I have two tabels
user
id | name
-------------
1 | User1 |
2 | User2 |
3 | User3 |
4 | User4 |
User can change name in any moment.
And another tabel
order
id |user_name | user_id | price | order_date
---------------------------------------------
1 | OldUser3| 3 | 5 | 2017-07-12 08:01:00.000000
2 | NewUser3| 3 | 6 | 2017-07-12 09:01:00.000000
3 | User1 | 1 | 8 | 2017-07-12 10:01:00.000000
4 | NewUser | | 10 | 2017-07-12 11:01:00.000000
5 | NewUser | | 100 | 2017-07-12 12:01:00.000000
user_name copied from tabel user in moment making order and if user change name several times there are can be diferent value.
user_id can be null if it's not registered user
I need result tabel like this
order
no |user_name | user_id | total_pr| count | last_order
---------------------------------------------
1 | NewUser3| 3 | 11 | 2 |2017-07-12 09:01:00.000000
2 | User1 | 1 | 8 | 1 |2017-07-12 10:01:00.000000
3 | NewUser | | 10 | 1 |2017-07-12 11:01:00.000000
4 | NewUser | | 100 | 1 |2017-07-12 12:01:00.000000
user_name value must take from bigest order_date and need to sort by any column
and if user_id is null that all user with the same name it's different users
I try this
SELECT order.user_id, order.user_name, SUM(price), COUNT(order.user_id), MAX(order_date)
FROM order, user
WHERE
order.order_date >= '2017-07-01 08:01:00.000000'
AND order.order_date <= '2017-07-15 08:01:00.000000'
GROUP BY user_id, user_name ORDER BY count ASC
but its not all
Try this
with users_cte (user_name,user_id,total_pr,count,last_order) as (
--Fetching data for members who are in users table
Select user_name,user_id,total_pr,count,last_order from (
SELECT o.user_name, o.user_id, row_number() over (partition by o.user_id order by order_date desc) rno
, SUM(price) over (partition by o.user_id) as total_pr, COUNT(o.user_id) over(partition by o.user_id) as count , MAX(order_date) over (partition by o.user_id) as last_order
FROM orders o
left join users u
on o.user_id = u.id
WHERE
u.id is not null
and o.order_date >= '2017-07-01 08:01:00.000000'
AND o.order_date <= '2017-07-15 08:01:00.000000'
) A WHere A.rno=1
union all
--Fetching data for new members
SELECT o.user_name,null as user_id
, SUM(price) as total_pr, COUNT(o.user_name), MAX(order_date)
FROM orders o
left join users u
on o.user_id = u.id
WHERE
u.id is null
and o.order_date >= '2017-07-01 08:01:00.000000'
AND o.order_date <= '2017-07-15 08:01:00.000000'
GROUP BY o.user_name
)
Select row_number() over(order by last_order) as no,* from users_cte
try:
SELECT order.id, order.user_name, SUM(price), COUNT(order.user_id), MAX(order_date)
FROM order
LEFT OUTER JOIN user on order.user_id = user.id
WHERE
order.order_date >= '2017-07-01 08:01:00.000000'
AND order.order_date <= '2017-07-15 08:01:00.000000'
GROUP BY user_id, user_name ORDER BY count ASC
Related
Very new to SQL querying. Using PostgreSQL.
I am trying to build a query that tells me what percentage of the time a unique customer id makes multiple transactions on the same day.
I have a query built that gets me the customer ids and transaction dates (if there are multiple on the same day, the date repeats.
Below is my query..
SELECT customer.customer_id, rental_date::date FROM customer
FULL OUTER JOIN rental
ON customer.customer_id = rental.customer_id
FULL OUTER JOIN inventory
ON rental.inventory_id = inventory.inventory_id
FULL OUTER JOIN film
ON inventory.film_id = film.film_id
ORDER BY customer.customer_id, rental_date
Update:
Query now reads:
SELECT customer.customer_id, rental_date::date, COUNT (*)
FROM customer
JOIN rental ON customer.customer_id = rental.customer_id
JOIN inventory ON rental.inventory_id = inventory.inventory_id
JOIN film ON inventory.film_id = film.film_id
GROUP BY customer.customer_id, rental_date
ORDER BY customer.customer_id, rental_date
Output:
+-------------+-------------+-------+
| customer_id | rental_date | count |
+-------------+-------------+-------+
| 1 | 2005-05-25 | 1 |
| 1 | 2005-05-28 | 1 |
| 1 | 2005-06-15 | 1 |
| 1 | 2005-06-15 | 1 |
| 1 | 2005-06-15 | 1 |
| 2 | 2005-06-16 | 1 |
+-------------+-------------+-------+
Desired output:
+-------------+-------------+-------+
| customer_id | rental_date | count |
+-------------+-------------+-------+
| 1 | 2005-05-25 | 1 |
| 1 | 2005-05-28 | 1 |
| 1 | 2005-06-15 | 3 |
| 2 | 2005-06-16 | 1 |
+-------------+-------------+-------+
What you are looking for is count and having. Count will get you the number of purchases by day and Having can be used to eliminate those with 0 or 1 purchases on a given day.
select customer.customer_id, rental_date, count(*)
from customer
join rental on customer.customer_id = rental.customer_id
join inventory on rental.inventory_id = inventory.inventory_id
join film on inventory.film_id = film.film_id
group by customer.customer_id, rental_date
having count(*) > 1
order by customer.customer_id, rental_date ;
Also I doubt you want full outer join. That returns all rows from both the joined tables even when none exist in the other. I change it to an inner join (you only want customers that have rentals and also inventory that also have rentals. Even though now the having would eliminate the extras. Try removing the having clause then run with both full and again inner joins and see the difference.
I am trying to find the daily count of frequent visitors from a very large data-set. Frequent visitors in this case are visitor IDs used on 2 distinct days in a rolling 3 day period.
My data set looks like the below:
ID | Date | Location | State | Brand |
1 | 2020-01-02 | A | CA | XYZ |
1 | 2020-01-03 | A | CA | BCA |
1 | 2020-01-04 | A | CA | XYZ |
1 | 2020-01-06 | A | CA | YQR |
1 | 2020-01-06 | A | WA | XYZ |
2 | 2020-01-02 | A | CA | XYZ |
2 | 2020-01-05 | A | CA | XYZ |
This is the result I am going for. The count in the visits column is equal to the count of distinct days from the date column, -2 days for each ID. So for ID 1 on 2020-01-05, there was a visit on the 3rd and 4th, so the count is 2.
Date | ID | Visits | Frequent Prior 3 Days
2020-01-01 |Null| Null | Null
2020-01-02 | 1 | 1 | No
2020-01-02 | 2 | 1 | No
2020-01-03 | 1 | 2 | Yes
2020-01-03 | 2 | 1 | No
2020-01-04 | 1 | 3 | Yes
2020-01-04 | 2 | 1 | No
2020-01-05 | 1 | 2 | Yes
2020-01-05 | 2 | 1 | No
2020-01-06 | 1 | 2 | Yes
2020-01-06 | 2 | 1 | No
2020-01-07 | 1 | 1 | No
2020-01-07 | 2 | 1 | No
2020-01-08 | 1 | 1 | No
2020-01-09 | 1 | null | Null
I originally tried to use the following line to get the result for the visits column, but end up with 3 in every successive row at whichever date it first got to 3 for that ID.
,
count(ID) over (Partition by ID order by Date ASC rows between 3 preceding and current row) as visits
I've scoured the forum, but every somewhat similar question seems to involve counting the values rather than the dates and haven't been able to figure out how to tweak to get what I need. Any help is much appreciated.
You can aggregate the dataset by user and date, then use window functions with a range frame to look at the three preceding rows.
You did not tell which database you are running - and not all databases support the window ranges, nor have the same syntax for literal intervals. In standard SQL, you would go:
select
id,
date,
count(*) cnt_visits
case
when sum(count(*)) over(
partition by id
order by date
range between interval '3' day preceding and current row
) >= 2
then 'Yes'
else 'No'
end is_frequent_visitor
from mytable
group by id, date
On the other hand, if you want a record for every user and every day (event when there is no visit), then it is a bit different. You can generate the dataset first, then bring the table with a left join:
select
i.id,
d.date,
count(t.id) cnt_visits,
case
when sum(count(t.id)) over(
partition by i.id
order by d.date
rows between '3' day preceding and current row
) >= 2
then 'Yes'
else 'No'
end is_frequent_visitor
from (select distinct id from mytable) i
cross join (select distinct date from mytable) d
left join mytable t
on t.date = d.date
and t.id = i.id
group by i.id, d.date
I would be inclined to approach this by expanding out the days and visitors using a cross join and then just window functions. Assuming you have all dates in the data:
select i.id, d.date,
count(t.id) over (partition by i.id
order by d.date
rows between 2 preceding and current row
) as cnt_visits,
(case when count(t.id) over (partition by i.id
order by d.date
rows between 2 preceding and current row
) >= 2
then 'Yes' else 'No'
end) as is_frequent_visitor
from (select distinct id from t) i cross join
(select distinct date from t) d left join
(select distinct id, date from t) t
on t.date = d.date and
t.id = i.id;
My problem is similar with MySQL: Select rows with more than one occurrence but I am using PostgreSQL. I have a query like:
select d.user_id, d.recorded_at, d.glucose_value, d.unit
from diary as d
join (
select u.id
from health_user as u
join (
select distinct user_id
from care_connect
where clinic_id = 217
and role = 'user'
and status = 'active'
) as c
on u.id = c.user_id
where u.is_tester is false
) as cu
on d.user_id = cu.id
where d.created_at >= d.recorded_at
and d.recorded_at < current_date and d.recorded_at >= current_date - interval '30 days'
and d.glucose_value > 0
and (d.state = 'wakeup' or (d.state = 'before_meal' and d.meal_type = 'breakfast'))
The result looks like:
+---------+---------------------+---------------+--------+
| user_id | recorded_at | glucose_value | unit |
+---------+---------------------+---------------+--------+
| 12041 | 2018-06-26 01:10:12 | 100 | mg/dL |
| 12041 | 2018-06-30 02:10:11 | 90 | mg/dL |
| 12214 | 2018-06-25 12:40:13 | 10 | mmol/L |
| 12214 | 2018-06-26 12:41:13 | 12 | mmol/L |
| 12214 | 2018-06-29 00:21:14 | 11 | mmol/L |
| 12214 | 2018-06-29 12:59:32 | 10 | mmol/L |
+---------+---------------------+---------------+--------+
As you can see that is already a long query with many conditions. Now I want to get only the records that are from users who have no less than four records (rows) in the result, so I tried:
select d.user_id, d.recorded_at, d.glucose_value, d.unit, count(d.*)
from diary as d
join (
select u.id
from health_user as u
join (
select distinct user_id
from care_connect
where clinic_id = 217
and role = 'user'
and status = 'active'
) as c
on u.id = c.user_id
where u.is_tester is false
) as cu
on d.user_id = cu.id
where d.created_at >= d.recorded_at
and d.recorded_at < current_date and d.recorded_at >= current_date - interval '30 days'
and d.glucose_value > 0
and (d.state = 'wakeup' or (d.state = 'before_meal' and d.meal_type = 'breakfast'))
group by d.user_id
having count(d.*) >= 4
My expected output is:
+---------+---------------------+---------------+--------+
| user_id | recorded_at | glucose_value | unit |
+---------+---------------------+---------------+--------+
| 12214 | 2018-06-25 12:40:13 | 10 | mmol/L |
| 12214 | 2018-06-26 12:41:13 | 12 | mmol/L |
| 12214 | 2018-06-29 00:21:14 | 11 | mmol/L |
| 12214 | 2018-06-29 12:59:32 | 10 | mmol/L |
+---------+---------------------+---------------+--------+
However, it throws an error, saying that d.recorded_at should also be added in group by, but that's not what I want. Besides grouping raw timestamps is not meaningful.
I know I can probably join another table, which are generated by the same query but only select d.user_id, count(d.*) at the first line, but the whole query would look crazy.
Would somebody please help me how to achieve this in a better way? Sorry I don't put table structures here, but I can edit and clarify things if needed.
Try this
Select user_id, recorded_at, glucose_value, unit
From (
select d.user_id, d.recorded_at, d.glucose_value, d.unit, count(1) over (partition by d.user_id) rcnt
from diary as d
join (
select u.id
from health_user as u
join (
select distinct user_id
from care_connect
where clinic_id = 217
and role = 'user'
and status = 'active'
) as c
on u.id = c.user_id
where u.is_tester is false
) as cu
on d.user_id = cu.id
where d.created_at >= d.recorded_at
and d.recorded_at < current_date and d.recorded_at >= current_date - interval '30 days'
and d.glucose_value > 0
and (d.state = 'wakeup' or (d.state = 'before_meal' and d.meal_type = 'breakfast'))
) x
Where rcnt >= 4
Try this:
Replace your_query with your actual query.
Using with clause and exists clause.
with original_query as ( your_query )
select * from original_query q1
where
exists( select q2.user_id from original_query q2 where q1.user_id = q2.user_id
group by q2.user_id
having count(q2.user_id) >= 4 )
So, I have the next table:
time | name | ID |
12:00:00| access | 1 |
12:05:00| select | null |
12:10:00| update | null |
12:15:00| insert | null |
12:20:00| out | null |
12:30:00| access | 2 |
12:35:00| select | null |
The table is bigger (aprox 1-1,5 mil rows) and there will be ID equal to 2,3,4 etc and rows between.
The following should be the result:
time | name | ID |
12:00:00| access | 1 |
12:05:00| select | 1 |
12:10:00| update | 1 |
12:15:00| insert | 1 |
12:20:00| out | 1 |
12:30:00| access | 2 |
12:35:00| select | 2 |
What is the most simple method to update the rows without making the log full? Like, one ID at a time.
You can do it with a sub query:
UPDATE YourTable t
SET t.ID = (SELECT TOP 1 s.ID
FROM YourTable s
WHERE s.time < t.time AND s.name = 'access'
ORDER BY s.time DESC)
WHERE t.name <> 'access'
Index on (ID,time,name) will help.
You can do it using CTE as below:
;WITH myCTE
AS ( SELECT time
, name
, ROW_NUMBER() OVER ( PARTITION BY name ORDER BY time ) AS [rank]
, ID
FROM YourTable
)
UPDATE myCTE
SET myCTE.ID = myCTE.rank
SELECT *
FROM YourTable ORDER BY ID
I have query:
Select * from mytable order by 'date'
And result:
date | item_id | user_id | some_data
------------------------------------------
2015-01-01 | 1 | 1 | null
2015-01-01 | 1 | 1 | null
2015-01-02 | 1 | 1 | null
2015-01-03 | 1 | 1 | null
2015-01-03 | 1 | 2 | null
2015-01-04 | 1 | 1 | null
2015-01-05 | 1 | 2 | null
And I want to get position of first row where user_id = 2. In this example it be 5. How to do it?
select pos_overall
from (
select user_id,
row_number() over (order by "date") as pos_overall,
row_number() over (partition by user_id order by "date") as user_pos
from mytable
) t
where user_id = 2
and user_pos = 1
You can use the row_number() function to number the rows in order of date, user_id and then select the minimum value:
select min(rn)
from (
select
user_id, row_number() over (order by date, user_id) as rn
from mytable
) x
where user_id = 2;
If the item_id can change you might want to include that in the order by clause for the row_number function in the derived table.