I am using a query the join columns one has a clustered index and the other has a non-clustered index. The query is taking a long time. Is this the reason that I am using different type of indexes ?
SELECT #NoOfOldBills = COUNT(*)
FROM Billing_Detail D, Meter_Info m, Meter_Reading mr
WHERE D.Meter_Reading_ID = mr.id
AND m.id = mr.Meter_Info_ID
AND m.id = #Meter_Info_ID
AND BillType = 'Meter'
IF (#NoOfOldBills > 0) BEGIN
SELECT TOP 1 #PReadingDate = Bill_Date
FROM Billing_Detail D, Meter_Info m, Meter_Reading mr
WHERE D.Meter_Reading_ID = mr.id
AND m.id = mr.Meter_Info_ID
AND m.id = #Meter_Info_ID
AND billtype = 'Meter'
ORDER BY Bill_Date DESC
END
Without knowing more details and the context, it's tricky to advise - but it looks like you are trying to find out the date of the oldest bill. You could probably rewrite those two queries as one, which would improve the performance significantly (assuming that there are some old bills).
I would suggest something like this - which in addition to probably performing better, is a little easier to read!
SELECT count(d.*) NoOfOldBills, MAX(d.Billing_Date) OldestBillingDate FROM Billing_Detail d
INNER JOIN Meter_Reading mr ON mr.id=d.Meter_Reading_ID
INNER JOIN Meter_Info m ON m.Id=mr.Meter_Info_ID
WHERE
m.id = #Meter_Info_ID AND billtype = 'Meter'
The reason is not because you have different types of indices. Since you said you have clustered indices on all primary keys, you should be fine there. To support this query, you would also need an index with two columns on BillType and Bill_Date to cut down the time.
Hopefully they are in the same table. Otherwise, you may need a few indices to finally create one that does have two columns.
OK there are a number of things here. First, are the indexes you have relevant (or as relevant as they can be). There should be a clustered index on each table - a non clustered index does not work effectively if the table does not have a clustered index which non clustered indexes use to identify rows.
Does your index cover only one column? SQL Server uses indexes with the order of the columns for that index being very important. The left most column of the index should (in general) be the column that has the greatest ordinality (divides the data into the smallest amounts) Do either of the indexes cover all the columns referred to in the query (this is known as a covering index (Google this for more info).
In general indexes should be wide, if SQL Server has an index on col1, col2, col3, col4 and another on col1, col2 the later is redundant, as the information from the second index is fully contained in the first and SQL Server understands this.
Are you statistics up to date? SQL Server can / will choose a bad execution plan if the statistics are not up to date. What does query analyser show for plan execution (SSMS Query | show execution plan)?
Related
The question is for Firebird 2.5. Let's assume we have the following query:
SELECT
EVENTS.ID,
EVENTS.TS,
EVENTS.DEV_TS,
EVENTS.COMPLETE_TS,
EVENTS.OBJ_ID,
EVENTS.OBJ_CODE,
EVENTS.SIGNAL_CODE,
EVENTS.SIGNAL_EVENT,
EVENTS.REACTION,
EVENTS.PROT_TYPE,
EVENTS.GROUP_CODE,
EVENTS.DEV_TYPE,
EVENTS.DEV_CODE,
EVENTS.SIGNAL_LEVEL,
EVENTS.SIGNAL_INFO,
EVENTS.USER_ID,
EVENTS.MEDIA_ID,
SIGNALS.ID AS SIGNAL_ID,
SIGNALS.SIGNAL_TYPE,
SIGNALS.IMAGE AS SIGNAL_IMAGE,
SIGNALS.NAME AS SIGNAL_NAME,
REACTION.INFO,
USERS.NAME AS USER_NAME
FROM EVENTS
LEFT OUTER JOIN SIGNALS ON (EVENTS.SIGNAL_ID = SIGNALS.ID)
LEFT OUTER JOIN REACTION ON (EVENTS.ID = REACTION.EVENTS_ID)
LEFT OUTER JOIN USERS ON (EVENTS.USER_ID = USERS.ID)
WHERE (TS BETWEEN '27.07.2021 00:00:00' AND '28.07.2021 10:34:08')
AND (OBJ_ID = 8973)
AND (DEV_CODE IN (0, 1234))
AND (DEV_TYPE = 79)
AND (PROT_TYPE = 8)
ORDER BY TS;
EVENTS has about 190 million records by now and this query takes too much time to complete. As I read here, the tables have to have indexes on all the columns that are used.
Here are the CREATE INDEX statements for the EVENTS table:
CREATE INDEX FK_EVENTS_OBJ ON EVENTS (OBJ_ID);
CREATE INDEX FK_EVENTS_SIGNALS ON EVENTS (SIGNAL_ID);
CREATE INDEX IDX_EVENTS_COMPLETE_TS ON EVENTS (COMPLETE_TS);
CREATE INDEX IDX_EVENTS_OBJ_SIGNAL_TS ON EVENTS (OBJ_ID,SIGNAL_ID,TS);
CREATE INDEX IDX_EVENTS_TS ON EVENTS (TS);
Here is the data from the PLAN analyzer:
PLAN JOIN (JOIN (JOIN (EVENTS ORDER IDX_EVENTS_TS INDEX (FK_EVENTS_OBJ, IDX_EVENTS_TS), SIGNALS INDEX (PK_SIGNALS)), REACTION INDEX (IDX_REACTION_EVENTS)), USERS INDEX (PK_USERS))
As requested the speed of the execution:
without LEFT JOIN -> 138ms
with LEFT JOIN -> 338ms
Is there another way to speed up the execution of the query besides indexing the columns or maybe add another index?
If I add another index will the optimizer choose to use it?
You can only optimize the joins themselves by being sure that the keys are indexed in the second tables. These all look like primary keys, so they should have appropriate indexes.
For this WHERE clause:
WHERE TS BETWEEN '27.07.2021 00:00:00' AND '28.07.2021 10:34:08')
OBJ_ID = 8973 AND
DEV_CODE IN (0, 1234) AND
DEV_TYPE = 79 AND
PROT_TYPE = 8
You probably want an index on (OBJ_ID, DEV_TYPE, PROT_TYPE, TS, DEV_CODE). The order of the first three keys is not particularly important because they are all equality comparisons. I am guessing that one day of data is fewer rows than two device codes.
First of all you want to find the table1 rows quickly. You are using several columns in your WHERE clause to get them. Provide an index on these columns. Which column is the most selective? I.e. which criteria narrows the result rows most? Let's say it's dt, so we put this first:
create index idx1 on table1 (dt, oid, pt, ts, dc);
I have put ts and dt last, because we are looking for more than one value in these columns. It may still be that putting ts or dsas the first column is a good choice. Sometimes we have to play around with this. I.e. provide several indexes with the column order changed and then see which one gets used by the DBMS.
Tables table2 and tabe4 get accessed by the primary key for which exists an index. But table3 gets accessed by t1id. So provide an index on that, too:
create index idx2 on table3 (t1id);
I have a table which is a link table from objects in my SQL Server 2012 database, (annonsid, annonsid2). This table is used to create chains of triangle or even rectangles to see who can swap with who.
This is the query I use on the table Matching_IDs which has 1,5 million rows in it, producing 14 million possible chains using this query:
SELECT COUNT(*)
FROM Matching_IDs AS m
INNER JOIN Matching_IDs AS m2
ON m.annonsid2 = m2.annonsid
INNER JOIN Matching_IDs AS m3
ON m2.annonsid2 = m3.annonsid
AND m.annonsid = m3.annonsid2
I must improve performance to take maybe 1 second or less, Is there a faster way to do this? The query takes about 1 minute on my computer. I normally use a WHERE m.annonsid=x, but it takes just the same amount of time, cause it has to go through all possible combinations anyway.
Update: the latest query plan
|--Compute Scalar(DEFINE:([Expr1006]=CONVERT_IMPLICIT(int,[globalagg1011],0)))
|--Stream Aggregate(DEFINE:([globalagg1011]=SUM([partialagg1010])))
|--Parallelism(Gather Streams)
|--Stream Aggregate(DEFINE:([partialagg1010]=Count(*)))
|--Hash Match(Inner Join, HASH:([m2].[annonsid2], [m2].[annonsid])=([m3].[annonsid], [m].[annonsid2]), RESIDUAL:([MyDatabase].[dbo].[Matching_IDs].[annonsid2] as [m2].[annonsid2]=[MyDatabase].[dbo].[Matching_IDs].[annonsid] as [m3].[annonsid] AND [MyDatabase].[dbo].[Matching_IDs].[annonsid2] as [m].[annonsid2]=[MyDatabase].[dbo].[Matching_IDs].[annonsid] as [m2].[annonsid]))
|--Parallelism(Repartition Streams, Hash Partitioning, PARTITION COLUMNS:([m2].[annonsid2], [m2].[annonsid]))
| |--Index Scan(OBJECT:([MyDatabase].[dbo].[Matching_IDs].[NonClusteredIndex-20121229-133207] AS [m2]))
|--Parallelism(Repartition Streams, Hash Partitioning, PARTITION COLUMNS:([m3].[annonsid], [m].[annonsid2]))
|--Merge Join(Inner Join, MANY-TO-MANY MERGE:([m].[annonsid])=([m3].[annonsid2]), RESIDUAL:([MyDatabase].[dbo].[Matching_IDs].[annonsid] as [m].[annonsid]=[MyDatabase].[dbo].[Matching_IDs].[annonsid2] as [m3].[annonsid2]))
|--Parallelism(Repartition Streams, Hash Partitioning, PARTITION COLUMNS:([m].[annonsid]), ORDER BY:([m].[annonsid] ASC))
| |--Index Scan(OBJECT:([MyDatabase].[dbo].[Matching_IDs].[NonClusteredIndex-20121229-133152] AS [m]), ORDERED FORWARD)
|--Parallelism(Repartition Streams, Hash Partitioning, PARTITION COLUMNS:([m3].[annonsid2]), ORDER BY:([m3].[annonsid2] ASC))
|--Index Scan(OBJECT:([MyDatabase].[dbo].[Matching_IDs].[NonClusteredIndex-20121229-133207] AS [m3]), ORDERED FORWARD)
Some ideas:
Try two indexes (annonsid,annonsid2) and (annonsid2,annonsid)
Have you tried a column store index? It makes the table read only but it might improve performance.
Also, some variations of the query could help. Examples:
SELECT COUNT(*)
FROM Matching_IDs AS m
INNER JOIN Matching_IDs AS m2
ON m.annonsid2 = m2.annonsid
INNER JOIN Matching_IDs AS m3
ON m2.annonsid2 = m3.annonsid
where m.annonsid = m3.annonsid2
or
SELECT COUNT(*)
FROM Matching_IDs AS m, Matching_IDs AS m2, Matching_IDs AS m3
where m2.annonsid2 = m3.annonsid
and m.annonsid2 = m2.annonsid
and m.annonsid = m3.annonsid2
Did you check the CPU/IO-Load? If IO-Load is high, then the server is not crunching numbers but swapping => more RAM solves the problem.
How fast is this query?
SELECT COUNT(*)
FROM Matching_IDs AS m
INNER JOIN Matching_IDs AS m2
ON m.annonsid2 = m2.annonsid
If this is very fast but adding the next join slows thing down then you propably need more RAM.
It seems like you already indexed this quite well. You can try converting the hash to a merge join by adding the right multi-column index, but it won't give you the desired speedup of 60x.
I think this index would be on annonsid, annonsid2 although I might have made a mistake here.
It would be nice to materialize all of this but indexed views do not support self-joins. You can try to materialize this query (unaggregated) into a new table. Whenever you execute DML against the base table, also update the second table (using either application logic or triggers). That would allow you to query blazingly fast.
You should make this query a bit more separated. I think first you should create a table, where you can store the primary key + annonsid, annonsid2 -if annosid is not the primary key itself.
DECLARE #AnnonsIds TABLE
(
primaryKey int,
-- if you need later more info from the original rows like captions
-- AND it is not (just) the annonsid
annonsid int,
annonsid2 int
)
If you declare a table, and you have index on this column, it is quite fast to get the specified rows by the WHERE annonsid = #annonsid OR annonsid2 = #annosid
After the first step you have a much smaller (I guess), and "thin" table to work with. Then you just have to use the joins here or make a temp table and a CTE on it.
I think it should be faster, depending on the selectivity of the condition in the WHERE, of yourse if 1.1 million rows fits in it, then it does not make sense, but if just a couple hundred or tousend, then you should give it a try!
1 - change select Count(*) to Count(1) or Count(id)
2 - Write set Nocount on at the first of your Stored procedure or at the first of your Query
3 - Use index on annonsid , annonsid2
4 - Having your Indexes after the primary key in your Table
You could denormalize the data by adding a table RelatedIds with AnnonsId, RelatedAnnonId and Distance. For every value of AnnonsId the table would contains rows for each RelatedAnnonId and the number of relations that need to be traversed to reach it, aka Distance. Triggers on the existing MatchingIds table would maintain the new table with some configured maximum value for Distance, e.g. 3 to handle rectangular shares. Index the table on (AnnonsId, Distance).
Edit: An index on (Distance, AnnonsId) will allow you to quickly find rows that have enough related entries to form a particular shape. Adding a column for MaxDistance may be useful if you want to be able to exclude rows based on, for example, having a triangular but not rectangular relationship.
The new query would inner join RelatedIds as RI on RI.AnnonsId = m.AnnonsId and RI.Distance <= #MaxDistance with the desired "shape" dictating the value of #MaxDistance.
It should provide much better performance on the select. Downsides are another table with a large number of rows and overhead of the triggers when altering the MatchingIds table.
Example: There are two entries in Matching_IDs: (1,2) and (2,3).
The new table would contain 3 entries:
1-> 2: distance = 1
1-> 3: distance = 2 (it takes one intermediate 'node' to go from 1 to 3)
2-> 3: distance = 1
adding one more entry to the matching ids (3,1) would result in another entry:
1-> 1: distance = 3
And voilá: You found a triangle (distance=3).
Now, to find all triangles, simply do this:
select *
from RelatedIds
where AnnonsId=RelatedAnnonId
and Distance=3
I am performing an update with a query like this:
UPDATE (SELECT h.m_id,
m.id
FROM h
INNER JOIN m
ON h.foo = m.foo)
SET m_id = id
WHERE m_id IS NULL
Some info:
Table h is roughly ~5 million rows
All rows in table h have NULL values for m_id
Table m is roughly ~500 thousand rows
m_id on table h is an indexed foreign key pointing to id on table m
id on table m is the primary key
There are indexes on m.foo and h.foo
The EXPLAIN PLAN for this query indicated a hash join and full table scans, but I'm no DBA, so I can't really interpret it very well.
The query itself ran for several hours and did not complete. I would have expected it to complete in no more than a few minutes. I've also attempted the following query rewrite:
UPDATE h
SET m_id = (SELECT id
FROM m
WHERE m.foo = h.foo)
WHERE m_id IS NULL
The EXPLAIN PLAN for this mentioned ROWID lookups and index usage, but it also went on for several hours without completing. I've also always been under the impression that queries like this would cause the subquery to be executed for every result from the outer query's predicate, so I would expect very poor performance from this rewrite anyway.
Is there anything wrong with my approach, or is my problem related to indexes, tablespace, or some other non-query-related factor?
Edit:
I'm also having abysmal performance from simple count queries like this:
SELECT COUNT(*)
FROM h
WHERE m_id IS NULL
These queries are taking anywhere from ~30 seconds to sometimes ~30 minutes(!).
I am noticing no locks, but the tablespace for these tables is sitting at 99.5% usage (only ~6MB free) right now. I've been told that this shouldn't matter as long as indexes are being used, but I don't know...
Some points:
Oracle does not index NULL values (it will index a NULL that is part of a globally non-null tuple, but that's about it).
Oracle is going for a HASH JOIN because of the size of both h and m. This is likely the best option performance-wise.
The second UPDATE might get Oracle to use indexes, but then Oracle is usually smart about merging subqueries. And it would be a worse plan anyway.
Do you have recent, reasonable statistics for your schema? Oracle really needs decent statistics.
In your execution plan, which is the first table in the HASH JOIN? For best performance it should be the smaller table (m in your case). If you don't have good cardinality statistics, Oracle will get messed up. You can force Oracle to assume fixed cardinalities with the cardinality hint, it may help Oracle get a better plan.
For example, in your first query:
UPDATE (SELECT /*+ cardinality(h 5000000) cardinality(m 500000) */
h.m_id, m.id
FROM h
INNER JOIN m
ON h.foo = m.foo)
SET m_id = id
WHERE m_id IS NULL
In Oracle, FULL SCAN reads not only every record in the table, it basically reads all storage allocated up to the maximum used (the high water mark in Oracle documentation). So if you have had a lot of deleted rows your tables might need some cleaning up. I have seen a SELECT COUNT(*) on an empty table consume 30+ seconds because the table in question had like 250 million deleted rows. If that is the case, I suggest analyzing your specific case with a DBA, so he/she can reclaim space from deleted rows and lower the high water mark.
As far as I remember, a WHERE m_id IS NULL performs a full-table scan, since NULL values cannot be indexed.
Full-table scan means, that the engine needs to read every record in the table to evaluate the WHERE condition, and cannot use an index.
You could try to add a virtual column set to a not-null value if m_id IS NULL, and index this column, and use this column in the WHERE condition.
Then you could also move the WHERE condition from the UPDATE statement to the sub-select, which will probably make the statement faster.
Since JOINs are expensive, rewriting INNER JOIN m ON h.foo = m.foo as
WHERE h.foo IN (SELECT m.foo FROM m WHERE m.foo IS NOT NULL)
may also help.
For large tables, MERGE is often much faster than UPDATE. Try this (untested):
MERGE INTO h USING
(SELECT h.h_id,
m.id as new_m_id
FROM h
INNER JOIN m
ON h.foo = m.foo
WHERE h.m_id IS NULL
) new_data
ON (h.h_id = new_data.h_id)
WHEN MATCHED THEN
UPDATE SET h.m_id = new_data.new_m_id;
Try undocumented hint /*+ BYPASS_UJVC */. If it works, add an UNIQUE/PK constraint on m.foo.
I would update the table in iterations, for example, add a condition according to where h.date_created > sysdate-30 and after it finishes I would run the same query and change the condition to: where h.date_created between sysdate-30 and sysdate-60 etc. If you don't have a column like date_created maybe there's another column you can filter by ? for example: WHERE m.foo = h.foo AND m.foo between 1 and 10
Only the result of plan can explain why the cost of this update is high, but an educated guess will be that both tables are very big and that there are many NULL values as well as a lot of matching (m.foo = h.foo)...
I have a query which is not using my indexes. Can someone say why?
explain plan set statement_id = 'bad8' for
select
g1.g1id,a.a1id from atable a,
(
select
phone,address,g1id from gtable g
where
g.active = 0 and
(g.name is not null) AND
(SYSDATE - g.CTIME <= 2*365)
) g1
where
(
(a.phone.ph1 = g1.phone.ph1 and
a.phone.ph2 = g1.phone.ph2 and
a.phone.ph3 = g1.phone.ph3
)
OR
(a.address.ad1 = g1.address.ad1 and a.address.ad2 = g1.address.ad2)
)
In both the tables : atable,gtable I have these indexes :
1. On phone.ph1,phone.ph2,phone.ph3
2. On address.ad1,address.ad2
phone,address are of custom data types.
Using Oracle 11g.
Here is the explain plan query and output :
SELECT cardinality "Rows",
lpad(' ',level-1)||operation||' '||
options||' '||object_name "Plan"
FROM PLAN_TABLE
CONNECT BY prior id = parent_id
AND prior statement_id = statement_id
START WITH id = 0
AND statement_id = 'bad8'
ORDER BY id;
Result:
> Rows Plan
490191190 SELECT STATEMENT
> null CONCATENATION
> 490190502 HASH JOIN
> 511841 TABLE ACCESS FULL gtable
> 41332965 PARTITION LIST ALL
> 41332965 TABLE ACCESS FULL atable
> 688 HASH JOIN
> 376893 TABLE ACCESS FULL gtable
> 41332965 PARTITION LIST ALL
> 41332965 TABLE ACCESS FULL atable
Both atable,gtable have more than 10 million rows each.
Most values in columns phone and address don't repeat.
What indices Oracle chosen depends on many factor including things you haven't mentioned in your question such as the number of rows in the table, frequency of values within a column and whether you have separate or combined indices when more than one column is indexed.
Having said that, I suppose that the main reason your indices aren't used are:
You don't join directly with GTABLE / GLOBAL. Instead you join with a view that has three additional WHERE clauses that aren't part of the index and thus make it less effective in this constellation.
The JOIN condition includes an OR, which makes it difficult to use indices.
Update:
If Oracle used your indices to do the join - which is already very difficult due to the OR condition - it would end up with a huge number of ROWIDs. For each ROWID, it then had to fetch the full row. Since a full table scan can easily be up to 50 times faster than a fetch by ROWID (I don't know what value Oracle uses), it will only use the indices if it reliably knows that the join will reduce the number of rows to fetch by a factor of 50.
In your case, there are the remaining WHERE conditions (g.active = 0, g.name is not null, SYSDATE - g.CTIME <= 2*365), which aren't represented in the indices. So they have to applied after the join and after the GTABLE rows have been fetched. This makes it even more difficult to reach a 50 times smaller result set than a full table scan.
So I'm pretty sure the Oracle cost estimate is correct, i.e. using the indices would result in a more expensive query and even longer execution time.
We can say "your query does not use your indexes because does not need them". A hash join is better. To use your indexes, oracle need to full scan them(4 indexes), make two joins, make a rowid or, and after that read from tables probably many blocks. If he belives that the result has many rows, the CBO coose the full scans, because is faster.
There are no conditions that reduce the number of rows taken from tables. There is no range scan. It must do full scans.
We have a table value function that returns a list of people you may access, and we have a relation between a search and a person called search result.
What we want to do is that wan't to select all people from the search and present them.
The query looks like this
SELECT qm.PersonID, p.FullName
FROM QueryMembership qm
INNER JOIN dbo.GetPersonAccess(1) ON GetPersonAccess.PersonID = qm.PersonID
INNER JOIN Person p ON p.PersonID = qm.PersonID
WHERE qm.QueryID = 1234
There are only 25 rows with QueryID=1234 but there are almost 5 million rows total in the QueryMembership table. The person table has about 40K people in it.
QueryID is not a PK, but it is an index. The query plan tells me 97% of the total cost is spent doing "Key Lookup" witht the seek predicate.
QueryMembershipID = Scalar Operator (QueryMembership.QueryMembershipID as QM.QueryMembershipID)
Why is the PK in there when it's not used in the query at all? and why is it taking so long time?
The number of people total 25, with the index, this should be a table scan for all the QueryMembership rows that have QueryID=1234 and then a JOIN on the 25 people that exists in the table value function. Which btw only have to be evaluated once and completes in less than 1 second.
if you want to avoid "key lookup", use covered index
create index ix_QueryMembership_NameHere on QueryMembership (QueryID)
include (PersonID);
add more column names, that you gonna select in include arguments.
for the point that, why PK's "key lookup" working so slow, try DBCC FREEPROCCACHE, ALTER INDEX ALL ON QueryMembership REBUILD, ALTER INDEX ALL ON QueryMembership REORGANIZE
This may help if your PK's index is disabled, or cache keeps wrong plan.
You should define indexes on the tables you query. In particular on columns referenced in the WHERE and ORDER BY clauses.
Use the Database Tuning Advisor to see what SQL Server recommends.
For specifics, of course you would need to post your query and table design.
But I have to make a couple of points here:
You've already jumped to the conclusion that the slowness is a result of the ORDER BY clause. I doubt it. The real test is whether or not removing the ORDER BY speeds up the query, which you haven't done. Dollars to donuts, it won't make a difference.
You only get the "log n" in your big-O claim when the optimizer actually chooses to use the index you defined. That may not be happening because your index may not be selective enough. The thing that makes your temp table solution faster than the optimizer's solution is that you know something about the subset of data being returned that the optimizer does not (specifically, that it is a really small subset of data). If your indexes are not selective enough for your query, the optimizer can't always reasonably assume this, and it will choose a plan that avoids what it thinks could be a worst-case scenario of tons of index lookups, followed by tons of seeks and then a big sort. Oftentimes, it chooses to scan and hash instead. So what you did with the temp table is often a way to solve this problem. Often you can narrow down your indexes or create an indexed view on the subset of data you want to work against. It all depends on the specifics of your wuery.
You need indexes on your WHERE and ORDER BY clauses. I am not an expert but I would bet it is doing a table scan for each row. Since your speed issue is resolved by Removing the INNER JOIN or the ORDER BY I bet the issue is specifically with the join. I bet it is doing the table scan on your joined table because of the sort. By putting an index on the columns in your WHERE clause first you will be able to see if that is in fact the case.
Have you tried restructuring the query into a CTE to separate the TVF call? So, something like:
With QueryMembershipPerson
(
Select QM.PersonId, P.Fullname
From QueryMembership As qm
Join Person As P
On P.PersonId = QM.PersonId
Where QM.QueryId = 1234
)
Select PersonId, Fullname
From QueryMembershipPerson As QMP
Join dbo.GetPersonAccess(1) As PA
On PA.PersonId = QMP.PersonId
EDIT: Btw, I'm assuming that there is an index on PersonId in both the QueryMembership and the Person table.
EDIT What about two table expressions like so:
With
QueryMembershipPerson As
(
Select QM.PersonId, P.Fullname
From QueryMembership As qm
Join Person As P
On P.PersonId = QM.PersonId
Where QM.QueryId = 1234
)
, With PersonAccess As
(
Select PersonId
From dbo.GetPersonAccess(1)
)
Select PersonId, Fullname
From QueryMembershipPerson As QMP
Join PersonAccess As PA
On PA.PersonId = QMP.PersonId
Yet another solution would be a derived table like so:
Select ...
From (
Select QM.PersonId, P.Fullname
From QueryMembership As qm
Join Person As P
On P.PersonId = QM.PersonId
Where QM.QueryId = 1234
) As QueryMembershipPerson
Join dbo.GetPersonAccess(1) As PA
On PA.PersonId = QueryMembershipPerson.PersonId
If pushing some of the query into a temp table and then joining on that works, I'd be surprised that you couldn't combine that concept into a CTE or a query with a derived table.