I have written pretty straightforward queries so far, so I am now looking a help to write a SQL statement so that it will combine two separate period end rows from a table into one row. The rows are basically can be matched by their PId, Region, Market, Code, Source. For example-
if 1st row is:
Id Region Market CODE Source Period_End Amt Pct
100 CAN CABLE V1 SA 20120930 100.00 0.2
and 2nd row is:
Id Region Market CODE Source Period_End Amt Pct
100 CAN CABLE V1 SA 20121231 200.00 0.5
Then the SQL should return this result:
Id Region Market CODE Source Period_End_1 Amt_1 Pct_1 Period_End_2 Amt_2 Pct_2
100 CAN CABLE V1 SA 20120930 100.00 0.2 20121231 200.00 0.5
Your help is really appreciated.
Ana.
Thanks for your responses. This is what I started with but I am not sure if I am on right direction or not. I also noticed as I would add more and more information to the row based on Period End then the below query would be too long with redundant "case condition" in each select.
select
A.id , A.region, A.market, A.code, A.source ,
case when period_end = #day_id1 then period_end else '' end as Period_End_1,
case when period_end = #day_id2 then period_end else '' end as Period_End_2,
case when period_end = #day_id1 then Amt else 0.0 end as Amt_1,
case when period_end = #day_id2 then Amt else 0.0 end as Amt_2,
case when period_end = #day_id1 then Pct else 0.0 end as Pct_1,
case when period_end = #day_id2 then pct else 0.0 end as Pct_2,
from
products A with (nolock)
where
A.product_id in (select product_id from #products) -- temp table holding multiple Ids
If I'm understanding your question correctly, you're trying to pivot multiple rows into multiple columns.
Assuming it's always 2 rows you're trying to combine, using the period_end field to order the first from the second, then something like this should work using max with case to pivot your results:
WITH CTE AS (
SELECT *,
Row_Number() Over (Partition By Id, Region, Market, Code, Source
Order By Period_End) rn
FROM YourTable
)
SELECT Id,
Region,
Market,
Code,
Source,
max(case when rn = 1 then Period_End end) Period_End_1,
max(case when rn = 1 then Amt end) Amt_1,
max(case when rn = 1 then Pct end) Pct_1,
max(case when rn = 2 then Period_End end) Period_End_2,
max(case when rn = 2 then Amt end) Amt_2,
max(case when rn = 2 then Pct end) Pct_2
FROM CTE
GROUP BY Id, Region, Market, Code, Source
If you have more potential period_end dates, then you might need to use dynamic sql to achieve your results.
SELECT t1.Id
,t1.Region
,t1.Market
,t1.CODE
,t1.Source
,t1.Period_End AS Period_End_1
,t1.Amt AS Amt_1
,t1.Pct AS Pct_1
,t2.Period_End AS Period_End_2
,t2.Amt AS Amt_2
,t2.Pct AS Pct_2
FROM Table_Name t1
INNER JOIN TABLE_Name t2 ON t1.ID = t2.ID
WHERE t1.ID = 100 AND t1.Period_End <> t2.Period_End
Related
I would like a cross table from the following table.
The cross table should look like this
A pivot table does not seem to solve the problem, because only one column can be used at a time. But in our case we are dealing with 4 different columns. (payment, month, year and free of charge)
I solved the problem by splitting these 4 columns into four different pivot tables, using temporary tables and finally reassembling the obtained data. But this is very complicated, long and confusing, in short not very nice...
The years and months should be shown in ascending form, exactly as you can see in the cross table above.
I have been looking for a solution for quite a while but I can't find the same problem anywhere.
If someone would give me a short, elegant solution I would be very grateful.
Under http://www.sqlfiddle.com/#!18/7216f/2 you can see the problem definition.
Thank you!
You can rank records by date in a subquery with row_number(), and then pivot with conditional aggregation:
select
ClientId,
max(case when rn = 1 then Payment end) Payment1,
max(case when rn = 2 then Payment end) Payment2,
max(case when rn = 3 then Payment end) Payment3,
max(case when rn = 1 then [Month] end) Month1,
max(case when rn = 2 then [Month] end) Month2,
max(case when rn = 3 then [Month] end) Month3,
max(case when rn = 1 then [Year] end) Year1,
max(case when rn = 2 then [Year] end) Year2,
max(case when rn = 3 then [Year] end) Year3,
max(case when rn = 1 then FreeOfCharge end) FreeOfCharge1,
max(case when rn = 2 then FreeOfCharge end) FreeOfCharge2,
max(case when rn = 3 then FreeOfCharge end) FreeOfCharge3
from (
select
t.*,
row_number() over(partition by ClientId order by [Year], [Month]) rn
from mytable t
) t
group by ClientId
You can join the table with itself a few times, as in:
with p as (
select
*, row_number() over(partition by clientid order by year, month) as n
from Payment
)
select
p1.clientid,
p1.payment, p2.payment, p3.payment,
p1.month, p2.month, p3.month,
p1.year, p2.year, p3.year,
p1.freeofcharge, p2.freeofcharge, p3.freeofcharge
from p p1
left join p p2 on p2.clientid = p1.clientid and p2.n = 2
left join p p3 on p3.clientid = p1.clientid and p3.n = 3
where p1.n = 1
See Fiddle.
I'm trying to figure out if what I'm trying to do is possible. Instead of resorting to multiple queries on a table, I wanted to group the records by business date and id then group by the id and select one date for a field and another date for the other field.
SELECT
*
{AMOUNT FROM DATE}
{AMOUNT FROM OTHER DATE}
FROM (
SELECT
date,
id,
SUM(amount) AS amount
FROM
table
GROUP BY id, date
AS subquery
GROUP BY id
It seems that you're looking to do a pivot query. I usually use cross tabs for this. Based on the query you posted, it could look like:
SELECT
id,
SUM(CASE WHEN date = '20190901' THEN amount ELSE 0 END) AmountFromSept01,
SUM(CASE WHEN date = '20191001' THEN amount ELSE 0 END) AmountFromOct01
FROM (
SELECT
date,
id,
SUM(amount) AS amount
FROM
table
GROUP BY id, date
)AS subquery
GROUP BY id;
You could also use a CTE.
WITH CTE AS(
SELECT
date,
id,
SUM(amount) AS amount
FROM
table
GROUP BY id, date
)
SELECT
id,
SUM(CASE WHEN date = '20190901' THEN amount ELSE 0 END) AmountFromSept01,
SUM(CASE WHEN date = '20191001' THEN amount ELSE 0 END) AmountFromOct01
FROM CTE
GROUP BY id;
Or even be a rebel and do the operation directly.
SELECT
id,
SUM(CASE WHEN date = '20190901' THEN amount ELSE 0 END) AmountFromSept01,
SUM(CASE WHEN date = '20191001' THEN amount ELSE 0 END) AmountFromOct01
FROM CTE
GROUP BY id;
However, some people have tested for performance and found that pre-aggregating can improve performance.
If I understand you correctly, then you're just trying to pivot, but only with two particular dates:
select id,
date1 = sum(iif(date = '2000-01-01', amount, null)),
date2 = sum(iif(date = '2000-01-02', amount, null))
from [table]
group by id
I need to calculate the percentage difference between 2 days for a specific group in my table.
I need percentage change of column [Group] with the field 'FUND' between days 20190731 and 20190628 by using the column Value. In my example should be -75%.
I hope this works for you:
select prd, grp, 100.0 * (sum(end_value) - sum(start_value)) / sum(start_value)
from
(
select prd, grp,
(case when date = "20190731" then val else 0 end) as end_value,
(case when date = "20190628" then val else 0 end) as start_value
from table1
) as t
group by prd, grp
I am working on Hive and am facing an issue with rolling counts. The sample data I am working on is as shown below:
and the output I am expecting is as shown below:
I tried using the following query but it is not returning the rolling count:
select event_dt,status, count(distinct account) from
(select *, row_number() over (partition by account order by event_dt
desc)
as rnum from table.A
where event_dt between '2018-05-02' and '2018-05-04') x where rnum =1
group by event_dt, status;
Please help me with this if some one has solved a similar issue.
You seem to just want conditional aggregation:
select event_dt,
sum(case when status = 'Registered' then 1 else 0 end) as registered,
sum(case when status = 'active_acct' then 1 else 0 end) as active_acct,
sum(case when status = 'suspended' then 1 else 0 end) as suspended,
sum(case when status = 'reactive' then 1 else 0 end) as reactive
from table.A
group by event_dt
order by event_dt;
EDIT:
This is a tricky problem. The solution I've come up with does a cross-product of dates and users and then calculates the most recent status as of each date.
So:
select a.event_dt,
sum(case when aa.status = 'Registered' then 1 else 0 end) as registered,
sum(case when aa.status = 'active_acct' then 1 else 0 end) as active_acct,
sum(case when aa.status = 'suspended' then 1 else 0 end) as suspended,
sum(case when aa.status = 'reactive' then 1 else 0 end) as reactive
from (select d.event_dt, ac.account, a.status,
max(case when a.status is not null then a.timestamp end) over (partition by ac.account order by d.event_dt) as last_status_timestamp
from (select distinct event_dt from table.A) d cross join
(select distinct account from table.A) ac left join
(select a.*,
row_number() over (partition by account, event_dt order by timestamp desc) as seqnum
from table.A a
) a
on a.event_dt = d.event_dt and
a.account = ac.account and
a.seqnum = 1 -- get the last one on the date
) a left join
table.A aa
on aa.timestamp = a.last_status_timestamp and
aa.account = a.account
group by d.event_dt
order by d.event_dt;
What this is doing is creating a derived table with rows for all accounts and dates. This has the status on certain days, but not all days.
The cumulative max for last_status_timestamp calculates the most recent timestamp that has a valid status. This is then joined back to the table to get the status on that date. Voila! This is the status used for the conditional aggregation.
The cumulative max and join is a work-around because Hive does not (yet?) support the ignore nulls option in lag().
I am trying to count events (which are rows in the event_table) in the year before and the year after a particular target date for each person. For example, say I have a person 100 and target date is 10/01/2012. I would like to count events in 9/30/2011-9/30/2012 and in 10/02/2012-9/30/2013.
My query looks like:
select *
from (
select id, target_date
from subsample_table
) as i
left join (
select id, event_date, count(*) as N
, case when event_date between target_date-365 and target_date-1 then 0
when event_date between target_date+1 and target_date+365 then 1
else 2 end as after
from event_table
group by id, target_date, period
) as h
on i.id = h.id
and i.target_date = h.event_date
The output should look something like:
id target_date after N
100 10/01/2012 0 1000
100 10/01/2012 1 0
It's possible that some people do not have any events in the before or after periods (or both), and it would be nice to have zeros in that case. I don't care about the events outside the 730 days.
Any suggestions would be greatly appreciated.
I think the following may approach what you are trying to accomplish.
select id
, target_date
, event_date
, count(*) as N
, SUM(case when event_date between target_date-365 and target_date-1
then 1
else 0
end) AS Prior_
, SUM(case when event_date between target_date+1 and target_date+365
then 1
else 0
end) as After_
from subsample_table i
left join
event_table h
on i.id = h.id
and i.target_date = h.event_date
group by id, target_date, period
This is a generic answer. I don't know what date functions teradata has, so I will use sql server syntax.
select id, target_date, sum(before) before, sum(after) after, sum(righton) righton
from yourtable t
join (
select id, target_date td
, case when yourdate >= dateadd(year, -1, target_date)
and yourdate < target_date then 1 else 0 end before
, case when yourdate <= dateadd(year, 1, target_date)
and yourdate > target_date then 1 else 0 end after
, case when yourdate = target_date then 1 else 0 end righton
from yourtable
where whatever
group by id, target_date) sq on t.id = sq.id and target_date = dt
where whatever
group by id, target_date
This answer assumes that an id can have more than one target date.