1 day'::interval first_day_of_prev_year_month
;
Result
end_of_this_month | first_day_of_prev_year_month
---------------------+------------------------------
2022-08-31 00:00:00 | 2021-09-01 00:00:00
(1 row)
I have a pickupDate and returnDate in my OrderHistory table. I want to extract the sum of rental days of all OrderHistory entries, grouped/ordered by month. A cte seems to be the solution but I don´t get how to implement it in my query since the cte´s i saw were refering to themselves where it says "FROM cte".
I tried something like this:
SELECT
SUM((EXTRACT (DAY FROM("OrderHistory"."returnDate")-("OrderHistory"."pickupDate")))) as traveltime
, to_char("OrderHistory"."pickupDate"::date, 'YYYY-MM') as M
FROM
"OrderHistory"
GROUP BY
M
ORDER BY
M
But the outcome doesn´t split bookings btw two months (e.g. pickupDate=27th march 2022 and returnDate=03rd of april 2022) but will assign the whole 7 days to the month of march, since the returndate is in it. It should show 4 days in march and 3 in april.
Sorry for the probably very stupid question but I am a beginner. (my code is written in postgresql btw)
PostgreSQL naming conventions
Are PostgreSQL column names case-sensitive?
use legal, lower-case names exclusively so double-quoting is not
needed.
Final result in db fiddle
Add daterange column.
alter table order_history add column date_ranges daterange;
update order_history
with a(m_begin, m_end, pickup_date) as
(select date_trunc('month', pickup_date)::date,
(date_trunc('month', pickup_date) + interval '1 month - 1 day')::date,
pickup_date from order_history)
update order_history set date_ranges =
daterange(a.m_begin, a.m_end,'[]') from a
where a.pickup_date = order_history.pickup_date;
then final query:
WITH A AS(
select
pickup_date,
return_date,
return_date - pickup_date as total,
case when return_date <# date_ranges then (return_date - pickup_date)
else ( date_trunc('month', pickup_date) + interval '1 month - 1 day')::date - pickup_date
end partial_mth
from order_history),
b as (SELECT *, a.total - partial_mth parital_not_mth FROM a)
select *,
case when to_char(pickup_date,'YYYY-MM') = to_char(return_date,'YYYY-MM')
then
sum(partial_mth) over(partition by to_char(pickup_date,'YYYY-MM')) +
sum(parital_not_mth) over (partition by to_char(return_date,'YYYY-MM'))
else sum(partial_mth) over(partition by to_char(pickup_date,'YYYY-MM'))
end
from b;
After trying different things I think I found the best answer to my question, that I want to share with the community:
WITH hier as (
SELECT
"OrderHistory"."pickupDate" as start_date
, "OrderHistory"."returnDate" as end_date
, to_char("OrderHistory"."pickupDate"::date, 'YYYY-MM') as M
FROM
"OrderHistory"
GROUP BY
1, 2, 3
ORDER BY
3
), calendar as (
select date '2022-01-01' + (n || ' days')::interval calendar_date
from generate_series(0, 365) n
)
select
to_char(calendar_date::date, 'YYYY-MM')
, count(*) as tage_gebucht
from calendar
inner join hier on calendar.calendar_date between start_date and end_date
where calendar_date between '2022-01-01' and '2022-12-31'
group by 1
order by 1;
I think this is the simplest solution I came up with.
I want to be able to generate groups of row by days, weeks, month or depending on the interval I set
Following this solution, it works when granularity is by month. But trying the interval of 1 week, no records are being returned.
This is the rows on my table
This is the current query I have for per month interval, which works perfectly.
SELECT *
FROM (
SELECT day::date
FROM generate_series(timestamp '2018-09-01'
, timestamp '2018-12-01'
, interval '1 month') day
) d
LEFT JOIN (
SELECT date_trunc('month', created_date)::date AS day
, SUM(escrow_amount) AS profit, sum(total_amount) as revenue
FROM (
select distinct on (order_id) order_id, escrow_amount, total_amount, create_time from order_item
WHERE created_date >= date '2018-09-01'
AND created_date <= date '2018-12-01'
-- AND ... more conditions
) t2 GROUP BY 1
) t USING (day)
ORDER BY day;
Result from this query
And this is the per week interval query. I will reduce the range to two months for brevity.
SELECT *
FROM (
SELECT day::date
FROM generate_series(timestamp '2018-09-01'
, timestamp '2018-11-01'
, interval '1 week') day
) d
LEFT JOIN (
SELECT date_trunc('week', created_date)::date AS day
, SUM(escrow_amount) AS profit, sum(total_amount) as revenue
FROM (
select distinct on (order_id) order_id, escrow_amount, total_amount, create_time from order_item
WHERE created_date >= date '2018-09-01'
AND created_date <= date '2018-11-01'
-- AND ... more conditions
) t2 GROUP BY 1
) t USING (day)
ORDER BY day;
Take note that I have records from October, but the result here doesn't show anything for October dates.
Any idea what I am missing here?
Results from your first query are not truncated to the begin of the week.
date_trunc('2018-09-01'::date, 'week')::date
is equal to
'2018-08-27'::date
so your join using day is not working
'2018-09-01'::date <> '2018-08-27'::date
Your query should look more like that:
SELECT *
FROM (
SELECT day::date
FROM generate_series(date_trunc('week',timestamp '2018-09-01') --series begin trunc
, timestamp '2018-11-01'
, interval '1 week') day
) d
LEFT JOIN (
SELECT date_trunc('week', created_date::date)::date AS day
, SUM(escrow_amount) AS profit, sum(total_amount) as revenue
FROM (
select distinct on (order_id) order_id, escrow_amount, total_amount, create_time from order_item
WHERE created_date::date >= date '2018-09-01'
AND created_date::date <= date '2018-11-01'
-- AND ... more conditions
) t2 GROUP BY 1
) t USING (day)
WHERE day >= '2018-09-01' --to skip days from begining of the week to the begining of the series before trunc
ORDER BY day;
I'm trying to calculate monthly retention rate in Amazon Redshift and have come up with the following query:
Query 1
SELECT EXTRACT(year FROM activity.created_at) AS Year,
EXTRACT(month FROM activity.created_at) AS Month,
COUNT(DISTINCT activity.member_id) AS active_users,
COUNT(DISTINCT future_activity.member_id) AS retained_users,
COUNT(DISTINCT future_activity.member_id) / COUNT(DISTINCT activity.member_id)::float AS retention
FROM ads.fbs_page_view_staging activity
LEFT JOIN ads.fbs_page_view_staging AS future_activity
ON activity.mongo_id = future_activity.mongo_id
AND datediff ('month',activity.created_at,future_activity.created_at) = 1
GROUP BY Year,
Month
ORDER BY Year,
Month
For some reason this query returns zero retained_users and zero retention. I'd appreciate any help regarding why this may be happening or maybe a completely different query for monthly retention would work.
I modified the query as per another SO post and here it goes:
Query 2
WITH t AS (
SELECT member_id
,date_trunc('month', created_at) AS month
,count(*) AS item_transactions
,lag(date_trunc('month', created_at)) OVER (PARTITION BY member_id
ORDER BY date_trunc('month', created_at))
= date_trunc('month', created_at) - interval '1 month'
OR NULL AS repeat_transaction
FROM ads.fbs_page_view_staging
WHERE created_at >= '2016-01-01'::date
AND created_at < '2016-04-01'::date -- time range of interest.
GROUP BY 1, 2
)
SELECT month
,sum(item_transactions) AS num_trans
,count(*) AS num_buyers
,count(repeat_transaction) AS repeat_buyers
,round(
CASE WHEN sum(item_transactions) > 0
THEN count(repeat_transaction) / sum(item_transactions) * 100
ELSE 0
END, 2) AS buyer_retention
FROM t
GROUP BY 1
ORDER BY 1;
This query gives me the following error:
An error occurred when executing the SQL command:
WITH t AS (
SELECT member_id
,date_trunc('month', created_at) AS month
,count(*) AS item_transactions
,lag(date_trunc('m...
[Amazon](500310) Invalid operation: Interval values with month or year parts are not supported
Details:
-----------------------------------------------
error: Interval values with month or year parts are not supported
code: 8001
context: interval months: "1"
query: 616822
location: cg_constmanager.cpp:145
process: padbmaster [pid=15116]
-----------------------------------------------;
I have a feeling that Query 2 would fare better than Query 1, so I'd prefer to fix the error on that.
Any help would be much appreciated.
Query 1 looks good. I tried similar one. See below. You are using self join on table (ads.fbs_page_view_staging) and the same column (created_at). Assuming mongo_id is unique, the datediff('month'....) will always return 0 and datediff ('month',activity.created_at,future_activity.created_at) = 1 will always be false.
-- Count distinct events of join_col_id that have lapsed for one month.
SELECT count(distinct E.join_col_id) dist_ct
FROM public.fact_events E
JOIN public.dim_table Z
ON E.join_col_id = Z.join_col_id
WHERE datediff('month', event_time, sysdate) = 1;
-- 2771654 -- dist_ct