I have a table that contains information about log-in events. Every time a user logs in, a record is added containing the user and the date. I want to calculate a new column in that table that holds the number of times that user has logged in in the past 31 days (including the current attempt). This is a simplified version of what my table looks like, including the column I want to add:
UserID Date LoginsInPast31Days
-------- ------------- --------------------
1 01-01-2012 1
2 02-01-2012 1
2 10-01-2012 2
1 25-01-2012 2
2 03-02-2012 2
2 22-03-2012 1
I know how to calculate a total amount of login attempts: I'd use COUNT(*) OVER (PARTITION BY UserId ORDER BY Date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW). However, I want to limit the timeframe to the last 31 days. My guess is that I have to change the UNBOUNDED PRECEDING, but how do I alter it in such a way that it select the right amount of rows?
One pretty efficient way is to add a record 30 days after each date. It looks like this:
select userid, dte,
sum(inc) over (partition by userid order by dte) as LoginsInPast31Days
from ((select distinct userid, logindate as dte, 1 as inc from logins) union all
(select distinct userid, dateadd(day, 31, dte, -1 as inc from logins)
) l;
You're almost there, 2 adjustments:
First make sure to group by user and date so you know how many rows to select
Secondly, you'll need to use 'ROWS BETWEEN CURRENT ROW AND 31 FOLLOWING' since you cannot limit the number of preceding records to use. By using descending sort order, you'll get the required result.
Combine these tips and you'll get:
SELECT SUM(COUNT(*)) OVER (
PARTITION BY t.userid_KEY
ORDER BY CAST(t.login_ts AS DATE) DESC
ROWS BETWEEN CURRENT ROW AND 31 FOLLOWING
)
FROM table AS t
GROUP BY t.userid, CAST(t.login_ts AS DATE)
Related
My table consists of user_id, revenue, publish_month columns.
Right now I use group_by user_id and sum(revenue) to get revenue for all individual users.
Is there a single SQL query I can use to query for user revenue across a time period conditionally? If for a specific user, there is a row for this month, I want to query for this month, last month and the month before. If there is not yet a row for this month, I want to query for last month and the two months before.
Any advice with which approach to take would be helpful. If I should be using cases, if-elses with exists or if this is do-able with a single SQL query?
UPDATE---since I did a bad job of describing the question, I've come to include some example data and expected results
Where current month is not present for user 33
Where current month is present
Assuming publish_month is a DATE datatype, this should get the most recent three months of data per user...
SELECT
user_id, SUM(revenue) as s_revenue
FROM
(
SELECT
user_id, revenue, publish_month,
MAX(publish_month) OVER (PARTITION BY user_id) AS user_latest_publish_month
FROM
yourtableyoudidnotname
)
summarised
WHERE
publish_month >= DATEADD(month, -2, user_latest_publish_month)
GROUP BY
user_id
If you want to limit that to the most recent 3 months out of the last 4 calendar months, just add AND publish_month >= DATEADD(month, -3, DATE_TRUNC(month, GETDATE()))
The ambiguity here is why it is important to include a Minimal Reproducible Example
With input data and require results, we could test our code against your requirements
If you're using strings for the publish_month, you shouldn't be, and should fix that with utmost urgency.
You can use a windowing function to "number" the months. In this way the most recent one will have a value of 1, the prior 2, and the one before 3. Then you can only select the items with a number of 3 or less.
Here is how:
SELECT user_id, revienue, publish_month,
ROW_NUMBER() OVER(PARTITION BY user_id ORDER BY publish_month DESC) as RN
FROM yourtableyoudidnotname
now you just select the items with RN less than 3 and do your sum
SELECT user_id, SUM(revenue) as s_revenue
FROM (
SELECT user_id, revenue, publish_month,
ROW_NUMBER() OVER(PARTITION BY user_id ORDER BY publish_month DESC) as RN
FROM yourtableyoudidnotname
) X
WHERE RN <= 3
GROUP BY user_id
You could also do this without a sub query if you use the windowing function for SUM and a range, but I think this is easier to understand.
From the comment -- there could be an issue if you have months from more than one year. To solve this make the biggest number in the order by always the most recent. so instead of
ORDER BY publish_month DESC
you would have
ORDER BY (100*publish_year)+publish_month DESC
This means more recent years will always have a higher number so january of 2023 will be 202301 while december of 2022 will be 202212. Since january is a bigger number it will get a row number of 1 and december will get a row number of 2.
I have a table uv_user_date looks like this:
Its basically a user log in table which shows the cumulative login days partition by user_id.
And the column pre show the last login date of a user login record.
Based on this I want to compute the consecutive login days for each user record.
The answer should be :
My idea is : for a record
if(uv_date - pre = 1 day)
then consecutive login days is the last consecutive login days + 1
else
1
but I am having trouble with accessing the last consecutive login days value.
The Code would be:
SELECT *,
if(pre = date_add(uv_date, -1), last(consecutive_days) + 1, 1) consecutive_days
FROM uv_user_date
Is there any way to get the value of last(consecutive_days)
First find date difference
tbl1:
select *,
if(pre = NULL, 1, datediff(uv_date, pre)) as diff
from your_table
then difference between cumulative sum of difference and accumulative_uv_date for each user_id, you want to use it as rank
tbl2:
select *,
sum(diff) over (partition by user_id order by uv_date rows between unbounded preceding and current) - accumulative_uv_date as rnk
from tbl1
finally, count consecutive days
select user_id, uv_date, rnk
row_number() over (partition by user_id, rnk order by uv_date) as consecutive_days
from tbl2
A distribution of # days active within a week: I am trying to find how many members are active for 1 day, 2days, 3days,…7days during a specific week 3/1-3/7.
Is there any way to use aggregate function on top of partition by?
If not what can be used to achieve this?
select distinct memberID,count(date) over(partition by memberID) as no_of_days_active
from visitor
where date between '"2019-01-01 00:00:00"' and '"2019-01-07 00:00:00"'
order by no_of_days_active
result should look something like this
#Days Active Count
1 20
2 32
3 678
4 34
5 3
6 678
7 2345
I think you want two levels of aggregation to count the number of days during the week:
select num_days_active, count(*) as num_members
from (select memberID, count(distinct date::date) as num_days_active
from visitor
where date >= '2019-01-01'::date and
date < '2019-01-08'::date
group by memberID
) v
group by num_days_active
order by num_days_active;
Note that I changed the date comparisons. If you have a time component, then between does not work. And, because you included time in the constant, I added an explicit conversion to date for the count(distinct). That might not be necessary, if date is really a date with no time component.
Piggybacking off of #Gordon's answer, I personally like using a with statement for the subqueries:
with dat as (
select distinct
memberID,
count(date) over(partition by memberID) as no_of_days_active
from visitor
where 1=1
and date between '2019-01-01'::date and '2019-01-07'::date
order by no_of_days_active
)
select
no_of_days_active,
count(no_of_days_active) no_of_days_active_cnt
from dat
group by no_of_days_active
order by no_of_days_active
I have a table with just two columns: User_ID and fail_date. Each time somebody's card is rejected they are logged in the table, their card is automatically tried again 3 days later, and if they fail again, another entry is added to the table. I am trying to write a query that counts unique failures by month so I only want to count the first entry, not the 3 day retries, if they exist. My data set looks like this
user_id fail_date
222 01/01
222 01/04
555 02/15
777 03/31
777 04/02
222 10/11
so my desired output would be something like this:
month unique_fails
jan 1
feb 1
march 1
april 0
oct 1
I'll be running this in Vertica, but I'm not so much looking for perfect syntax in replies. Just help around how to approach this problem as I can't really think of a way to make it work. Thanks!
You could use lag() to get the previous timestamp per user. If the current and the previous timestamp are less than or exactly three days apart, it's a follow up. Mark the row as such. Then you can filter to exclude the follow ups.
It might look something like:
SELECT month,
count(*) unique_fails
FROM (SELECT month(fail_date) month,
CASE
WHEN datediff(day,
lag(fail_date) OVER (PARTITION BY user_id,
ORDER BY fail_date),
fail_date) <= 3 THEN
1
ELSE
0
END follow_up
FROM elbat) x
WHERE follow_up = 0
GROUP BY month;
I'm not so sure about the exact syntax in Vertica, so it might need some adaptions. I also don't know, if fail_date actually is some date/time type variant or just a string. If it's just a string the date/time specific functions may not work on it and have to be replaced or the string has to be converted prior passing it to the functions.
If the data spans several years you might also want to include the year additionally to the month to keep months from different years apart. In the inner SELECT add a column year(fail_date) year and add year to the list of columns and the GROUP BY of the outer SELECT.
You can add a flag about whether this is a "unique_fail" by doing:
select t.*,
(case when lag(fail_date) over (partition by user_id order by fail_date) > fail_date - 3
then 0 else 1
end) as first_failure_flag
from t;
Then, you want to count this flag by month:
select to_char(fail_date, 'Mon'), -- should aways include the year
sum(first_failure_flag)
from (select t.*,
(case when lag(fail_date) over (partition by user_id order by fail_date) > fail_date - 3
then 0 else 1
end) as first_failure_flag
from t
) t
group by to_char(fail_date, 'Mon')
order by min(fail_date)
In a Derived Table, determine the previous fail_date (prev_fail_date), for a specific user_id and fail_date, using a Correlated subquery.
Using the derived table dt, Count the failure, if the difference of number of days between current fail_date and prev_fail_date is greater than 3.
DateDiff() function alongside with If() function is used to determine the cases, which are not repeated tries.
To Group By this result on Month, you can use MONTH function.
But then, the data can be from multiple years, so you need to separate them out yearwise as well, so you can do a multi-level group by, using YEAR function as well.
Try the following (in MySQL) - you can get idea for other RDBMS as well:
SELECT YEAR(dt.fail_date) AS year_fail_date,
MONTH(dt.fail_date) AS month_fail_date,
COUNT( IF(DATEDIFF(dt.fail_date, dt.prev_fail_date) > 3, user_id, NULL) ) AS unique_fails
FROM (
SELECT
t1.user_id,
t1.fail_date,
(
SELECT t2.fail_date
FROM your_table AS t2
WHERE t2.user_id = t1.user_id
AND t2.fail_date < t1.fail_date
ORDER BY t2.fail_date DESC
LIMIT 1
) AS prev_fail_date
FROM your_table AS t1
) AS dt
GROUP BY
year_fail_date,
month_fail_date
ORDER BY
year_fail_date ASC,
month_fail_date ASC
I have a requirement to get values from a table based on an offset conditions on a date column.
Say for eg: for the below attached table, if there is any dates that comes close within 15 days based on effectivedate column I should return only the first one.
So my expected result would be as below:
Here for A1234 policy, it returns 6/18/16 entry and skipped 6/12/16 entry as the offset between these 2 dates is within 15 days and I took the latest one from the list.
If you want to group rows together that are within 15 days of each other, then you have a variant of the gaps-and-islands problem. I would recommend lag() and cumulative sum for this version:
select polno, min(effectivedate), max(expirationdate)
from (select t.*,
sum(case when prev_ed >= dateadd(day, -15, effectivedate)
then 1 else 0
end) over (partition by polno order by effectivedate) as grp
from (select t.*,
lag(expirationdate) over (partition by polno order by effectivedate) as prev_ed
from t
) t
) t
group by polno, grp;