I have two tables.
One have userid and email (users table). The other have payments information (payments table) from the userid in users.
users
+--------+------------+
| Userid | Name |
+--------+------------+
| 1 | Alex T |
| 2 | Jeremy T |
| 3 | Frederic A |
+--------+------------+
payments
+--------+-----------+------------+----------+
| Userid | ValuePaid | PaidMonths | Refunded |
+--------+-----------+------------+----------+
| 1 | 1 | 12 | null |
| 1 | 20 | 12 | null |
| 1 | 20 | 12 | null |
| 1 | 20 | 1 | null |
| 2 | 1 | 1 | null |
| 2 | 20 | 12 | 1 |
| 2 | 20 | 12 | null |
| 2 | 20 | 1 | null |
| 3 | 1 | 12 | null |
| 3 | 20 | 1 | 1 |
| 3 | 20 | 1 | null |
+--------+-----------+------------+----------+
I want to count the PaidMonths taking in consideration the following rules:
If ValuePaid < 10 PaidMonths should be = 0.23 (even if in the column the value seen is any other mumber).
If Refund=1 the PaidMonths should be = 0.
Based on this when i join both tables by userid, and sum the PaidMonths based in the previousrules, i expect to see as result:
+--------+------------+------------+
| userid | Name | paidMonths |
+--------+------------+------------+
| 1 | Alex T | 25.23 |
| 2 | Jeremy T | 13.23 |
| 3 | Frederic A | 1.23 |
+--------+------------+------------+
Can you help me to achieve this in the most elegant way? Should a temporary table be used?
The following gives your desired results, using apply with case expression to map your values:
select u.UserID, u.Name, Sum(pm) PaidMonths
from users u join payments p on p.userid=u.userid
cross apply (values(
case
when valuepaid <10 then 0.23
when Refunded=1 then 0
else PaidMonths end
))x(pm)
group by u.UserID, u.Name
See Working Fiddle
I have a data orders that looks like this:
| Order | Step | Step Complete Date |
|:-----:|:----:|:------------------:|
| A | 1 | 11/1/2019 |
| | 2 | 11/1/2019 |
| | 3 | 11/1/2019 |
| | 4 | 11/3/2019 |
| | 5 | 11/3/2019 |
| | 6 | 11/5/2019 |
| | 7 | 11/5/2019 |
| B | 1 | 12/1/2019 |
| | 2 | 12/2/2019 |
| | 3 | |
| C | 1 | 10/21/2019 |
| | 2 | 10/23/2019 |
| | 3 | 10/25/2019 |
| | 4 | 10/25/2019 |
| | 5 | 10/25/2019 |
| | 6 | |
| | 7 | 10/27/2019 |
| | 8 | 10/28/2019 |
| | 9 | 10/29/2019 |
| | 10 | 10/30/2019 |
| D | 1 | 10/30/2019 |
| | 2 | 11/1/2019 |
| | 3 | 11/1/2019 |
| | 4 | 11/2/2019 |
| | 5 | 11/2/2019 |
What I need to accomplish is the following:
For each order, assign the 'Order_Completion_Date' field as the most recent 'Step_Complete_Date'. If ANY 'Step_Complete_Date' is NULL, then the value for 'Order_Completion_Date' should be NULL.
I set up a SQL FIDDLE with this data and my attempt, below:
SELECT
OrderNum,
MAX(Step_Complete_Date)
FROM
OrderNums
WHERE
Step_Complete_Date IS NOT NULL
GROUP BY
OrderNum
This is yielding:
ORDERNUM MAX(STEP_COMPLETE_DATE)
D 11/2/2019
A 11/5/2019
B 12/2/2019
C 10/30/2019
How can I achieve:
| OrderNum | Order_Completed_Date |
|:--------:|:--------------------:|
| A | 11/5/2019 |
| B | NULL |
| C | NULL |
| D | 11/2/2019 |
Aggregate function with KEEP can handle this
select ordernum,
max(step_complete_date)
keep (DENSE_RANK FIRST ORDER BY step_complete_date desc nulls first) res
FROM
OrderNums
GROUP BY
OrderNum
You can use a CASE expression to first count if there are any NULL values and if not then find the maximum value:
Query 1:
SELECT OrderNum,
CASE
WHEN COUNT( CASE WHEN Step_Complete_Date IS NULL THEN 1 END ) > 0
THEN NULL
ELSE MAX(Step_Complete_Date)
END AS Order_Completion_Date
FROM OrderNums
GROUP BY OrderNum
Results:
| ORDERNUM | ORDER_COMPLETION_DATE |
|----------|-----------------------|
| D | 11/2/2019 |
| A | 11/5/2019 |
| B | (null) |
| C | (null) |
First, you are representing dates as varchars in mm/dd/yyyy format (at least in fiddle). With max function it can produce incorrect result, try for example order with dates '11/10/2019' and '11/2/2019'.
Second, the most simple solution is IMHO to use fallback date for nulls and get null back when fallback date wins:
SELECT
OrderNum,
NULLIF(MAX(NVL(Step_Complete_Date,'~')),'~')
FROM
OrderNums
GROUP BY
OrderNum
(Example is still for varchars since tilde is greater than any digit. For dates, you could use 9999-12-31, for instance.)
I'm trying to provide rolled up summaries of the following data including only the group in question as well as excluding the group. I think this can be done with a window function, but I'm having problems with getting the syntax down (in my case Hive SQL).
I want the following data to be aggregated
+------------+---------+--------+
| date | product | rating |
+------------+---------+--------+
| 2018-01-01 | A | 1 |
| 2018-01-02 | A | 3 |
| 2018-01-20 | A | 4 |
| 2018-01-27 | A | 5 |
| 2018-01-29 | A | 4 |
| 2018-02-01 | A | 5 |
| 2017-01-09 | B | NULL |
| 2017-01-12 | B | 3 |
| 2017-01-15 | B | 4 |
| 2017-01-28 | B | 4 |
| 2017-07-21 | B | 2 |
| 2017-09-21 | B | 5 |
| 2017-09-13 | C | 3 |
| 2017-09-14 | C | 4 |
| 2017-09-15 | C | 5 |
| 2017-09-16 | C | 5 |
| 2018-04-01 | C | 2 |
| 2018-01-13 | D | 1 |
| 2018-01-14 | D | 2 |
| 2018-01-24 | D | 3 |
| 2018-01-31 | D | 4 |
+------------+---------+--------+
Aggregated results:
+------+-------+---------+----+------------+------------------+----------+
| year | month | product | ct | avg_rating | avg_rating_other | other_ct |
+------+-------+---------+----+------------+------------------+----------+
| 2018 | 1 | A | 5 | 3.4 | 2.5 | 4 |
| 2018 | 2 | A | 1 | 5 | NULL | 0 |
| 2017 | 1 | B | 4 | 3.6666667 | NULL | 0 |
| 2017 | 7 | B | 1 | 2 | NULL | 0 |
| 2017 | 9 | B | 1 | 5 | 4.25 | 4 |
| 2017 | 9 | C | 4 | 4.25 | 5 | 1 |
| 2018 | 4 | C | 1 | 2 | NULL | 0 |
| 2018 | 1 | D | 4 | 2.5 | 3.4 | 5 |
+------+-------+---------+----+------------+------------------+----------+
I've also considered producing two aggregates, one with the product in question and one without, but having trouble with creating the appropriate joining key.
You can do:
select year(date), month(date), product,
count(*) as ct, avg(rating) as avg_rating,
sum(count(*)) over (partition by year(date), month(date)) - count(*) as ct_other,
((sum(sum(rating)) over (partition by year(date), month(date)) - sum(rating)) /
(sum(count(*)) over (partition by year(date), month(date)) - count(*))
) as avg_other
from t
group by year(date), month(date), product;
The rating for the "other" is a bit tricky. You need to add everything up and subtract out the current row -- and calculate the average by doing the sum divided by the count.
thank you for attention.
I have a table called "PROD_COST" with 5 fields
(ID,Duration,Cost,COST_NEXT,COST_CHANGE).
I need extra field called "groups" for aggregation.
Duration = number of days the price is valid (1 day=1row).
Cost = product price in this day.
Cost_next = lead(cost,1,0).
Cost_change = Cost_next - Cost.
Now i need to group by Cost_change. It can be
positive,negative or 0 values.
+----+---+------+------+------+
| 1 | 1 | 10 | 8,5 | -1,5 |
| 2 | 1 | 8,5 | 12,2 | 3,7 |
| 3 | 1 | 12,2 | 5,3 | -6,9 |
| 4 | 1 | 5,3 | 4,2 | 1,2 |
| 5 | 1 | 4,2 | 6,2 | 2 |
| 6 | 1 | 6,2 | 9,2 | 3 |
| 7 | 1 | 9,2 | 7,5 | -2,7 |
| 8 | 1 | 7,5 | 6,2 | -1,3 |
| 9 | 1 | 6,2 | 6,3 | 0,1 |
| 10 | 1 | 6,3 | 7,2 | 0,9 |
| 11 | 1 | 7,2 | 7,5 | 0,3 |
| 12 | 1 | 7,5 | 0 | 7,5 |
+----+---+------+------+------+`
I need to make a query, which will group it by first negative or positive value (+ - + - + -). Last one field is what i want.
Sorry for my English `
+----+---+------+------+------+---+
| 1 | 1 | 10 | 8,5 | -1,5 | 1 |
| 2 | 1 | 8,5 | 12,2 | 3,7 | 2 |
| 3 | 1 | 12,2 | 5,3 | -6,9 | 3 |
| 4 | 1 | 5,3 | 4,2 | 1,2 | 4 |
| 5 | 1 | 4,2 | 6,2 | 2 | 4 |
| 6 | 1 | 6,2 | 9,2 | 3 | 4 |
| 7 | 1 | 9,2 | 7,5 | -2,7 | 5 |
| 8 | 1 | 7,5 | 6,2 | -1,3 | 5 |
| 9 | 1 | 6,2 | 6,3 | 0,1 | 6 |
| 10 | 1 | 6,3 | 7,2 | 0,9 | 6 |
| 11 | 1 | 7,2 | 7,5 | 0,3 | 6 |
| 12 | 1 | 7,5 | 0 | 7,5 | 6 |
+----+---+------+------+------+---+`
If you're in SQL Server 2012 you can use the window functions to do this:
select
id, COST_CHANGE, sum(GRP) over (order by id asc) +1
from
(
select
*,
case when sign(COST_CHANGE) != sign(isnull(lag(COST_CHANGE)
over (order by id asc),COST_CHANGE)) then 1 else 0 end as GRP
from
PROD_COST
) X
Lag will get the value from previous row, check the sign of it and compare it to the current row. If the values don't match, the case will return 1. The outer select will do a running total of these numbers, and every time there is 1, it will increase the total.
It is possible to use the same logic with older versions too, you'll just have to fetch the previous row from the table using the id and do running total by re-calculating all rows before the current one.
Example in SQL Fiddle
James's answer is close but it doesn't handle the zero value correctly. This is a pretty easy modification. One tricky approximation uses differences between product changes:
select id, COST_CHANGE, sum(IsNewGroup) over (order by id asc) +1
from (select pc.*,
(case when sign(cost_change) - sign(lag(cost_change) over (order by id)) between -1 and 1
then 0
else 1 -- `NULL` intentionally goes here
end) IsNewGroup
from Prod_Cost
) pc
For clarity, here is a SQL Fiddle with zero values. From my understanding of the question, the OP only wants an actual sign change.
This may still not be correct. The OP simply is not clear about what to do about 0 values.
i have table like this:
| ID | id_number | a | b |
| 1 | 1 | 0 | 215 |
| 2 | 2 | 28 | 8952 |
| 3 | 3 | 10 | 2000 |
| 4 | 1 | 0 | 215 |
| 5 | 1 | 0 |10000 |
| 6 | 3 | 10 | 5000 |
| 7 | 2 | 3 |90933 |
I want to sum a*b where id_number is same, what the query to get all value for every id_number? for example the result is like this :
| ID | id_number | result |
| 1 | 1 | 0 |
| 2 | 2 | 523455 |
| 3 | 3 | 70000 |
This is a simple aggregation query:
select id_number, sum(a*b)
from t
group by id_number
I'm not sure what the first column is for.