I want to do something very simple but I'm obviously missing a trick! I want to get an average of average values but I want to include the weighting of the original average calculation. I'll use a stripped back version of what I'm attempting to do.
So let's say I have the following table
Product date RunInterval AvgDuration_secs Executions
--------------------------------------------------------------------
A 29/12/19 1 1 100
A 29/12/19 2 2 10
What I want to find out is what the average duration was for Product A on 29/12. All the things I've tried so far are giving me an average of 1.5 secs ie it's adding together the duration of 1 & 2 secs (3) and dividing by number of rows (2) to give 1.5. What I want to get to is to have the average but taking into account how often it runs so (100*1) + (10*2) / 110 = 1.09 secs. I've tried various attempts with GROUP BY statements and CURSORS but not getting there.
I'm evidently tackling it the wrong way! Any help welcome :-)
You can do it like this:
select product, date,
round(1.0 * sum([Executions] * [AvgDuration_secs]) / sum([Executions]), 2) result
from tablename
group by product, date
I'm not sure if you want RunInterval or AvgDuration_secs in the 1st sum.
See the demo.
Results:
> product | date | result
> :------ | :---------| :-----
> A | 29/12/2019| 1.09
If you got those results from a query or view that select from some table, then grouped by Product & date & RunInterval.
Then you could simply run a query that on that table that
groups only by the Product & date.
An example:
--
-- Sample data
--
CREATE TABLE sometable
(
Product varchar(30),
ExecutionDatetime datetime,
RunInterval int
);
WITH RCTE_NUMS AS
(
SELECT 1 AS n
UNION ALL
SELECT n+1
FROM RCTE_NUMS
WHERE n < 110
)
INSERT INTO sometable
(Product, ExecutionDatetime, RunInterval)
SELECT
'A' p,
DATEADD(minute,n*12,'2019-12-29 01:00:00') dt,
IIF(n<=100,1,2) ri
FROM RCTE_NUMS
OPTION (MAXRECURSION 1000);
110 rows affected
select
Product,
cast(ExecutionDatetime as date) as [Date],
AVG(1.0*RunInterval) AS AvgDuration_secs,
COUNT(*) AS Executions
from sometable t
group by
Product,
cast(ExecutionDatetime as date)
ORDER BY Product, [Date]
Product | Date | AvgDuration_secs | Executions
:------ | :------------------ | :--------------- | ---------:
A | 29/12/2019 00:00:00 | 1.090909 | 110
db<>fiddle here
Related
My database is Db2 for IBM i.
I have read-only access, so my query must use only basic SQL select commands.
==============================================================
Goal:
I want to select every record in the table until the sum of the amount column exceeds the predetermined limit.
Example:
I want to match every item down the table until the sum of matched values in the "price" column >= $9.00.
The desired result:
Is this possible?
You may use sum analytic function to calculate running total of price and then filter by its value:
with a as (
select
t.*,
sum(price) over(order by salesid asc) as price_rsum
from t
)
select *
from a
where price_rsum <= 9
SALESID | PRICE | PRICE_RSUM
------: | ----: | ---------:
1001 | 5 | 5
1002 | 3 | 8
1003 | 1 | 9
db<>fiddle here
We are trying to make a query where we get the sum of unique customers on a specific year-month + the sum of unique customers on the 364 days before the specific date.
For example:
Our customer-table looks like this:
| order_date | customer_unique_id |
| -------- | -------------- |
| 2020-01-01 | tom#email.com |
| 2020-01-01 | daisy#email.com |
| 2019-05-02 | tom#email.com |
In this example we have two customers who ordered on 2020-01-01 and one of them already ordered within the 364-days timeframe.
The desired table should look like this:
| year_month | unique_customers |
| -------- | -------------- |
| 2020-01 | 2 |
We tried multiple solutions, such as partitioning and windows, but nothing seem to work correctly. The tricky part is the uniqueness. We want the look 364 days back but want to do a count distinct on the customers based on that whole period and not based on date/year/month because then we would get duplicates. For example, if you partition by date, year or month tom#email.com would be counted twice instead of once.
The goal of this query is to get insight into the order-frequency (orders divided by customers) over a time period from 12 months.
We work with Google BigQuery.
Hope someone can help us out! :)
Here is a way to achieve your desired results. Note that this query does year-month join in a separate query, and joins it with the rolling 364-day-interval query.
with year_month_distincts as (
select
concat(
cast(extract(year from order_date) as string),
'-',
cast(extract(month from order_date) as string)
) as year_month,
count(distinct customer_id) as ym_distincts
from customer_table
group by 1
)
select x.order_date, x.ytd_distincts, y.ym_distincts from (
select
a. order_date,
(select
count(distinct customer_id)
from customer_table b
where b.order_date between date_sub(a.order_date, interval 364 day) and a.order_date
) as ytd_distincts
from orders a
group by 1
) x
join year_month_distincts y on concat(
cast(extract(year from x.order_date) as string),
'-',
cast(extract(month from x.order_date) as string)
) = y.year_month
Two options using arrays that may help.
Look back 364 days as requested
In case you wish to look back 11 months (given reporting is monthly)
month_array AS (
SELECT
DATE_TRUNC(order_date,month) AS order_month,
STRING_AGG(DISTINCT customer_unique_id) AS cust_mth
FROM customer_table
GROUP BY 1
),
year_array AS (
SELECT
order_month,
STRING_AGG(cust_mth) OVER(ORDER by UNIX_DATE(order_month) RANGE BETWEEN 364 PRECEDING AND CURRENT ROW) cust_12m
-- (option 2) STRING_AGG(cust_mth) OVER (ORDER by cast(format_date('%Y%m', order_month) as int64) RANGE BETWEEN 99 PRECEDING AND CURRENT ROW) AS cust_12m
FROM month_array
)
SELECT format_date('%Y-%m',order_month) year_month,
(SELECT COUNT(DISTINCT cust_unique_id) FROM UNNEST(SPLIT(cust_12m)) AS cust_unique_id) as unique_12m
FROM year_array
I have a table that has the following schema:
ID | FirstName | Surname | TransmissionID | CaptureDateTime
1 | Billy | Goat | ABCDEF | 2018-09-20 13:45:01.098
2 | Jonny | Cash | ABCDEF | 2018-09-20 13:45.01.108
3 | Sally | Sue | ABCDEF | 2018-09-20 13:45:01.298
4 | Jermaine | Cole | PQRSTU | 2018-09-20 13:45:01.398
5 | Mike | Smith | PQRSTU | 2018-09-20 13:45:01.498
There are well over 70,000 records and they store logs of transmissions to a web-service. What I'd like to know is how would I go about writing a script that would select the distinct TransmissionID values and also show the timespan between the earliest CaptureDateTime record and the latest record? Essentially I'd like to see what the rate of records the web-service is reading & writing.
Is it even possible to do so in a single SELECT statement or should I just create a stored procedure or report in code? I don't know where to start aside from SELECT DISTINCT TransmissionID for this sort of query.
Here's what I have so far (I'm stuck on the time calculation)
SELECT DISTINCT [TransmissionID],
COUNT(*) as 'Number of records'
FROM [log_table]
GROUP BY [TransmissionID]
HAVING COUNT(*) > 1
Not sure how to get the difference between the first and last record with the same TransmissionID I would like to get a result set like:
TransmissionID | TimeToCompletion | Number of records |
ABCDEF | 2.001 | 5000 |
Simply GROUP BY and use MIN / MAX function to find min/max date in each group and subtract them:
SELECT
TransmissionID,
COUNT(*),
DATEDIFF(second, MIN(CaptureDateTime), MAX(CaptureDateTime))
FROM yourdata
GROUP BY TransmissionID
HAVING COUNT(*) > 1
Use min and max to calculate timespan
SELECT [TransmissionID],
COUNT(*) as 'Number of records',datediff(s,min(CaptureDateTime),max(CaptureDateTime)) as timespan
FROM [log_table]
GROUP BY [TransmissionID]
HAVING COUNT(*) > 1
A method that returns the average time for all transmissionids, even those with only 1 record:
SELECT TransmissionID,
COUNT(*),
DATEDIFF(second, MIN(CaptureDateTime), MAX(CaptureDateTime)) * 1.0 / NULLIF(COUNT(*) - 1, 0)
FROM yourdata
GROUP BY TransmissionID;
Note that you may not actually want the maximum of the capture date for a given transmissionId. You might want the overall maximum in the table -- so you can consider the final period after the most recent record.
If so, this looks like:
SELECT TransmissionID,
COUNT(*),
DATEDIFF(second,
MIN(CaptureDateTime),
MAX(MAX(CaptureDateTime)) OVER ()
) * 1.0 / COUNT(*)
FROM yourdata
GROUP BY TransmissionID;
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
;
Input:
Date Price
12/27 5
12/21 5
12/20 4
12/19 4
12/15 5
Required Output:
The earliest date when the price was set in comparison to the current price.
For e.g., price has been 5 since 12/21.
The answer cannot be 12/15 as we are interested in finding the earliest date where the price was the same as the current price without changing in value(on 12/20, the price has been changed to 4)
This should be about right. You didn't provide table structures or names, so...
DECLARE #CurrentPrice MONEY
SELECT TOP 1 #CurrentPrice=Price FROM Table ORDER BY Date DESC
SELECT MIN(Date) FROM Table WHERE Price=#CurrentPrice AND Date>(
SELECT MAX(Date) FROM Table WHERE Price<>#CurrentPrice
)
In one query:
SELECT MIN(Date)
FROM Table
WHERE Date >
( SELECT MAX(Date)
FROM Table
WHERE Price <>
( SELECT TOP 1 Price
FROM Table
ORDER BY Date DESC
)
)
This question kind of makes no sense so im not 100% sure what you are after.
create four columns, old_price, new_price, old_date, new_date.
! if old_price === new_price, simply print the old_date.
What database server are you using? If it was Oracle, I would use their windowing function. Anyway, here is a quick version that works in mysql:
Here is the sample data:
+------------+------------+---------------+
| date | product_id | price_on_date |
+------------+------------+---------------+
| 2011-01-01 | 1 | 5 |
| 2011-01-03 | 1 | 4 |
| 2011-01-05 | 1 | 6 |
+------------+------------+---------------+
Here is the query (it only works if you have 1 product - will have to add a "and product_id = ..." condition on the where clause if otherwise).
SELECT p.date as last_price_change_date
FROM test.prices p
left join test.prices p2 on p.product_id = p2.product_id and p.date < p2.date
where p.price_on_date - p2.price_on_date <> 0
order by p.date desc
limit 1
In this case, it will return "2011-01-03".
Not a perfect solution, but I believe it works. Have not tested on a larger dataset, though.
Make sure to create indexes on date and product_id, as it will otherwise bring your database server to its knees and beg for mercy.
Bernardo.