Sql-server – How to troubleshoot how resource governed query is being throttled in SQL 2012

would it even care to go through this memory grant queue or right away spill to the disk?

It doesn't work that way. Once a plan was chosen that requires a memory grant, the query must obtain the grant so it goes through the queue. Spilling, if any, occurs much later in the execution cycle. A query cannot decide to go ahead without the grant and 'spill' instead. It must get the grant and, based on that, start the execution. If the grant turns out to be insufficient (due to bad estimate or due to obtaining a much lower grant than requested) then the query will be forced to spill.

if query optimizer realize that due to bad statistics or missing indexes

Strictly speaking it's not the optimizer, it's the query execution. Query Optimizer has only a say in deciding the plan, but once the plan is chosen and launched into execution the optimizer is out of the picture. Also, 'missing indexes' play no role here. A missing index can force a bad plan, but it cannot influence how that 'bad' plan is executed since that plan was built considering exactly what indexes actually exist so it knows exactly what it can and cannot do. Spills occur almost always due to bad estimates, ie. bad stats or bad cardinality estimation algorithms in the optimizer and in the execution (it gets really complex if you dig into the details so I'll stop here).

Even if system has plenty of workspace memory available during the execution once the query exceeded the granted workspace memory during the execution it will has to spill to the disk

Unfortunately, yes. However the optimizer should come up with a plan that leverages the available RAM.

I recommend reading Understanding SQL server memory grant, which is the best info on the subject by a wide margin.

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.