SQL Server – Optimize Optional Parameters for Better Performance

Depending on the amount of RAM your server has, it might just read the whole table into memory and keep it there - so your first query will load it, and any subsequent queries will just operate on those pages loaded in memory.

To get any meaningful data, you need to flush the cache before every run of your query! (run DBCC FREEPROCCACHE and DBCC DROPCLEANBUFFERS before any query execution!)

I tried your scenario with 100'000 dummy file names and almost the same table structure (I changed FileName to be VARCHAR(260)).

Without index:

Table 'TestFiles'. Scan count 1, logical reads 2137, physical reads 5, read-ahead reads 2136, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Query Subtree Cost 1.68884

With non-clustered index on filename:

Table 'TestFiles'. Scan count 1, logical reads 6, physical reads 4, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

Query Subtree Cost 0.0065704

This was done with this statement:

DBCC FREEPROCCACHE
DBCC DROPCLEANBUFFERS 

SET STATISTICS IO ON

SELECT * 
FROM testfiles
WHERE FileName = 'File-D8584B44-518F-428A-86A1-7836E0B60502'

I love your approach to careful consideration to query tuning and reviewing options and plans. I wish more developers did this. One caution would be - always test with a lot of rows, looking at the logical reads, this is a smallish table. Try and generate a sample load and run the query again. One small issue - in your top query you are not asking for an order by, in your bottom query you are. You should compare and contrast them each with ordering.

I just quickly created a SalesOrders table with 200,000 sales orders in it - still not huge by any stretch of the imagination. And ran the queries with the ORDER BY in each. I also played with indexes a bit.

With no clustered index on OrderID, just a non-clustered index on CustID The second query outperformed. Especially with the order by included in each. There was twice as many reads on the first query than the second query, and the cost percentages were 67% / 33% between the queries.

With a clustered index on OrderID and a non-clustered index just on CustID They performed in a similar speed and the exact same number of reads.

So I would suggest you increase the number of rows and do some more testing. But my final analysis on your queries -

You may find them behaving more similarly than you realize when you increase the rows, so keep that caveat in mind and test that way.

If all you ever want to return is the maximum OrderID for each Customer, and you want to determine that by the OrderID being the greatest OrderID then the second query out of these two is the best way to go from my mindset - it is a bit simpler and while ever so slightly more expensive based on subtree cost it is a quicker and easier to decipher statement. If you intend on adding other columns into your result set someday? Then the first query allows you do to do that.

Updated: One of your comments under your question was:

Please keep in mind, that finding the best query in this question is a means of refining the techniques used for comparing them.

But best takeaway for doing that- test with more data - always makes sure you have data consistent with production and expected future production. Query plans start looking data when you give more rows to the tables, and try and keep the distribution what you'd expect in production. And pay attention to things like including Order By or not, here I don't think it makes a terrible bit of difference in the end, but still worth digging into.

Your approach of comparing this level of detail and data is a good one. Subtree costs are arbitrary and meaningless mostly, but still worth at least looking at for comparison between edits/changes or even between queries. Looking at the time statistics and the IO are quite important, as is looking at the plan for anything that feels out of place for the size of the data you are working with and what you are trying to do.