I have a query that is taking 48 seconds to execute as follows:
SELECT count(DISTINCT tmd_logins.userID) as totalLoginsUniqueLast30Days
FROM tmd_logins
join tmd_users on tmd_logins.userID = tmd_users.userID
where tmd_users.isPatient = 1 AND loggedIn > '2011-03-25'
and tmd_logins.userID in
(SELECT userID as accounts30Days FROM tmd_users
where isPatient = 1 AND created > '2012-04-29' AND computerID is null)
When I remove the DISTINCT keyword it takes less than 1 second, so it seems the bottle neck lies within that.
The database adds an entry to the tmd_logins table every time a user logs into the system. I am trying to get a total count of all users that are patients that have been created and logged in within a given period of time, such as in the last 30 days.
I have tried removing the DISTINCT keyword and adding group by tmd_logins.userID to the statement but the performance issue remains.
Table tmd_logins has about 300,000 records, tmd_users has about 40,000
Is there a better way of doing this?
The problem that you have is the execution plan. My guess is that the "in" clause might be confusing it. You might try:
SELECT count(DISTINCT tmd_logins.userID) as totalLoginsUniqueLast30Days
FROM tmd_logins join
tmd_users
on tmd_logins.userID = tmd_users.userID join
(SELECT distinct userID as accounts30Days
FROM tmd_users
where isPatient = 1 AND
created > '2012-04-29' AND
computerID is null
) t
on tmd_logins.userID = t.accounts30Days
where tmd_users.isPatient = 1 AND
loggedIn > '2011-03-25'
That might or might not work. However, I'm wondering about the structure of the query itself. It would seem that UserID should be distinct in a table called tmd_users. If so, then you can wrap all your conditions into one:
SELECT count(DISTINCT tmd_logins.userID) as totalLoginsUniqueLast30Days
FROM tmd_logins join
tmd_users
on tmd_logins.userID = tmd_users.userID
where tmd_users.isPatient = 1 AND
loggedIn > '2011-03-25' and
created > '2012-04-29' AND
computerID is null
If my guess is true, then this should definitely run faster.
Related
I am using a NOT EXSITS clause in my query and wanted to make sure it was working correctly since I was getting lesser rows than expected.
SELECT DISTINCT offer.courier_uuid,
offer.region_uuid,
offer.offer_time_local,
Cast(scores.acceptance_rate AS DECIMAL(5, 3)) AS acceptance_rate
FROM integrated_delivery.trip_offer_fact offer
JOIN integrated_product.driver_score_v2 scores ON offer.courier_uuid = scores.courier_id
AND offer.region_uuid = scores.region_id
AND offer.business_day BETWEEN date '2019-04-04' AND date '2019-04-07'
AND scores.extract_dt = 20190331
AND NOT EXISTS
(SELECT NULL
FROM source_cassandra_courier_scheduling.assigned_block_by_id_v2 sched
JOIN source_cassandra_delivery.region r ON sched.region_id = r.id
WHERE offer.courier_uuid = sched.courier_id
AND offer.offer_time_local >= date_parse(date_format(AT_TIMEZONE("start",r.time_zone),'%Y-%m-%d %H:%i:%s'),'%Y-%m-%d %H:%i:%s')
AND offer.offer_time_local <= date_parse(date_format(AT_TIMEZONE("end",r.time_zone),'%Y-%m-%d %H:%i:%s'),'%Y-%m-%d %H:%i:%s')
AND element_at(sched.state,-1) = 'ASSIGNED')
ORDER BY 3
Is there anything wrong with my not exists clause? I am only asking since I am getting back lesser rows than expected. The not exists caluse contains a time conversion but i dont think that would affect anything.
I am trying to get all possible ids and their offer times that do NOT EXIST in the scheduled shifts table. I wanted confirm if the way I have the NOT EXISTS clause is correct or if there is something else I would need that would correctly pull all records that exist or not exist in that shed table?
I'm trying to include a column calculated as a % of OTYPE.
IE
Order type | Status | volume of orders at each status | % of all orders at this status
SELECT
T.OTYPE,
STATUS_CD,
COUNT(STATUS_CD) AS STATVOL,
(STATVOL / COUNT(ROW_ID)) * 100
FROM Database.S_ORDER O
LEFT JOIN /* Finding definitions for status codes & attaching */
(
SELECT
ROW_ID AS TYPEJOIN,
"NAME" AS OTYPE
FROM database.S_ORDER_TYPE
) T
ON T.TYPEJOIN = ORDER_TYPE_ID
GROUP BY (T.OTYPE, STATUS_CD)
/*Excludes pending and pending online orders */
WHERE CAST(CREATED AS DATE) = '2018/09/21' AND STATUS_CD <> 'Pending'
AND STATUS_CD <> 'Pending-Online'
ORDER BY T.OTYPE, STATUS_CD DESC
OTYPE STATUS_CD STATVOL TOTALPERC
Add New Service Provisioning 2,740 100
Add New Service In-transit 13 100
Add New Service Error - Provisioning 568 100
Add New Service Error - Integration 1 100
Add New Service Complete 14,387 100
Current output just puts 100 at every line, need it to be a % of total orders
Could anyone help out a Teradata & SQL student?
The complication making this difficult is my understanding of the group by and count syntax is tenuous. It took some fiddling to get it displayed as I have it, I'm not sure how to introduce a calculated column within this combo.
Thanks in advance
There are a couple of places the total could be done, but this is the way I would do it. I also cleaned up your other sub query which was not required, and changed the date to a non-ambiguous format (change it back if it cases an issue in Teradata)
SELECT
T."NAME" as OTYPE,
STATUS_CD,
COUNT(STATUS_CD) AS STATVOL,
COUNT(STATUS_CD)*100/TotalVol as Pct
FROM database.S_ORDER O
LEFT JOIN EDWPRDR_VW40_SBLCPY.S_ORDER_TYPE T on T.ROW_ID = ORDER_TYPE_ID
cross join (select count(*) as TotalVol from database.S_ORDER) Tot
GROUP BY T."NAME", STATUS_CD, TotalVol
WHERE CAST(CREATED AS DATE) = '2018-09-21' AND STATUS_CD <> 'Pending' AND STATUS_CD <> 'Pending-Online'
ORDER BY T."NAME", STATUS_CD DESC
A where clause comes before a group by clause, so the query
shown in the question isn't valid.
Always prefix every column reference with the relevant table alias, below I have assumed that where you did not use the alias that it belongs to the orders table.
You probably do not need a subquery for this left join. While there are times when a subquery is needed or good for performance, this does not appear to be the case here.
Most modern SQL compliant databases provide "window functions", and Teradata does do this. They are extremely useful, and here when you combine count() with an over clause you can get the total of all rows without needing another subquery or join.
Because there is neither sample data nor expected result provided with the question I do not actually know which numbers you really need for your percentage calculation. Instead I have opted to show you different ways to count so that you can choose the right ones. I suspect you are getting 100 for each row because the count(status_cd) is equal to the count(row_id). You need to count status_cd differently to how you count row_id. nb: The count() function increases by 1 for every non-null value
I changed the way your date filter is applied. It is not efficient to change data on every row to suit constants in a where clause. Leave the data untouched and alter the way you apply the filter to suit the data, this is almost always more efficient (search sargable)
SELECT
t.OTYPE
, o.STATUS_CD
, COUNT(o.STATUS_CD) count_status
, COUNT(t.ROW_ID count_row_id
, count(t.row_id) over() count_row_id_over
FROM dbo.S_ORDER o
LEFT JOIN dbo.S_ORDER_TYPE t ON t.TYPEJOIN = o.ORDER_TYPE_ID
/*Excludes pending and pending online orders */
WHERE o.CREATED >= '2018-09-21' AND o.CREATED < '2018-09-22'
AND o.STATUS_CD <> 'Pending'
AND o.STATUS_CD <> 'Pending-Online'
GROUP BY
t.OTYPE
, o.STATUS_CD
ORDER BY
t.OTYPE
, o.STATUS_CD DESC
As #TomC already noted, there's no need for the join to a Derived Table. The simplest way to get the percentage is based on a Group Sum. I also changed the date to an Standard SQL Date Literal and moved the where before group by.
SELECT
t."NAME",
o.STATUS_CD,
Count(o.STATUS_CD) AS STATVOL,
-- rule of thumb: multiply first then divide, otherwise you will get unexpected results
-- (Teradata rounds after each calculation)
100.00 * STATVOL / Sum(STATVOL) Over ()
FROM database.S_ORDER AS O
/* Finding definitions for status codes & attaching */
LEFT JOIN database.S_ORDER_TYPE AS t
ON t.ROW_ID = o.ORDER_TYPE_ID
/*Excludes pending and pending online orders */
-- if o.CREATED is a Timestamp there's no need to apply the CAST
WHERE Cast(o.CREATED AS DATE) = DATE '2018-09-21'
AND o.STATUS_CD NOT IN ('Pending', 'Pending-Online')
GROUP BY (T.OTYPE, o.STATUS_CD)
ORDER BY T.OTYPE, o.STATUS_CD DESC
Btw, you probably don't need an Outer Join, Inner should return the same result.
Hi I have a redshift table of articles that has a field on it that can contain many accounts. So there is a one to many relationship between articles to accounts.
However I want to create a new view where it lists the partner id's in one column and in another column a count of how many times the partner id appears in the articles table.
I've attempted to do this using regex and created a new redshift view, but am getting weird results where it doesn't always build properly. So one day it will say a partner appears 15 times, then the next 17, then the next 15, when the partner id count hasn't actually changed.
Any help would be greatly appreciated.
SELECT partner_id,
COUNT(DISTINCT id)
FROM (SELECT id,
partner_ids,
SPLIT_PART(partner_ids,',',i) partner_id
FROM positron_articles a
LEFT JOIN util.seq_0_to_500 s
ON s.i < regexp_count (partner_ids,',') + 2
OR s.i = 1
WHERE i > 0
AND regexp_count (partner_ids,',') = 0
ORDER BY id)
GROUP BY 1;
Let's start with some of the more obvious things and see if we can start to glean other information.
Next GROUP BY 1 on your outer query needs to be GROUP BY partner_id.
Next you don't need an order by in your INNER query and the database engine will probably do a better job optimizing performance without it so remove ORDER BY id.
If you want your final results to be ordered then add an ORDER BY partner_id or similar clause after your group by of your OUTER query.
It looks like there are also problems with how you are splitting a partnerid from partnerids but I am not positive about that because I need to understand your view and the data it provides to know how that affects your record count for partnerid.
Next your LEFT JOIN statement on the util.seq_0_to_500 I am pretty sure you can drop off the s.i = 1 as the first condition will satisfy that as well because 2 is greater than 1. However your left join really acts more like an inner join because you then exclude any non matches from positron_articles that don't have a s.i > 0.
Oddly then your entire join and inner query gets kind of discarded because you only want articles that have no commas in their partnerids: regexp_count (partner_ids,',') = 0
I would suggest posting the code for your util.seq_0_to_500 and if you have a partner table let use know about that as well because you can probably get your answer a lot easier with that additional table depending on how regexp_count works. I suspect regex_count(partnerids,partnerid) exampleregex_count('12345,678',1234) will return greater than 0 at which point you have no choice but to split the delimited strings into another table before counting or building a new matching function.
If regex_count only matches exact between commas and you have a partner table your query could be as easy as this:
SELECT
p.partner_id
,COUNT(a.id) AS ArticlesAppearedIn
FROM
positron_articles a
LEFT JOIN PARTNERTABLE p
ON regexp_count(a.partnerids,p.partnerid) > 0
GROUP BY
p.partner_id
I will actually correct myself as I just thought of a way to join a partner table without regexp_count. So if you have a partner table this might work for you. If not you will need to split strings. It basically tests to see if the partnerid is the entire partnerids, at the beginning, in the middle, or at the end of partnerids. If one of those is met then the records is returned.
SELECT
p.partner_id
,COUNT(a.id) AS ArticlesAppearedIn
FROM
PARTNERTABLE p
INNER JOIN positron_articles a
ON
(
CASE
WHEN a.partnerids = CAST(p.partnerid AS VARCHAR(100)) THEN 1
WHEN a.partnerids LIKE p.partnerid + ',%' THEN 1
WHEN a.partnerids LIKE '%,' + p.partnerid + ',%' THEN 1
WHEN a.partnerids LIKE '%,' + p.partnerid THEN 1
ELSE 0
END
) = 1
GROUP BY
p.partner_id
I am writing a sql query to get data from different tables but it is getting data from different tables very slowly.
Approximately above 2 minutes to complete.
What i am doing is here :
1. I am getting data differences and on behalf of date difference i am getting account numbers
2. I am comparing tables to get exact data i need.
here is my query
select T.accountno,
MAX(T.datetxn) as MxDt,
datediff(MM,MAX(T.datetxn), '2011-6-30') as Diffs,
max(P.Name) as POName
from Account_skd A,
AccountTxn_skd T,
POName P
where A.AccountNo = T.AccountNo and
GPOCode = A.OfficeCode and
Code = A.POCode and
A.servicecode = T.ServiceCode
group by T.AccountNo
order by len(T.AccountNo) DESC
please help that how i can use joins or any other way to get data within very less time say 5-10 seconds.
Since it appears you are getting EVERY ACCOUNT, and performance is slow, I would try by creating a prequery by just account, then do a single join to the other join tables something like..
select
T.Accountno,
T.MxDt,
datediff(MM, T.MxDt, '2011-6-30') as Diffs,
P.Name as POName
from
( select T1.AccountNo,
Max( T1.DateTxn ) MxDt
from AccontTxn_skd T1
group by T1.AccountNo ) T
JOIN Account_skd A
on T.AccountNo = A.AccountNo
JOIN POName P
on A.POCode = P.Code <-- GUESSING as you didn't qualify alias.field
AND A.OfficeCode = P.GPOCode <-- in your query for these two fields
order by
len(T.AccountNo) DESC
You had other elements based on the T.ServiceCode matching, but since you are only grouping on the account number anyhow, did it matter which service code was used? Otherwise, you would need to group by both the account AND service code (which I would have added the service code into the prequery and added as join condition to the account table too).
I'm trying to using the aggregation features of the django ORM to run a query on a MSSQL 2008R2 database, but I keep getting a timeout error. The query (generated by django) which fails is below. I've tried running it directs the SQL management studio and it works, but takes 3.5 min
It does look it's aggregating over a bunch of fields which it doesn't need to, but I wouldn't have though that should really cause it to take that long. The database isn't that big either, auth_user has 9 records, ticket_ticket has 1210, and ticket_watchers has 1876. Is there something I'm missing?
SELECT
[auth_user].[id],
[auth_user].[password],
[auth_user].[last_login],
[auth_user].[is_superuser],
[auth_user].[username],
[auth_user].[first_name],
[auth_user].[last_name],
[auth_user].[email],
[auth_user].[is_staff],
[auth_user].[is_active],
[auth_user].[date_joined],
COUNT([tickets_ticket].[id]) AS [tickets_captured__count],
COUNT(T3.[id]) AS [assigned_tickets__count],
COUNT([tickets_ticket_watchers].[ticket_id]) AS [tickets_watched__count]
FROM
[auth_user]
LEFT OUTER JOIN [tickets_ticket] ON ([auth_user].[id] = [tickets_ticket].[capturer_id])
LEFT OUTER JOIN [tickets_ticket] T3 ON ([auth_user].[id] = T3.[responsible_id])
LEFT OUTER JOIN [tickets_ticket_watchers] ON ([auth_user].[id] = [tickets_ticket_watchers].[user_id])
GROUP BY
[auth_user].[id],
[auth_user].[password],
[auth_user].[last_login],
[auth_user].[is_superuser],
[auth_user].[username],
[auth_user].[first_name],
[auth_user].[last_name],
[auth_user].[email],
[auth_user].[is_staff],
[auth_user].[is_active],
[auth_user].[date_joined]
HAVING
(COUNT([tickets_ticket].[id]) > 0 OR COUNT(T3.[id]) > 0 )
EDIT:
Here are the relevant indexes (excluding those not used in the query):
auth_user.id (PK)
auth_user.username (Unique)
tickets_ticket.id (PK)
tickets_ticket.capturer_id
tickets_ticket.responsible_id
tickets_ticket_watchers.id (PK)
tickets_ticket_watchers.user_id
tickets_ticket_watchers.ticket_id
EDIT 2:
After a bit of experimentation, I've found that the following query is the smallest that results in the slow execution:
SELECT
COUNT([tickets_ticket].[id]) AS [tickets_captured__count],
COUNT(T3.[id]) AS [assigned_tickets__count],
COUNT([tickets_ticket_watchers].[ticket_id]) AS [tickets_watched__count]
FROM
[auth_user]
LEFT OUTER JOIN [tickets_ticket] ON ([auth_user].[id] = [tickets_ticket].[capturer_id])
LEFT OUTER JOIN [tickets_ticket] T3 ON ([auth_user].[id] = T3.[responsible_id])
LEFT OUTER JOIN [tickets_ticket_watchers] ON ([auth_user].[id] = [tickets_ticket_watchers].[user_id])
GROUP BY
[auth_user].[id]
The weird thing is that if I comment out any two lines in the above, it runs in less that 1s, but it doesn't seem to matter which lines I remove (although obviously I can't remove a join without also removing the relevant SELECT line).
EDIT 3:
The python code which generated this is:
User.objects.annotate(
Count('tickets_captured'),
Count('assigned_tickets'),
Count('tickets_watched')
)
A look at the execution plan shows that SQL Server is first doing a cross-join on all the table, resulting in about 280 million rows, and 6Gb of data. I assume that this is where the problem lies, but why is it happening?
SQL Server is doing exactly what it was asked to do. Unfortunately, Django is not generating the right query for what you want. It looks like you need to count distinct, instead of just count: Django annotate() multiple times causes wrong answers
As for why the query works that way: The query says to join the four tables together. So say an author has 2 captured tickets, 3 assigned tickets, and 4 watched tickets, the join will return 2*3*4 tickets, one for each combination of tickets. The distinct part will remove all the duplicates.
what about this?
SELECT auth_user.*,
C1.tickets_captured__count
C2.assigned_tickets__count
C3.tickets_watched__count
FROM
auth_user
LEFT JOIN
( SELECT capturer_id, COUNT(*) AS tickets_captured__count
FROM tickets_ticket GROUP BY capturer_id ) AS C1 ON auth_user.id = C1.capturer_id
LEFT JOIN
( SELECT responsible_id, COUNT(*) AS assigned_tickets__count
FROM tickets_ticket GROUP BY responsible_id ) AS C2 ON auth_user.id = C2.responsible_id
LEFT JOIN
( SELECT user_id, COUNT(*) AS tickets_watched__count
FROM tickets_ticket_watchers GROUP BY user_id ) AS C3 ON auth_user.id = C3.user_id
WHERE C1.tickets_captured__count > 0 OR C2.assigned_tickets__count > 0
--WHERE C1.tickets_captured__count is not null OR C2.assigned_tickets__count is not null -- also works (I think with beter performance)