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Assume we have an #RelevantOrders table and an OrderRow table.

create table #RelevantOrders(Order_Id int primary key)

create talbe OrderRow (Order_Id int, OrderRow_Id, [lots of data columns], primary key (Order_Id, OrderRow_Id)

Now what I by instinct did initialy do was a simple join

select OrderRow.* from #RelevantOrders as ro join OrderRow on OrderRow.Order_Id = ro.Order_Id

Now, due to the data, the Order_Id in #RelevantOrders are several ranges of Ids, ie. 1,2,3,4,5,100,101...120, 240..250 ... hope i am being clear that.

So what happens, if there are 1000 Relevant orders, execution plan does 1000 index seeks on OrderRow table, 1 per each Order_Id. This leads quite 'big' scan count and logical reads (~5000).

SO it got me thinking, i would expect that instead of doing 1000 index seeks, it would do ~80 (number of continuous ranges) index seeks.

So i tried an alternative:

create table #RelevantOrderRanges(f int, t int)

select OrderRow.* from #RelevantOrderRanges
join OrderRow on OrderRow.Order_Id between f and t

The good news is ammount of index seek and logical reads drops to 80 index seeks (equal to amount of ranges) and 609 logical reads. Much better. The only issue i have here and thats due to my experience with it causing to gobble up memory is the estimated number of rows and estimated data size, which goes up to about 3.5GB in this case instad of 49KBs.

Now i know that in case number of ranges approaches number of orders any gain is lost.

So this got me thinking and i have two questions: 1) am i overthinking the estimates too much? I know more testing is required but i am a bit afraid the estimates would bite me later due to memory hog

2) is there any way to 'better explain' SQL what to expect? Indices, hints, statistics.... ?

Thanks for discussion

PERFORMANCE UPDATE:
TC 1:
Order_Id count: 9045
Range count: 5499
Total rows produced:

Range based approach:
~800ms CPU, ~800ms ellapsed time
Scan count: 5,500   
Logical reads: 37,896

ID join approach:
~900ms CPU, ~900ms ellapsed time
Scan count: 9045
Logical reads: 56774

TC 2:
Order_Id count: 19684
Range count: 12849
Total rows produced: 124561

Range based approach:
~718ms CPU, ~770ms ellapsed time
Scan count: 12849
Logical reads: 74127

ID join approach:
~950ms CPU, ~950ms ellapsed time
Scan count: 19684
Logical reads: 110352
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  • What version of sql server are you using? Is your primary key clustered? Do you have any other indexes? Yes you can use hints to influence behaviour but i would avoid that unless you have a clear need. – Sir Swears-a-lot Apr 21 '20 at 10:03
  • How bad is performance now? How fast does it need to be? Do you have query store enabled? How many rows in each table? How much growth are you expectibg? – Sir Swears-a-lot Apr 21 '20 at 10:13
  • you may try to use SELECT o.* FROM #RelevantOrderRanges AS r CROSS APPLY (SELECT * FROM OrderRow WHERE Order_Id between r.f and r.t) AS o – NikitaSerbskiy Apr 21 '20 at 11:18
  • @Nikita performance wise no change, estimate still way off. – Vladislav Zalesak Apr 21 '20 at 12:01
  • @SirSwears-a-lot right now the performance in production is ... borderline?. This is kind of selfimproement/study. The biggest issue is that this query can run several hundreds - thousand times in hour and parallel. At 100 times ,with 25% reduction in logical reads estimated, that equals to ~ 3 000 000 logical reads per hour. And 100 times/hour is LOW – Vladislav Zalesak Apr 21 '20 at 12:26
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Edit. I misunderstood the question. I thought this was about app design rather than query performance.

For an App: Personally i would avoid using an intermediate table. Its just extra steps and complication.

The #table makes sense for queries.

Provided you have useful indexes and your stats are up to date, you are typically better off leaving SQL Server to decide the best execution plan.

Depending on the growth and distribution of data the best execution plan may change over time. So i would be reluctant to invest a lot of time and effort coding to squeeze the last 1% of extra performance.

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  • This intermediate table may come from some complex query for which is hard to estimate the amount of rows returned. In this case temporary table may be useful for better estimations and helps SQL Server to generate the best execution plan. – NikitaSerbskiy Apr 21 '20 at 11:12
  • as stated the temp table is product of a process that simply said 1) must remain 2) is too complex – Vladislav Zalesak Apr 21 '20 at 12:01

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