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
;
Related
This is a similar scenario to
SQL: Count of rows since certain value first occurred
In SQL Server, I'm trying to calculate the count of days since the same weather as today (let's assume today is 6th August 2018) was observed first in the past 5 days. Per town.
Here's the data:
+---------+---------+--------+--------+--------+
| Date | Toronto | Cairo | Zagreb | Ankara |
+---------+---------+--------+--------+--------+
| 1.08.18 | Rain | Sun | Clouds | Sun |
| 2.08.18 | Sun | Sun | Clouds | Sun |
| 3.08.18 | Rain | Sun | Clouds | Rain |
| 4.08.18 | Clouds | Sun | Clouds | Clouds |
| 5.08.18 | Rain | Clouds | Rain | Rain |
| 6.08.18 | Rain | Sun | Sun | Sun |
+---------+---------+--------+--------+--------+
This needs to perform well but all I came up with so far is single queries for each town (and there are going to be dozens of towns, not just the four). This works but is not going to scale.
Here's the one for Toronto...
SELECT
DATEDIFF(DAY, MIN([Date]), GETDATE()) + 1
FROM
(SELECT TOP 5 *
FROM Weather
WHERE [Date] <= GETDATE()
ORDER BY [Date] DESC) a
WHERE
Toronto = (SELECT TOP 1 Toronto
FROM Weather
WHERE DataDate = GETDATE())
...which correctly returns 4 since today there is rain and the first occurrence of rain within the past 5 days was 3rd August.
But what I want returned is a table like this:
+---------+-------+--------+--------+
| Toronto | Cairo | Zagreb | Ankara |
+---------+-------+--------+--------+
| 4 | 5 | 1 | 5 |
+---------+-------+--------+--------+
Slightly modified from the accepted answer by #Used_By_Already is this code:
CREATE TABLE mytable(
Date date NOT NULL
,Toronto VARCHAR(9) NOT NULL
,Cairo VARCHAR(9) NOT NULL
,Zagreb VARCHAR(9) NOT NULL
,Ankara VARCHAR(9) NOT NULL
);
INSERT INTO mytable(Date,Toronto,Cairo,Zagreb,Ankara) VALUES ('20180801','Rain','Sun','Clouds','Sun');
INSERT INTO mytable(Date,Toronto,Cairo,Zagreb,Ankara) VALUES ('20180802','Sun','Sun','Clouds','Sun');
INSERT INTO mytable(Date,Toronto,Cairo,Zagreb,Ankara) VALUES ('20180803','Rain','Sun','Clouds','Rain');
INSERT INTO mytable(Date,Toronto,Cairo,Zagreb,Ankara) VALUES ('20180804','Clouds','Sun','Clouds','Clouds');
INSERT INTO mytable(Date,Toronto,Cairo,Zagreb,Ankara) VALUES ('20180805','Rain','Clouds','Rain','Rain');
INSERT INTO mytable(Date,Toronto,Cairo,Zagreb,Ankara) VALUES ('20180806','Rain','Sun','Sun','Sun');
with cte as (
select
date, city, weather
FROM (
SELECT * from mytable
) AS cp
UNPIVOT (
Weather FOR City IN (Toronto, Cairo, Zagreb, Ankara)
) AS up
)
select
date, city, weather, datediff(day,ca.prior,cte.date)+1 as daysPresent
from cte
cross apply (
select min(prev.date) as prior
from cte as prev
where prev.city = cte.city
and prev.date between dateadd(day,-4,cte.date) and dateadd(day,0,cte.date)
and prev.weather = cte.weather
) ca
order by city,date
Output:
However, what I'm trying now is to keep counting "daysPresent" up even after those five past days in question. Meaning that the last marked row in the output sample should show 6. The logic being to increase the previous number by the count of days between them if there is less than 5 days of a gap between them. If there has not been the same weather in the past 5 days, go back to 1.
I experimented with LEAD and LAG but cannot get it to work. Is it even the right way to add another layer to it or would the query need to look different entirely?
I'm a but puzzled.
You have a major problem with your data structure. The values should be in rows, not columns. So, start with:
select d.dte, v.*from data d cross apply
(values ('Toronto', Toronto), ('Cairo', Cairo), . . .
) v(city, val)
where d.date >= dateadd(day, -5, getdate());
From there, we can use the window function first_value() (or last_value()) to get the most recent reading. The rest is just aggregation by city:
with d as (
select d.dte, v.*,
first_value(v.val) over (partition by v.city order by d.dte desc) as last_val
from data d cross apply
(values ('Toronto', Toronto), ('Cairo', Cairo), . . .
) v(city, val)
where d.date >= dateadd(day, -5, getdate())
)
select city, datediff(day, min(dte), getdate()) + 1
from d
where val = last_val
group by city;
This gives you the information you want, in rows rather than columns. You can re-pivot if you really want. But I advise you to keep the data with city data in different rows.
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
I'm trying to select first & last date in window based on month & year of date supplied.
Here is example data:
F.rates
| id | c_id | date | rate |
---------------------------------
| 1 | 1 | 01-01-1991 | 1 |
| 1 | 1 | 15-01-1991 | 0.5 |
| 1 | 1 | 30-01-1991 | 2 |
.................................
| 1 | 1 | 01-11-2014 | 1 |
| 1 | 1 | 15-11-2014 | 0.5 |
| 1 | 1 | 30-11-2014 | 2 |
Here is pgSQL SELECT I came up with:
SELECT c_id, first_value(date) OVER w, last_value(date) OVER w FROM F.rates
WINDOW w AS (PARTITION BY EXTRACT(YEAR FROM date), EXTRACT(MONTH FROM date), c_id
ORDER BY date ASC)
Which gives me a result pretty close to what I want:
| c_id | first_date | last_date |
----------------------------------
| 1 | 01-01-1991 | 15-01-1991 |
| 1 | 01-01-1991 | 30-01-1991 |
.................................
Should be:
| c_id | first_date | last_date |
----------------------------------
| 1 | 01-01-1991 | 30-01-1991 |
.................................
For some reasons last_value(date) returns every record in a window. Which giving me a thought that I'm misunderstanding how windows in SQL works. It's like SQL forming a new window for each row it iterates through, but not multiple windows for entire table based on YEAR and MONTH.
So could any one be kind and explain if I'm wrong and how do I achieve the result I want?
There is a reason why i'm not using MAX/MIN over GROUP BY clause. My next step would be to retrieve associated rates for dates I selected, like:
| c_id | first_date | last_date | first_rate | last_rate | avg rate |
-----------------------------------------------------------------------
| 1 | 01-01-1991 | 30-01-1991 | 1 | 2 | 1.1 |
.......................................................................
If you want your output to become grouped into a single (or just fewer) row(s), you should use simple aggregation (i.e. GROUP BY), if avg_rate is enough:
SELECT c_id, min(date), max(date), avg(rate)
FROM F.rates
GROUP BY c_id, date_trunc('month', date)
More about window functions in PostgreSQL's documentation:
But unlike regular aggregate functions, use of a window function does not cause rows to become grouped into a single output row — the rows retain their separate identities.
...
There is another important concept associated with window functions: for each row, there is a set of rows within its partition called its window frame. Many (but not all) window functions act only on the rows of the window frame, rather than of the whole partition. By default, if ORDER BY is supplied then the frame consists of all rows from the start of the partition up through the current row, plus any following rows that are equal to the current row according to the ORDER BY clause. When ORDER BY is omitted the default frame consists of all rows in the partition.
...
There are options to define the window frame in other ways ... See Section 4.2.8 for details.
EDIT:
If you want to collapse (min/max aggregation) your data and want to collect more columns than those what listed in GROUP BY, you have 2 choice:
The SQL way
Select min/max value(s) in a sub-query, then join their original rows back (but this way, you have to deal with the fact, that min/max-ed column(s) usually not unique):
SELECT c_id,
min first_date,
max last_date,
first.rate first_rate,
last.rate last_rate,
avg avg_rate
FROM (SELECT c_id, min(date), max(date), avg(rate)
FROM F.rates
GROUP BY c_id, date_trunc('month', date)) agg
JOIN F.rates first ON agg.c_id = first.c_id AND agg.min = first.date
JOIN F.rates last ON agg.c_id = last.c_id AND agg.max = last.date
PostgreSQL's DISTINCT ON
DISTINCT ON is typically meant for this task, but highly rely on ordering (only 1 extremum can be searched for this way at a time):
SELECT DISTINCT ON (c_id, date_trunc('month', date))
c_id,
date first_date,
rate first_rate
FROM F.rates
ORDER BY c_id, date
You can join this query with other aggregated sub-queries of F.rates, but this point (if you really need both minimum & maximum, and in your case even an average) the SQL compliant way is more suiting.
Windowing functions aren't appropriate for this. Use aggregate functions instead.
select
c_id, date_trunc('month', date)::date,
min(date) first_date, max(date) last_date
from rates
group by c_id, date_trunc('month', date)::date;
c_id | date_trunc | first_date | last_date
------+------------+------------+------------
1 | 2014-11-01 | 2014-11-01 | 2014-11-30
1 | 1991-01-01 | 1991-01-01 | 1991-01-30
create table rates (
id integer not null,
c_id integer not null,
date date not null,
rate numeric(2, 1),
primary key (id, c_id, date)
);
insert into rates values
(1, 1, '1991-01-01', 1),
(1, 1, '1991-01-15', 0.5),
(1, 1, '1991-01-30', 2),
(1, 1, '2014-11-01', 1),
(1, 1, '2014-11-15', 0.5),
(1, 1, '2014-11-30', 2);
I have a table with 2 columns. UTCTime and Values.
The UTCTime is in 15 mins increment. I want a query that would compare the value to the previous value in one hour span and display a value between 0 and 4 depends on if the values are constant. In other words there is an entry for every 15 minute increment and the value can be constant so I just need to check each value to the previous one per hour.
For example
+---------|-------+
| UTCTime | Value |
------------------|
| 12:00 | 18.2 |
| 12:15 | 87.3 |
| 12:30 | 55.91 |
| 12:45 | 55.91 |
| 1:00 | 37.3 |
| 1:15 | 47.3 |
| 1:30 | 47.3 |
| 1:45 | 47.3 |
| 2:00 | 37.3 |
+---------|-------+
In this case, I just want a Query that would compare the 12:45 value to the 12:30 and 12:30 to 12:15 and so on. Since we are comparing in only one hour span then the constant values must be between 0 and 4 (O there is no constant values, 1 there is one like in the example above)
The query should display:
+----------+----------------+
| UTCTime | ConstantValues |
----------------------------|
| 12:00 | 1 |
| 1:00 | 2 |
+----------|----------------+
I just wanted to mention that I am new to SQL programming.
Thank you.
See SQL fiddle here
Below is the query you need and a working solution Note: I changed the timeframe to 24 hrs
;with SourceData(HourTime, Value, RowNum)
as
(
select
datepart(hh, UTCTime) HourTime,
Value,
row_number() over (partition by datepart(hh, UTCTime) order by UTCTime) RowNum
from foo
union
select
datepart(hh, UTCTime) - 1 HourTime,
Value,
5
from foo
where datepart(mi, UTCTime) = 0
)
select cast(A.HourTime as varchar) + ':00' UTCTime, sum(case when A.Value = B.Value then 1 else 0 end) ConstantValues
from SourceData A
inner join SourceData B on A.HourTime = B.HourTime and
(B.RowNum = (A.RowNum - 1))
group by cast(A.HourTime as varchar) + ':00'
select SUBSTRING_INDEX(UTCTime,':',1) as time,value, count(*)-1 as total
from foo group by value,time having total >= 1;
fiddle
Mine isn't much different from Vasanth's, same idea different approach.
The idea is that you need recursion to carry it out simply. You could also use the LEAD() function to look at rows ahead of your current row, but in this case that would require a big case statement to cover every outcome.
;WITH T
AS (
SELECT a.UTCTime,b.VALUE,ROW_NUMBER() OVER(PARTITION BY a.UTCTime ORDER BY b.UTCTime DESC)'RowRank'
FROM (SELECT *
FROM #Table1
WHERE DATEPART(MINUTE,UTCTime) = 0
)a
JOIN #Table1 b
ON b.UTCTIME BETWEEN a.UTCTIME AND DATEADD(hour,1,a.UTCTIME)
)
SELECT T.UTCTime, SUM(CASE WHEN T.Value = T2.Value THEN 1 ELSE 0 END)
FROM T
JOIN T T2
ON T.UTCTime = T2.UTCTime
AND T.RowRank = T2.RowRank -1
GROUP BY T.UTCTime
If you run the portion inside the ;WITH T AS ( ) you'll see that gets us the hour we're looking at and the values in order by time. That is used in the recursive portion below by joining to itself and evaluating each row compared to the next row (hence the RowRank - 1) on the JOIN.
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.