I'm very confused by the results I'm getting between 2 queries that I thought should be identical.
select count(distinct client_id)
from database1 a
inner join database2 b on a.client_id=b.client_id and b.year = 2021
and
select clienttype, count(distinct client_id)
from database1 a
inner join database2 b on a.client_id=b.client_id and b.year = 2021
group by 1
First one gives me 200,000 while when I sum all the clienttypes from the second one it gives me 300,000. And in the result from the second query, NULL clienttypes are counted (roughly 50,000 so it doesn't explain the difference anyways).
Any idea why those two are not the same? All I'm doing is breaking it down by clienttype, did I miss something?
Thanks
You clearly have examples where client_id is in multiple types.
You can find these using:
select client_id, count(distinct clienttype)
from database1
group by client_id
having count(distinct clienttype) > 1;
Related
So table A is an overall table of policy_id information, while table b is policy_id's with claims attached. Not all of the id's in A exist in B, but I want to join the two tables and sum(total claims).
The issue is that the sum is way higher than the actual sum within the table itself.
Here is what I've tried so far:
select a.policy_id, coalesce(sum(b.claim_amt), 0)
from database.table1 as a
left join database2.table2 as b on a.policy_id = b.policy_id
where product_code = 'CI'
group by a.policy_id
The id's that don't exist in b show up just fine with a 0 next to them, it's the ones that do exist where the claim_amt's seem like they're being duplicated heavily in the sum.
I suspect your policy_id in table1 are not unique and that leads to the doubled,tripled ,etc. amounts
You could aggregate the sums from table2 in a CTE to get around this.
WITH CTE AS (
SELECT
policy_id
coalesce(sum(claim_amt), 0) as sum_amt
FROM database2.table2
group by policy_id
)
select a.policy_id, b.sum_amt
from database.table1 as a
left join CTE as b on a.policy_id = b.policy_id
where product_code = 'CI'
My data looks like this:
Company Year Total Comment
Comp A 01-01-2000 5,000 Checked
Comp A 01-01-2001 6,000 Checked
Comp B 05-05-2007 3,000 Not checked completely
Comp B 05-05-2008 4,000 Checked
Comp C 18-01-2003 1,500 Not checked completely
Comp C 18-01-2002 3,500 Not checked completely
I've been asked to transpose certain data, but I do not believe this can be done using SQL (Server) so that it looks like this:
Company Base Date Base Date-1 Comment Base Date Comment Base Date-1
Comp A 01-01-2001 01-01-2000 Checked Checked
Comp B 05-05-2008 05-05-2007 Checked Not completely checked
Comp C 18-01-2003 18-01-2002 Not completely checked Not completely checked
I have never built anything like this. If I would then maybe Excel is a better alternative? How should I tackle this?
Is it possible using SELECT MAX(Base Date) and MIN(Base Date)? And how would I then tackle the strings like that..
You can use a self join to do this. However, you should think about dates like February 29 as they only occur in leap years.
select t1.company,t1.year as basedate,t2.year as basedate_1,
t1.comment as comment_basedate,t2.comment as comment_basedate_1
from t t1
left join t t2 on t1.company=t2.company dateadd(year,1,t2.year)=t1.year
Change the left join to an inner join if you only need results where both the date values exist for a company. This solution assumes there can only be one comment per day.
I'd assign a row number to each record partitioned by company ordered by year desc though an analytical function in a common table expression... then use a left self join... on the row number + 1 and company.
This assumes you only want 1 record per company using the 2 most recent years. and if only 1 record exists for a company null values are acceptable for the second year. If not we can change the left join to an inner and eliminate both records...
We use a common table expression (though a inline view would work as well) to assign a row number to each record. That value is then made available in our self join so we don't have to worry about different dates and max values. We then use our RowNumber (RN) and company to join the 2 desired records together. To save on some performance we limit 1 table to RN 1 and the second table to RN 2.
WITH CTE AS (
SELECT *, Row_Number() over (Partition by Company Order by Year Desc) RN FROM TABLE)
SELECT A.Company
, A.Year as Base_Date
, B.Year as Base_Date1
, A.comment as Base_Date_Comment
, B.Comment as Base_Date1_Comment
FROM CTE A
LEFT JOIN CTE B
on A.RN+1 = B.RN
and A.Company = B.Company
and B.RN = 2
WHERE A.RN = 1
Note the limit on RN=2 must be on the join since it's an outer join or we would eliminate the companies without 2 years. (in essence making the left join an inner)
This approach makes all columns of the data available for each row.
If there are only two rows each, then that's pretty simple. If there are more than two rows, you could do something like this -- essentially joining all rows, then making sure A represents the earliest row and B represents the latest row.
SELECT A.Company, A.Year AS [Base Date], B.Year AS [Base Date 1],
A.Comment AS [Comment Base Date], B.Comment AS [Comment Base Date 1]
FROM MyTable A
INNER JOIN MyTable B ON A.Company = B.Company
WHERE A.Year = (SELECT MIN(C.YEAR) FROM MyTable C WHERE C.Company = A.Company)
AND B.Year = (SELECT MAX(C.YEAR) FROM MyTable C WHERE C.Company = B.Company)
There might be a more efficient way to do this with Row_Number or something.
I have tables A, B, C. Table A is linked to B, and table A is linked to C. I want to join the 3 tables and find the sum of B.cost and the sum of C.clicks. However, it is not giving me the expected value, and when I select everything without the group by, it is showing duplicate rows. I am expecting the row values from B to roll up into a single sum, and the row values from C to roll up into a single sum.
My query looks like
select A.*, sum(B.cost), sum(C.clicks) from A
join B
left join C
group by A.id
having sum(cost) > 10
I tried to group by B.a_id and C.another_field_in_a also, but that didn't work.
Here is a DB fiddle with all of the data and the full query:
http://sqlfiddle.com/#!9/768745/13
Notice how the sum fields are greater than the sum of the individual tables? I'm expecting the sums to be equal, containing only the rows of the table B and C once. I also tried adding distinct but that didn't help.
I'm using Postgres. (The fiddle is set to MySQL though.) Ultimately I will want to use a having clause to select the rows according to their sums. This query will be for millions of rows.
If I understand the logic correctly, the problem is the Cartesian product caused by the two joins. Your query is a bit hard to follow, but I think the intent is better handled with correlated subqueries:
select k.*,
(select sum(cost)
from ad_group_keyword_network n
where n.event_date >= '2015-12-27' and
n.ad_group_keyword_id = 1210802 and
k.id = n.ad_group_keyword_id
) as cost,
(select sum(clicks)
from keyword_click c
where (c.date is null or c.date >= '2015-12-27') and
k.keyword_id = c.keyword_id
) as clicks
from ad_group_keyword k
where k.status = 2 ;
Here is the corresponding SQL Fiddle.
EDIT:
The subselect should be faster than the group by on the unaggregated data. However, you need the right indexes: ad_group_keyword_network(ad_group_keyword_id, ad_group_keyword_id, event_date, cost) and keyword_click(keyword_id, date, clicks).
I found this (MySQL joining tables group by sum issue) and created a query like this
select *
from A
join (select B.a_id, sum(B.cost) as cost
from B
group by B.a_id) B on A.id = B.a_id
left join (select C.keyword_id, sum(C.clicks) as clicks
from C
group by C.keyword_id) C on A.keyword_id = C.keyword_id
group by A.id
having sum(cost) > 10
I don't know if it's efficient though. I don't know if it's more or less efficient than Gordon's. I ran both queries and this one seemed faster, 27s vs. 2m35s. Here is a fiddle: http://sqlfiddle.com/#!15/c61c74/10
Simply split the aggregate of the second table into a subquery as follows:
http://sqlfiddle.com/#!9/768745/27
select ad_group_keyword.*, SumCost, sum(keyword_click.clicks)
from ad_group_keyword
left join keyword_click on ad_group_keyword.keyword_id = keyword_click.keyword_id
left join (select ad_group_keyword.id, sum(cost) SumCost
from ad_group_keyword join ad_group_keyword_network on ad_group_keyword.id = ad_group_keyword_network.ad_group_keyword_id
where event_date >= '2015-12-27'
group by ad_group_keyword.id
having sum(cost) > 20
) Cost on Cost.id=ad_group_keyword.id
where
(keyword_click.date is null or keyword_click.date >= '2015-12-27')
and status = 2
group by ad_group_keyword.id
I have this query to express a set of business rules.
To get the information I need, I tried joining the table on itself but that brings back many more records than are actually in the table. Below is the query I've tried. What am I doing wrong?
SELECT DISTINCT a.rep_id, a.rep_name, count(*) AS 'Single Practitioner'
FROM [SE_Violation_Detection] a inner join [SE_Violation_Detection] b
ON a.rep_id = b.rep_id and a.hcp_cid = b.hcp_cid
group by a.rep_id, a.rep_name
having count(*) >= 2
You can accomplish this with the having clause:
select a, b, count(*) c
from etc
group by a, b
having count(*) >= some number
I figured out a simpler way to get the information I need for one of the queries. The one above is still wrong.
--Rep violation for different HCP more than 5 times
select distinct rep_id,rep_name,count(distinct hcp_cid)
AS 'Multiple Practitioners'
from dbo.SE_Violation_Detection
group by rep_id,rep_name
having count(distinct hcp_cid)>4
order by count(distinct hcp_cid)
I have two tables
Table A
type_uid, allowed_type_uid
9,1
9,2
9,4
1,1
1,2
24,1
25,3
Table B
type_uid
1
2
From table A I need to return
9
1
Using a WHERE IN clause I can return
9
1
24
SELECT
TableA.type_uid
FROM
TableA
INNER JOIN
TableB
ON TableA.allowed_type_uid = TableB.type_uid
GROUP BY
TableA.type_uid
HAVING
COUNT(distinct TableB.type_uid) = (SELECT COUNT(distinct type_uid) FROM TableB)
Join the two tables togeter, so that you only have the records matching the types you are interested in.
Group the result set by TableA.type_uid.
Check that each group has the same number of allowed_type_uid values as exist in TableB.type_uid.
distinct is required only if there can be duplicate records in either table. If both tables are know to only have unique values, the distinct can be removed.
It should also be noted that as TableA grows in size, this type of query will quickly degrade in performance. This is because indexes are not actually much help here.
It can still be a useful structure, but not one where I'd recommend running the queries in real-time. Rather use it to create another persisted/cached result set, and use this only to refresh those results as/when needed.
Or a slightly cheaper version (resource wise):
SELECT
Data.type_uid
FROM
A AS Data
CROSS JOIN
B
LEFT JOIN
A
ON Data.type_uid = A.type_uid AND B.type_uid = A.allowed_type_uid
GROUP BY
Data.type_uid
HAVING
MIN(ISNULL(A.allowed_type_uid,-999)) != -999
Your explanation is not very clear. I think you want to get those type_uid's from table A where for all records in table B there is a matching A.Allowed_type_uid.
SELECT T2.type_uid
FROM (SELECT COUNT(*) as AllAllowedTypes FROM #B) as T1,
(SELECT #A.type_uid, COUNT(*) as AllowedTypes
FROM #A
INNER JOIN #B ON
#A.allowed_type_uid = #B.type_uid
GROUP BY #A.type_uid
) as T2
WHERE T1.AllAllowedTypes = T2.AllowedTypes
(Dems, you were faster than me :) )