I have a database table which have field counter that counts number of requests to API (by updating it +1), but I want to get these counts for specific date, (for this month). Is it possible to get it? I am using PostgreSQL.
SQL query
CREATE TABLE IF NOT EXISTS Admin (
id SERIAL PRIMARY KEY,
counter INTEGER NOT NULL DEFAULT 0
created_date TIMESTAMP NOT NULL DEFAULT Now()
);
Thanks in advance.
you can use subquery in two cases:
1- If with each request a field is saved in the database, then you will need the number of all fields per month:
count(counter) -> number of all fields per month.
EXTRACT(MONTH FROM created_date ) = EXTRACT(MONTH FROM Now()) -> date in this month
Query :
select count(counter) as "counter In This month"
from Admin
where created_date in( select created_date from Admin where EXTRACT(MONTH FROM created_date ) = EXTRACT(MONTH FROM Now()));
2- If you update the counter after each request, so that the counter in one day equals the number of all requests on the same day.
sum(counter) -> Total number of all requests per month
EXTRACT(MONTH FROM created_date ) = EXTRACT(MONTH FROM Now()) -> date in this month.
Query :
select sum(counter) as "counter In This month"
from Admin
where created_date in( select created_date from Admin where EXTRACT(MONTH FROM created_date ) = EXTRACT(MONTH FROM Now()));
I would also recommend using date_part function:
SELECT COUNT(counter)
FROM Admin
WHERE date_part('month', created_date) = date_part('month', current_date)
;
Related
I am trying to get the frequency of unique ID values for each month of the last year. However, I don't get the outcome.. including the error message "SELECT list expression references column user_id which is neither grouped nor aggregated".
How can I get the count of unique IDs in each month and them group them by month?
What I tried:
SELECT
user_id,
EXTRACT(MONTH FROM date) as month
FROM
TABLE
WHERE
date >= '2020-09-01'
GROUP BY
month
I want something like this:
month
count of unique user_id
1
300
2
200
...
...
12
250
You would use GROUP BY and COUNT(DISTINCT):
SELECT EXTRACT(MONTH FROM date) as month, COUNT(DISTINCT user_id)
FROM TABLE
WHERE date >= '2020-09-01'
GROUP BY 1;
I would advise you to include the year in the query. In BigQuery, this is simplest using DATE_TRUNC():
SELECT DATE_TRUNC(date, MONTH) as month, COUNT(DISTINCT user_id)
FROM TABLE
WHERE date >= '2020-09-01'
GROUP BY 1;
I am trying to write a query to find month over month percent change in user registration. \
Users table has the logs for user registrations
user_id - pk, integer
created_at - account created date, varchar
activated_at - account activated date, varchar
state - active or pending, varchar
I found the number of users for each year and month. How do I find month over month percent change in user registration? I think I need a window function?
SELECT
EXTRACT(month from created_at::timestamp) as created_month
,EXTRACT(year from created_at::timestamp) as created_year
,count(distinct user_id) as number_of_registration
FROM users
GROUP BY 1,2
ORDER BY 1,2
This is the output of above query:
Then I wrote this to find the difference in user registration in the previous year.
SELECT
*
,number_of_registration - lag(number_of_registration) over (partition by created_month) as difference_in_previous_year
FROM (
SELECT
EXTRACT(month from created_at::timestamp) as created_month
,EXTRACT(year from created_at::timestamp) as created_year
,count( user_id) as number_of_registration
FROM users as u
GROUP BY 1,2
ORDER BY 1,2) as temp
The output is this:
You want an order by clause that contains created_year.
number_of_registration
- lag(number_of_registration) over (partition by created_month order by created_year) as difference_in_previous_year
Note that you don't actually need a subquery for this. You can do:
select
extract(year from created_at) as created_year,
extract(month from created_at) as created_year
count(*) as number_of_registration,
count(*) - lag(count(*)) over(partition by extract(month from created_at) order by extract(year from created_at))
from users as u
group by created_year, created_month
order by created_year, created_month
I used count(*) instead of count(user_id), because I assume that user_id is not nullable (in which case count(*) is equivalent, and more efficient). Casting to a timestamp is also probably superfluous.
These queries work as long as you have data for every month. If you have gaps, then the problem should be addressed differently - but this is not the question you asked here.
I can get the registrations from each year as two tables and join them. But it is not that effective
SELECT
t1.created_year as year_2013
,t2.created_year as year_2014
,t1.created_month as month_of_year
,t1.number_of_registration_2013
,t2.number_of_registration_2014
,(t2.number_of_registration_2014 - t1.number_of_registration_2013) / t1.number_of_registration_2013 * 100 as percent_change_in_previous_year_month
FROM
(select
extract(year from created_at) as created_year
,extract(month from created_at) as created_month
,count(*) as number_of_registration_2013
from users
where extract(year from created_at) = '2013'
group by 1,2) t1
inner join
(select
extract(year from created_at) as created_year
,extract(month from created_at) as created_month
,count(*) as number_of_registration_2014
from users
where extract(year from created_at) = '2014'
group by 1,2) t2
on t1.created_month = t2.created_month
First off, Why are you using strings to hold date/time values? Your 1st step should to define created_at, activated_at as a proper timestamps. In the resulting query I assume this correction. If this is faulty (you do not correct it) then cast the string to timestamp in the CTE generating the date range. But keep in mind that if you leave it as text you will at some point get a conversion exception.
To calculate month-over-month use the formula "100*(Nt - Nl)/Nl" where Nt is the number of users this month and Nl is the number of users last month. There are 2 potential issues:
There are gaps in the data.
Nl is 0 (would incur divide by 0 exception)
The following handles this by first generating the months between the earliest date to the latest date then outer joining monthly counts to the generated dates. When Nl = 0 the query returns NULL indication the percent change could not be calculated.
with full_range(the_month) as
(select generate_series(low_month, high_month, interval '1 month')
from (select min(date_trunc('month',created_at)) low_month
, max(date_trunc('month',created_at)) high_month
from users
) m
)
select to_char(the_month,'yyyy-mm')
, users_this_month
, case when users_last_month = 0
then null::float
else round((100.00*(users_this_month-users_last_month)/users_last_month),2)
end percent_change
from (
select the_month, users_this_month , lag(users_this_month) over(order by the_month) users_last_month
from ( select f.the_month, count(u.created_at) users_this_month
from full_range f
left join users u on date_trunc('month',u.created_at) = f.the_month
group by f.the_month
) mc
) pc
order by the_month;
NOTE: There are several places there the above can be shortened. But the longer form is intentional to show how the final vales are derived.
I'm fairly new to SQL & maybe the complexity level for this report is above my pay grade
I need help to figure out the list of users who are logging to the app consecutively every week in the time period chosen(this logic eventually needs to be extended to a month, quarter & year ultimately but a week is good for now)
Table structure for ref
events: User_id int, login_date timestamp
The table events can have 1 or more entries for a user. This inherently means that the user can login multiple times to the app. To shed some light, if we focus on Jan 2020- Mar2020 then I need the following in the output
user_id who logged into the app every week from 2020wk1 to 2020Wk14
at least once
the week they logged in
number of times they logged in that week
I'm also okay if the output of the query is just the user_id. The thing is I'm unable to make sense out of the output that I'm seeing on my end after trying the following SQL code, perhaps working on this problem for so long might be the reason for that!
SQL code tried so far:
SELECT DISTINCT user_id
,extract('year' FROM timestamp)||'Wk'|| extract('week' FROM timestamp)
,lead(extract('week' FROM timestamp)) over (partition by user_id, extract('week' FROM timestamp) order by extract('week' FROM timestamp))
FROM events
WHERE user_id = 'Anything that u wish to enter'
You can get the summary you want as:
select user_id, date_trunc('week', timestamp) as week, count(*)
from events
group by user_id, week;
But the filtering is tricker. It is better to go with dates rather than week numbers:
select user_id, date_trunc('week', timestamp) as week, count(*) as cnt,
count(*) over (partition by user_id) as num_weeks
from events
where timestamp >= ? and timestamp < ?
group by user_id, week;
Then you can use a subquery:
select uw.*
from (select user_id, date_trunc('week', timestamp) as week, count(*) as cnt,
count(*) over (partition by user_id) as num_weeks
from events
where timestamp >= ? and timestamp < ?
group by user_id, week
) uw
where num_weeks = ? -- 14 in your example
SELECT EVENT_DT - ((EVENT_DT -DATE'1900-01-07') MOD 7) AS dates,
CLSFD_USER_ID AS user_id,
COUNT(DISTINCT CLSFD_USER_ID) AS number_of_user_ids,
COUNT(DISTINCT CLSFD_CAS_AD_ID) AS number_of_ads,
SUM(IMPRSN_CNT) AS number_of_impressions
FROM clsfd_access_views.CLSFD_CAS_AD_HST
WHERE CLSFD_SITE_ID = 3001
AND datum >= '2017-01-01'
GROUP BY 1,2
I want to have the total number of unique users during each month of the year 2017. I tried:
GROUP BY EXTRACT(MONTH FROM datum), 2
But this returns an error. What would be the most efficient code to retrieve the total number of user ids, ads, and impressions, per month.
It doesn't make sense to me to be aggregating by users, since they are what you are trying to count. Try grouping by the month and year alone:
SELECT
EXTRACT(YEAR FROM EVENT_DT) || '-' || EXTRACT(MONTH FROM EVENT_DT) AS month,
COUNT(DISTINCT CLSFD_USER_ID) AS number_of_user_ids,
COUNT(DISTINCT CLSFD_CAS_AD_ID) AS number_of_ads,
SUM(IMPRSN_CNT) AS number_of_impressions
FROM clsfd_access_views.CLSFD_CAS_AD_HST
WHERE
CLSFD_SITE_ID = 3001 AND
datum >= '2017-01-01' AND datum < '2018-01-01'
GROUP BY
EXTRACT(YEAR FROM EVENT_DT) || '-' || EXTRACT(MONTH FROM EVENT_DT);
Note that I changed your restriction on datum to also exclude any year greater than 2017.
If you want this values to be included in current query, then you should use analytical functions. For example "total number of unique users during each month" would be something like:
select count(distinct user_id) over(partition by EXTRACT(MONTH FROM datum))
Be aware that those values will be repeated for each user.
In postgresql, how do I perform a query that returns the sum amounts of rows created of a particular table by month? I would like the result to be something like:
month: January
count: 67
month: February
count: 85
....
....
Let's suppose a I have a table, users. This table has a primary key, id, and a created_at column with time stored in ISO8601 formatting. Last year n number of users were created, and now I want to know how many were created by month, and I want the data returned to me in the above format -- grouped by month and an associated count reflecting how many users were created that month.
Does anyone know how to perform the above SQL query in postgresql?
The query would look something like this:
select date_trunc('month', created_at) as mm, count(*)
from users u
where subscribed = true and
created_at >= '2016-01-01' and
created_at < '2017-01-01'
group by date_trunc('month', created_at);
I don't know where the constant '2017-03-20 13:38:46.688-04' is coming from.
Of course you can make the year comparison dynamic:
select date_trunc('month', created_at) as mm, count(*)
from users u
where subscribed = true and
created_at >= date_trunc('year', now()) - interval '1 year' and
created_at < date_trunc('year', now())
group by date_trunc('month', created_at);