Suppose I have a statistical table like this:
date | stats
-------------
10/1 | 2
10/1 | 3
10/1 | 2
10/2 | 1
10/3 | 3
10/3 | 2
10/4 | 1
10/4 | 1
What I want is three columns:
Date
sum(stats) of Date
sum(stats) of last three days before Date
I know I can use window function to handle the 2nd column, but I cannot handle 2nd and 3rd at the same time.
What should I do to archive this?
Thanks!
You can use aggregation and window functions:
select date, sum(stats) as day_stats,
sum(sum(stats)) over (order by date rows between 3 preceding and 1 preceding) as day_stats_3
from t
group by date
order by date;
You can use a correlated query:
SELECT s.date,sum(s.stats) as today_sum,
(SELECT sum(t.stats) FROM YourTable t
where t.date between s.date - 2 and s.date) as sum_3days
FROM YourTable s
GROUP BY s.date
Related
I have a table with as follows with user visits by day -
| date | user_id |
|:-------- |:-------- |
| 01/31/23 | a |
| 01/31/23 | a |
| 01/31/23 | b |
| 01/30/23 | c |
| 01/30/23 | a |
| 01/29/23 | c |
| 01/28/23 | d |
| 01/28/23 | e |
| 01/01/23 | a |
| 12/31/22 | c |
I am looking to get a running total of unique user_id for the last 30 days . Here is the expected output -
| date | distinct_users|
|:-------- |:-------- |
| 01/31/23 | 5 |
| 01/30/23 | 4 |
.
.
.
Here is the query I tried -
SELECT date
, SUM(COUNT(DISTINCT user_id)) over (order by date rows between 30 preceding and current row) AS unique_users
FROM mytable
GROUP BY date
ORDER BY date DESC
The problem I am running into is that this query not counting the unique user_id - for instance the result I am getting for 01/31/23 is 9 instead of 5 as it is counting user_id 'a' every time it occurs.
Thank you, appreciate your help!
Not the most performant approach, but you could use a correlated subquery to find the distinct count of users over a window of the past 30 days:
SELECT
date,
(SELECT COUNT(DISTINCT t2.user_id)
FROM mytable t2
WHERE t2.date BETWEEN t1.date - INTERVAL '30 day' AND t1.date) AS distinct_users
FROM mytable t1
ORDER BY date;
There are a few things going on here. First window functions run after group by and aggregation. So COUNT(DISTINCT user_id) gives the count of user_ids for each date then the window function runs. Also, window function set up like this work over the past 30 rows, not 30 days so you will need to fill in missing dates to use them.
As to how to do this - I can only think of the "expand to the data so each date and id has a row" method. This will require a CTE to generate the last 2 years of dates plus 30 days so that the look-back window works for the first dates. Then window over the past 30 days for each user_id and date to see which rows have an example of this user_id within the past 30 days, setting the value to NULL if no uses of the user_id are present within the window. Then Count the user_ids counts (non NULL) grouping by just date to get the number of unique user_ids for that date.
This means expanding the data significantly but I see no other way to get truly unique user_ids over the past 30 days. I can help code this up if you need but will look something like:
WITH RECURSIVE CTE to generate the needed dates,
CTE to cross join these dates with a distinct set of all the user_ids in user for the past 2 years,
CTE to join the date/user_id data set with the table of real data for past 2 years and 30 days and window back counting non-NULL user_ids, partition by date and user_id, order by date, and setting any zero counts to NULL with a DECODE() or CASE statement,
SELECT, grouping by just date count the user_ids by date;
I don't think I properly titled this, but in essence I'm wanting to be able to count distinct users but have those previous distinct users be considered as time goes on. As an example, say we have a dataset of user purchases over time:
Date | User
-----------------
2/3/22 | A
2/4/22 | B
2/22/22 | C
3/2/22 | A
3/4/22 | D
3/15/22 | A
4/30/22 | B
Generally, if I were to count distincts grouped by months as would be normal we would get:
Date | Count
-----------------
2/1/22 | 3
3/1/22 | 2
4/1/22 | 1
But what I'm really wanting to see would be how the total number of distinct users increases over the time period.
Date | Count
-----------------
2/1/22 | 3
3/1/22 | 4
4/1/22 | 4
As such it would be 3 distinct users for the first month. Then 4 for the second month considering the total number of distinct users grew by one with the addition of "D" while "A" isn't counted because it was already recognized as a distinct user in the previous month. The third month would then still be 4 because no new distinct user performed an action that month.
Any help would be greatly appreciated (even if it is just a better title so that it reaches more people more appropriately haha)
here's a solution based on running sum in Postgres that should translate well to Vertica.
select date_trunc('month', "Date") as "Date"
,sum(count(case rn when 1 then 1 end)) over (order by date_trunc('month', "Date")) as "Count"
from (
select "Date"
,"User"
,row_number() over(partition by "User" order by "Date") as rn
from t
) t
group by date_trunc('month', "Date")
order by "Date"
Date
Count
2022-02-01 00:00:00
3
2022-03-01 00:00:00
4
2022-04-01 00:00:00
4
Fiddle
I want to find the minimum value of a column in a certain date range of a table.
so lets say I have a table like the following,
Date | Value
---------------
01-26 | 2
01-26 | 1
01-27 | 2
01-27 | 4
01-28 | 3
01-28 | 5
How can I apply the MIN() function to the subgroup of the Value column so that the result might be
Date | MIN(Value)
---------------
01-26 | 1
01-27 | 2
01-28 | 3
I thought about GROUP BY .. or such but couldn't figure out how to get the results into a table.
Using UNION and JOIN isn't quite scalable because the query could be using a date range of a month
Group by should work:
Select date, min( value )
From table1
Group by date
Maybe too simple, but seems like this would work
Select Min(col1), datecol from yourtable group by datecol;
HTH
I have a problem with grouping my dataset in MS SQL Server.
My table looks like
# | CustomerID | SalesDate | Turnover
---| ---------- | ------------------- | ---------
1 | 1 | 2016-08-09 12:15:00 | 22.50
2 | 1 | 2016-08-09 12:17:00 | 10.00
3 | 1 | 2016-08-09 12:58:00 | 12.00
4 | 1 | 2016-08-09 13:01:00 | 55.00
5 | 1 | 2016-08-09 23:59:00 | 10.00
6 | 1 | 2016-08-10 00:02:00 | 5.00
Now I want to group the rows where the SalesDate difference to the next row is of a maximum of 5 minutes.
So that row 1 & 2, 3 & 4 and 5 & 6 are each one group.
My approach was getting the minutes with the DATEPART() function and divide the result by 5:
(DATEPART(MINUTE, SalesDate) / 5)
For row 1 and 2 the result would be 3 and grouping here would work perfectly.
But for the other rows where there is a change in the hour or even in the day part of the SalesDate, the result cannot be used for grouping.
So this is where I'm stuck. I would really appreciate, if someone could point me in the right direction.
You want to group adjacent transactions based on the timing between them. The idea is to assign some sort of grouping identifier, and then use that for aggregation.
Here is an approach:
Identify group starts using lag() and date arithmetic.
Do a cumulative sum of the group starts to identify each group.
Aggregate
The query looks like this:
select customerid, min(salesdate), max(saledate), sum(turnover)
from (select t.*,
sum(case when salesdate > dateadd(minute, 5, prev_salesdate)
then 1 else 0
end) over (partition by customerid order by salesdate) as grp
from (select t.*,
lag(salesdate) over (partition by customerid order by salesdate) as prev_salesdate
from t
) t
) t
group by customerid, grp;
EDIT
Thanks to #JoeFarrell for pointing out I have answered the wrong question. The OP is looking for dynamic time differences between rows, but this approach creates fixed boundaries.
Original Answer
You could create a time table. This is a table that contains one record for each second of the day. Your table would have a second column that you can use to perform group bys on.
CREATE TABLE [Time]
(
TimeId TIME(0) PRIMARY KEY,
TimeGroup TIME
)
;
-- You could use a loop here instead.
INSERT INTO [Time]
(
TimeId,
TimeGroup
)
VALUES
('00:00:00', '00:00:00'), -- First group starts here.
('00:00:01', '00:00:00'),
('00:00:02', '00:00:00'),
('00:00:03', '00:00:00'),
...
('00:04:59', '00:00:00'),
('00:05:00', '00:05:00'), -- Second group starts here.
('00:05:01', '00:05:00')
;
The approach works best when:
You need to reuse your custom grouping in several different queries.
You have two or more custom groups you often use.
Once populated you can simply join to the table and output the desired result.
/* Using the time table.
*/
SELECT
t.TimeGroup,
SUM(Turnover) AS SumOfTurnover
FROM
Sales AS s
INNER JOIN [Time] AS t ON t.TimeId = CAST(s.SalesDate AS Time(0))
GROUP BY
t.TimeGroup
;
I would like to get the average value of each day. For example, I would like to get 4 instead of getting 3 and 5 on "20130511". How can I do that on a single query? (Not hard code)
Select Val from Table 1 where??????????
Table1 :
Date Val
----------------
20130511 | 3
20130511 | 5
20130512 | 5
20130512 | 1
20130512 | 2
20130512 | 6
20130512 | 2
20130513 | 2
You have to group the date
SELECT Date, AVG(Val) FROM TABLE GROUP BY Date
You can group by date and do it in a single pass
SELECT DATE, SUM(VALUE)/COUNT(*) AS AVERAGE
FROM TABLE
GROUP BY DATE