I am using TSQL, SSMS v.17.9.1 The underlying db is Microsoft SQL Server 2014 SP3
For display purposes, I want to concatenate the results of two queries:
SELECT TOP 1 colA as 'myCol1' FROM tableA
--
SELECT TOP 1 colB as 'myCol2' FROM tableB
and display the results from the queries in one row in SSMS.
(The TOP 1 directive would hopefully guarantee the same number of results from each query, which would assist displaying them together. If this could be generalized to TOP 10 per query that would help also)
This should work for any number of rows, it assumes you want to pair ordered by the values in the column displayed
With
TableA_CTE AS
(
SELECT TOP 1 colA as myCol1
,Row_Number() OVER (ORDER BY ColA DESC) AS RowOrder
FROM tableA
),
TableB_CTE AS
(
SELECT TOP 1 colB as myCol2
,Row_Number() OVER (ORDER BY ColB DESC) AS RowOrder
FROM tableB
)
SELECT A.myCol1, B.MyCol2
FROM TableA_CTE AS A
INNER JOIN TableB_CTE AS B
ON A.RowOrder = B.RowOrder
There are currently two issues with the accepted answer:
I) a missing comma before the line: "Table B As"
II) TSQL seems to find it recursive as written, so I re-wrote it in a non-recursive way:
This is a re-working of the accepted answer that actually works in T-SQL:
USE [Database_1];
With
CTE_A AS
(
SELECT TOP 1 [Col1] as myCol1
,Row_Number() OVER (ORDER BY [Col2] desc) AS RowOrder
FROM [TableA]
)
,
CTE_B AS
(
SELECT TOP 1 [Col2] as myCol2
,Row_Number() OVER (ORDER BY [Col2] desc) AS RowOrder
FROM [TableB]
)
SELECT A.myCol1, B.myCol2
FROM CTE_A AS A
INNER JOIN CTE_B AS B
ON ( A.RowOrder = B.RowOrder)
Related
I am writing a query in Redshift to answer the question "Give the average lifetime spend of users who spent more on their first order than their second order." This is based off of an order_items table which has one row for every item ordered (so an order with 3 items would be represented in 3 rows). Here's a snapshot of the first 10 rows:
First 10 rows of order_items:
Here is my solution:
with
cte1_lifetime as (
select oi.user_id, sum(oi.sale_price) as lifetime_spend
from order_items as oi
group by oi.user_id
),
cte2_order as (
select oi.user_id, oi.order_id, sum(oi.sale_price) as order_total, rank() over(partition by oi.user_id order by oi.created_at) as order_rank
from order_items as oi
group by oi.user_id, oi.order_id, oi.created_at
order by oi.user_id, oi.order_id
),
cte3_first_order as (
select user_id, order_id, order_total
from cte2_order
where order_rank=1
order by user_id, order_id
),
cte4_second_order as (
select user_id, order_id, order_total
from cte2_order
where order_rank=2
order by user_id, order_id
)
select avg(cte1.lifetime_spend) as average_lifetime_spend
from cte1_lifetime as cte1
where exists (
select *
from cte3_first_order as cte3, cte4_second_order as cte4
where cte3.user_id=cte4.user_id
and cte1.user_id=cte3.user_id
and cte3.order_total > cte4.order_total)
And here is the answer key:
WITH
table1 AS
(SELECT user_id, order_id,
SUM(sale_price) OVER (PARTITION BY order_id ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) as order_total,
RANK() OVER (PARTITION BY user_id ORDER BY created_at) AS "sequence"
FROM order_items)
,
table2 AS
(SELECT user_id, SUM(sale_price) AS lifetime_spend
FROM order_items
WHERE EXISTS
(SELECT t1.user_id
FROM table1 t1, table1 t2
WHERE t1.user_id = t2.user_id AND t1.sequence = 1 AND t2.sequence = 2 AND t1.order_total>t2.order_total
AND t1.user_id = order_items.user_id)
GROUP BY 1
ORDER BY 1)
SELECT AVG(lifetime_spend)
FROM table2
These answers yield slightly different results on the same data- an average lifetime spend of $215 vs $220. I'd really like to understand why they are different but so far I can't figure it out. Any ideas?
I have a table called user_activity in Redshift that has department, user_id, activity_type, activity_id, activity_date.
I'd like to query a daily report of how many days since the last event (of any type). Using CROSS APPLY (SQL Server) or LATERAL JOIN (Postgres 9+), I'd do something like...
SELECT d.date, a.last_activity_date
FROM date_table d
CROSS JOIN (
SELECT DISTINCT user_id FROM activity_table
) u
CROSS APPLY (
SELECT TOP 1 activity_date as last_activity_date
FROM activity_table
WHERE user_id = u.user_id AND activity_date <= d.date
ORDER BY activity_date DESC
) a
For now, I write it similar to the below, but it is a bit slow and I am afraid it'll only get slower.
with user_activity as (
select distinct activity_date, user_id from activity_table
)
select
d.date, u.user_id,
max(u.activity_date) as last_activity_date
from date_table d
inner join user_activity u on u.activity_date <= d.date
where d.date between '2020-01-01' and current_date
group by 1, 2
Can someone suggest a good alternative for my needs or for CROSS APPLY / LATERAL JOIN.
As you are seeing cross joining and inequality joining will slow down as you data grows and are generally not the approach you want in Redshift. This is due to the data size increase that comes with this type of action when applied to large data tables that are typical in Redshift.
You want to use window functions to perform this type of analysis. But you will need to step back and rethink how you will structure the SQL. A MAX(activity_date) window function, partitioned by user_id and ordered by date and with a frame clause of all preceding rows, will find the most recent activity to any activity.
Now this will produce only rows for user_ids and dates that exist in the data table and it looks like you want 1 row for each date for each user_id, right? To do this you need to UNION in a frame of data that has 1 row for each date for each user_id ahead of the window function. You will need NULLs in for the other columns so that the data widths match. You will also want the dates in a separate column from activity_date. Now all dates for all user ids will be in the source and the window function will give you the result you want.
You also ask ‘how is this better than the joins?’ Well in the joins you are replicating all the data records by the number of dates which can get really big. In this approach you just have the original data records plus one row per user_id per date (which is the size of your output) and as the number of records per user_id grows this approach doesn’t.
——— Request to modify asker’s code per comments made to their approach ———
Your code is definitely on the right track as you have removed the massive inequality join of your original. I made 2 comments about it. The first is that I believe you need GROUP BY user_id, date to prevent multiple rows per user_id per date that would result if there are records for the same user_id on a single date with differing activity_types. This is a simple oversight.
The second is to state that I intended for you to use UNION ALL, not LEFT JOIN, in combining the actual data and the user_id/date framework. Your approach works fine but I have found that unioning with very large amounts of data is generally faster than joining but you do need to make sure the columns match up. Either way we end up with a data segment with 3 columns - 2 date columns, one with NULLs for framework rows, and 1 user_id. Your approach is fine and the difference in performance is likely very small unless you have huge tables.
Since you asked for a rewrite, here it is with both changes. (NOTE: my laptop is in the shop so I don’t have ready access to Redshift at the moment and this SQL is untested. If the intent is not clear from this and you need me to debug it will be delayed by a few days. I’m keeping your setup methods and SQL structure.)
with date_table as (
select '2000-01-01'::date as date
union all
select '2000-01-02'::date
union all
select '2000-01-03'::date
union all
select '2000-01-04'::date
union all
select '2000-01-05'::date
union all
select '2000-01-06'::date
),
users as (
select 1 as user_id
union all
select 2
union all
select 3
),
user_activity as (
select 1 as user_id, '2000-01-01'::date as activity_date
union all
select 1 as user_id, '2000-01-04'::date as activity_date
union all
select 3 as user_id, '2000-01-03'::date as activity_date
union all
select 1 as user_id, '2000-01-05'::date as activity_date
union all
select 1 as user_id, '2000-01-06'::date as activity_date
),
user_dates as (
select d.date, u.user_id
from date_table d
cross join users u
),
user_date_activity as (
select cal_date, user_id,
lag(max(activity_date), 1) ignore nulls over (partition by user_id order by date) as last_activity_date
from (
Select user_id, date as cal_date, NULL as activity_date from user_dates
Union all
Select user_id, activity_date as cal_date, activity_date from user_activity
)
Group by user_id, cal_date
)
select * from user_date_activity
order by user_id, cal_date```
This was my query based on Bill's answer.
with date_table as (
select '2000-01-01'::date as date
union all
select '2000-01-02'::date
union all
select '2000-01-03'::date
union all
select '2000-01-04'::date
union all
select '2000-01-05'::date
union all
select '2000-01-06'::date
),
users as (
select 1 as user_id
union all
select 2
union all
select 3
),
user_activity as (
select 1 as user_id, '2000-01-01'::date as activity_date
union all
select 1 as user_id, '2000-01-04'::date as activity_date
union all
select 3 as user_id, '2000-01-03'::date as activity_date
union all
select 1 as user_id, '2000-01-05'::date as activity_date
union all
select 1 as user_id, '2000-01-06'::date as activity_date
),
user_dates as (
select d.date, u.user_id
from date_table d
cross join users u
),
user_date_activity as (
select ud.date, ud.user_id,
lag(ua.activity_date, 1) ignore nulls over (partition by ud.user_id order by ud.date) as last_activity_date
from user_dates ud
left join user_activity ua on ud.date = ua.activity_date and ud.user_id = ua.user_id
)
select * from user_date_activity
order by user_id, date
Have a table with 3 columns: ID, Signature, and Datetime, and it's grouped by Signature Having Count(*) > 9.
select * from (
select s.Signature
from #Sigs s
group by s.Signature
having count(*) > 9
) b
join #Sigs o
on o.Signature = b.Signature
order by o.Signature desc, o.DateTime
I now want to select the 1st and 10th records only, per Signature. What determines rank is the Datetime descending. Thus, I would expect every Signature to have 2 rows.
Thanks,
I would go with a couple of common table expressions.
The first will select all records from the table as well as a count of records per signature, and the second one will select from the first where the record count > 9 and add row_number partitioned by signature - and then just select from that where the row_number is either 1 or 10:
With cte1 AS
(
SELECT ID, Signature, Datetime, COUNT(*) OVER(PARTITION BY Signature) As NumberOfRows
FROM #Sigs
), cte2 AS
(
SELECT ID, Signature, Datetime, ROW_NUMBER() OVER(PARTITION BY Signature ORDER BY DateTime DESC) As Rn
FROM cte1
WHERE NumberOfRows > 9
)
SELECT ID, Signature, Datetime
FROM cte2
WHERE Rn IN (1, 10)
ORDER BY Signature desc
Because I don't know what your data looks like, this might need some adjustment.
The simplest way here, since you already know your sort order (DateTime DESC) and partitioning (Signature), is probably to assign row numbers and then select the rows you want.
SELECT *
FROM
(
select o.Signature
,o.DateTime
,ROW_NUMBER() OVER (PARTITION BY o.Signature ORDER BY o.DateTime DESC) [Row]
from (
select s.Signature
from #Sigs s
group by s.Signature
having count(*) > 9
) b
join #Sigs o
on o.Signature = b.Signature
order by o.Signature desc, o.DateTime
)
WHERE [Row] IN (1,10)
I want to create calculated table that will summarize In_Force Premium from existing table fact_Premium.
How can I filter the result by saying:
TODAY() has to be between `fact_Premium[EffectiveDate]` and (SELECT TOP 1 fact_Premium[ExpirationDate] ORDE BY QuoteID DESC)
In SQL I'd do that like this:
`WHERE CONVERT(date, getdate()) between CONVERT(date, tblQuotes.EffectiveDate)
and (
select top 1 q2.ExpirationDate
from Table2 Q2
where q2.ControlNo = Table1.controlno
order by quoteid` desc
)
Here is my DAX statement so far:
In_Force Premium =
FILTER(
ADDCOLUMNS(
SUMMARIZE(
//Grouping necessary columns
fact_Premium,
fact_Premium[QuoteID],
fact_Premium[Division],
fact_Premium[Office],
dim_Company[CompanyGUID],
fact_Premium[LineGUID],
fact_Premium[ProducerGUID],
fact_Premium[StateID],
fact_Premium[ExpirationDate]
),
"Premium", CALCULATE(
SUM(fact_Premium[Premium])
),
"ControlNo", CALCULATE(
DISTINCTCOUNT(fact_Premium[ControlNo])
)
), // Here I need to make sure TODAY() falls between fact_Premium[EffectiveDate] and (SELECT TOP 1 fact_Premium[ExpirationDate] ORDE BY QuoteID DESC)
)
Also, what would be more efficient way, to create calculated table from fact_Premium or create same table using sql statement (--> Get Data--> SQL Server) ?
There are 2 potential ways in T-SQL to get the next effective date. One is to use LEAD() and another is to use an APPLY operator. As there are few facts to work with here are samples:
select *
from (
select *
, lead(EffectiveDate) over(partition by CompanyGUID order by quoteid desc) as NextEffectiveDate
from Table1
join Table2 on ...
) d
or
select table1.*, oa.NextEffectiveDate
from Table1
outer apply (
select top(1) q2.ExpirationDate AS NextEffectiveDate
from Table2 Q2
where q2.ControlNo = Table1.controlno
order by quoteid desc
) oa
nb. an outer apply is a little similar to a left join in that it will allow rows with a NULL to be returned by the query, if that is not needed than use cross apply instead.
In both these approaches you may refer to NextEffectiveDate in a final where clause, but I would prefer to avoid using the convert function if that is feasible (this depends on the data).
tblUserProfile - I have a table which holds all the Profile Info (too many fields)
tblMonthlyProfiles - Another table which has just the ProfileID in it (the idea is that this table holds 2 profileids which sometimes become monthly profiles (on selection))
Now when I need to show monthly profiles, I simply do a select from this tblMonthlyProfiles and Join with tblUserProfile to get all valid info.
If there are no rows in tblMonthlyProfile, then monthly profile section is not displayed.
Now the requirement is to ALWAYS show Monthly Profiles. If there are no rows in monthlyProfiles, it should pick up 2 random profiles from tblUserProfile. If there is only one row in monthlyProfiles, it should pick up only one random row from tblUserProfile.
What is the best way to do all this in one single query ?
I thought something like this
select top 2 * from tblUserProfile P
LEFT OUTER JOIN tblMonthlyProfiles M
on M.profileid = P.profileid
ORder by NEWID()
But this always gives me 2 random rows from tblProfile. How can I solve this ?
Try something like this:
SELECT TOP 2 Field1, Field2, Field3, FinalOrder FROM
(
select top 2 Field1, Field2, Field3, FinalOrder, '1' As FinalOrder from tblUserProfile P JOIN tblMonthlyProfiles M on M.profileid = P.profileid
UNION
select top 2 Field1, Field2, Field3, FinalOrder, '2' AS FinalOrder from tblUserProfile P LEFT OUTER JOIN tblMonthlyProfiles M on M.profileid = P.profileid ORDER BY NEWID()
)
ORDER BY FinalOrder
The idea being to pick two monthly profiles (if that many exist) and then 2 random profiles (as you correctly did) and then UNION them. You'll have between 2 and 4 records at that point. Grab the top two. FinalOrder column is an easy way to make sure that you try and get the monthly's first.
If you have control of the table structure, you might save yourself some trouble by simply adding a boolean field IsMonthlyProfile to the UserProfile table. Then it's a single table query, order by IsBoolean, NewID()
In SQL 2000+ compliant syntax you could do something like:
Select ...
From (
Select TOP 2 ...
From tblUserProfile As UP
Where Not Exists( Select 1 From tblMonthlyProfile As MP1 )
Order By NewId()
) As RandomProfile
Union All
Select MP....
From tblUserProfile As UP
Join tblMonthlyProfile As MP
On MP.ProfileId = UP.ProfileId
Where ( Select Count(*) From tblMonthlyProfile As MP1 ) >= 1
Union All
Select ...
From (
Select TOP 1 ...
From tblUserProfile As UP
Where ( Select Count(*) From tblMonthlyProfile As MP1 ) = 1
Order By NewId()
) As RandomProfile
Using SQL 2005+ CTE you can do:
With
TwoRandomProfiles As
(
Select TOP 2 ..., ROW_NUMBER() OVER ( ORDER BY UP.ProfileID ) As Num
From tblUserProfile As UP
Order By NewId()
)
Select MP.Col1, ...
From tblUserProfile As UP
Join tblMonthlyProfile As MP
On MP.ProfileId = UP.ProfileId
Where ( Select Count(*) From tblMonthlyProfile As MP1 ) >= 1
Union All
Select ...
From TwoRandomProfiles
Where Not Exists( Select 1 From tblMonthlyProfile As MP1 )
Union All
Select ...
From TwoRandomProfiles
Where ( Select Count(*) From tblMonthlyProfile As MP1 ) = 1
And Num = 1
The CTE has the advantage of only querying for the random profiles once and the use of the ROW_NUMBER() column.
Obviously, in all the UNION statements the number and type of the columns must match.