In my table trips , I have two columns: created_at and user_id
My goal is to count unique user_ids per month with a query in postgres. So far, I have written this - but it returns an error
SELECT user_id,
to_char(created_at, 'YYYY-MM') as t COUNT(*)
FROM (SELECT DISTINCT user_id
FROM trips) group by t;
How should I change this query?
The query is much simpler than that:
SELECT to_char(created_at, 'YYYY-MM') as yyyymm, COUNT(DISTINCT user_id)
FROM trips
GROUP BY yyyymm
ORDER BY yyyymm;
Related
I need to group different years in my dataset so that I can see the total number of login_log_id each year has(BigQuery)
SELECT login_log_id,
DATE(login_time) as login_date,
EXTRACT(YEAR FROM login_time) as login_year,
TIME(login_time) as login_time,
FROM `steel-time-347714.flex.logs`
GROUP BY login_log_id
I want to make a group by so that I can see total number of login_log_id generated in different years.
My columns are login_log_id, login_time
I am getting following error :-
SELECT list expression references column login_time which is neither grouped nor aggregated at [2:6]
The error is because every column you refer to in the select need to be aggregated or be in the GROUP BY.
If you want the total logins by year, you can do:
SELECT
EXTRACT(YEAR FROM login_time) as login_year,
COUNT(1) as total_logins,
COUNT(DISTINCT login_log_id) as total_unique_logins
FROM `steel-time-347714.flex.logs`
GROUP BY login_year
But if you want the total by login_log_id and year:
SELECT
login_log_id,
EXTRACT(YEAR FROM login_time) as login_year,
COUNT(1) as total_logins
FROM `steel-time-347714.flex.logs`
GROUP BY login_log_id, login_year
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.
How can I count the new users for each category who bought in the category for the first by year? For instance, 2015-2020 by year, if someone bought in 2015 for the first it will be counted as a new uesr in 2015 but not in 2016-2020.
Table_1 (Columns: product_name, date, category, sales, user_id)
Want to get the result as bleow
You’ll want to start with a sub query to get the first date each user purchased in the category. This is a pretty straightforward group by problem:
select
user_id,
category,
min(date) as first_category_purchase
from my_table
group by user_id, category;
Next, you can use Postgres’s date_trunc function to group by year and category, using your first query as a sub query:
select
category,
date_trunc('year', first_category_purchase)
count(*)
from (
select
user_id,
category,
min(date) as first_category_purchase
from my_table
group by user_id, category
) a
group by 1, 2;
In Postgres, one method is group by after a distinct on:
select date, count(*) as num_new_users
from (select distinct on (user_id, category) t.*
from t
order by user_id, category, date asc
) d
group by date
order by date;
If date is really a date and not a year, then you need something like to_char() or date_trunc() to convert it to a year.
I want to find customers where for example, system by error registered duplicates of an order.
It's pretty easy, if reg_date is EXACTLY the same but I have no idea how to implement it in query to count as duplicate if for example there was up to 1 second difference between transactions.
select * from
(select customer_id, reg_date, count(*) as cnt
from orders
group by 1,2
) x where cnt > 1
Here is example dataset:
https://www.db-fiddle.com/f/m6PhgReSQbVWVZhqe8n4mi/0
CUrrently only customer's 104 orders are counted as duplicates because its reg_date is identical, I want to count also orders 1,2 and 4,5 as there's just 1 second difference
demo:db<>fiddle
SELECT
customer_id,
reg_date
FROM (
SELECT
*,
reg_date - lag(reg_date) OVER (PARTITION BY customer_id ORDER BY reg_date) <= interval '1 second' as is_duplicate
FROM
orders
) s
WHERE is_duplicate
Use the lag() window function. It allows to have a look hat the previous record. With this value you can do a diff and filter the records where the diff time is more than one second.
Try this following script. This will return you day/customer wise duplicates.
SELECT
TO_CHAR(reg_date :: DATE, 'dd/mm/yyyy') reg_date,
customer_id,
count(*) as cnt
FROM orders
GROUP BY
TO_CHAR(reg_date :: DATE, 'dd/mm/yyyy'),
customer_id
HAVING count(*) >1
I have a table with a created timestamp and id identifier.
I can get number of unique id's per week with:
SELECT date_trunc('week', created)::date AS week, count(distinct id)
FROM my_table
GROUP BY week ORDER BY week;
Now I want to have the accumulated number of created by unique id's per week, something like this:
SELECT date_trunc('week', created)::date AS week, count(distinct id),
(SELECT count(distinct id)
FROM my_table
WHERE date_trunc('week', created)::date <= week) as acc
FROM my_table
GROUP BY week ORDER BY week;
But that doesn't work, as week is not accessible in the sub select (ERROR: column "week" does not exist).
How do I solve this?
I'm using PostgreSQL
Use a cumulative aggregation. But, I don't think you need the distinct, so:
SELECT date_trunc('week', created)::date AS week, count(*) as cnt,
SUM(COUNT(*)) OVER (ORDER BY MIN(created)) as running_cnt
FROM my_table
GROUP BY week
ORDER BY week;
In any case, as you've phrased the problem, you can change cnt to use count(distinct). Your subquery is not using distinct at all.
CTEs or a temp table should fix your problem. Here is an example using CTEs.
WITH abc AS (
SELECT date_trunc('week', created)::date AS week, count(distinct id) as IDCount
FROM my_table
GROUP BY week ORDER BY week;
)
SELECT abc.week, abc.IDcount,
(SELECT count(*)
FROM my_table
WHERE date_trunc('week', created)::date <= adc.week) as acc
FROM abc
GROUP BY week ORDER BY abc.week;
Hope this helps