A lot has been written about dynamic SQL over the years, but I ran into a situation recently where I needed to rewrite some code that needed it with minimal disruption to other parts of a stored procedure.
The goal was to set a bunch of variables equal to column values in a table, but the catch was that some of the values that needed to be set also needed to be passed in as search arguments. Here’s a really simplified example:
DECLARE
@i int = 4,
@s nvarchar(MAX) = N'';
SET
@s += N'
SELECT TOP (1)
@i = d.database_id
FROM sys.databases AS d
WHERE d.database_id > @i
ORDER BY d.database_id;
'
EXEC sys.sp_executesql
@s,
N'@i INT OUTPUT',
@i OUTPUT;
SELECT
@i AS input_output;
The result is this:
sinko
All Points In Between
Since we declare @i outside the dynamic SQL and set it to 4, it’s known to the outer scope.
When we execute the dynamic SQL, we tell it to expect the @i parameter, so we don’t need to declare a separate holder variable inside.
We also tell the dynamic SQL block that we expect to output a new value for @i.
While we’re also passing in @i as a parameter.
Mindblowing.
Thanks for reading!
Going Further
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Way back when, SQL Servers with lots of cores could hit some weird contention on CMEMTHREAD. To fix that and a lot of other issues associated with running on a large server, people would resort to all sorts of trace flags.
My dear friend L_____ (b|t) has a list. Maybe not the most up to date list, but there are a lot of good references in the post.
Something we’ve both observed working with large servers is that Trace Flag 8048 might still be necessary under some circumstances.
If you’re running a large server, this trace flag may still be of value even after SQL Server 2016.
Thanks for reading!
Going Further
If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.
In the year 950 B.C., Craig Freedman write a post about subqueries in CASE expressions. It’s amazing how relevant so much of this stuff stays.
In today’s post, we’re going to look at a slightly different example than the one given, and how you can avoid performance problems with them by using APPLY.
Like most query tuning tricks, this isn’t something you always need to employ, and it’s not a best practice. It’s just something you can use when a scalar subquery doesn’t perform as you’d like it to.
How Much Wood
Our starting query looks like this. The point of it is to determine the percentage of answered questions per month.
SELECT
x.YearPeriod,
MonthPeriod =
RIGHT('00' + RTRIM(x.MonthPeriod), 2),
PercentAnswered =
CONVERT(DECIMAL(18, 2),
(SUM(x.AnsweredQuestion * 1.) /
(COUNT_BIG(*) * 1.)) * 100.)
FROM
(
SELECT
YearPeriod = YEAR(p.CreationDate),
MonthPeriod = MONTH(p.CreationDate),
CASE
WHEN EXISTS
(
SELECT
1/0
FROM dbo.Votes AS v
WHERE v.PostId = p.AcceptedAnswerId
AND v.VoteTypeId = 1
)
THEN 1
ELSE 0
END AS AnsweredQuestion
FROM dbo.Posts AS p
WHERE p.PostTypeId = 1
) AS x
GROUP BY
x.YearPeriod,
x.MonthPeriod
ORDER BY
x.YearPeriod ASC,
x.MonthPeriod ASC;
Smack in the middle of it, we have a case expression that goes looking for rows in the Votes table where a question has an answer that’s been voted as the answer.
Amazing.
To start with, we’re going to give it this index.
CREATE INDEX p
ON dbo.Posts(PostTypeId, AcceptedAnswerId)
INCLUDE(CreationDate)
WITH(MAXDOP = 8, SORT_IN_TEMPDB = ON);
Planpains
In all, this query will run for about 18 seconds. The majority of it is spent in a bad neighborhood.
but first
Why does this suck? Boy oh boy. Where do we start?
Sorting the Votes table to support a Merge Join?
Choosing Parallel Merge Joins ever?
Choosing a Many To Many Merge Join ever?
All of the above?
Bout It
If we change the way the query is structured to use OUTER APPLY instead, we can get much better performance in this case.
SELECT
x.YearPeriod,
MonthPeriod =
RIGHT('00' + RTRIM(x.MonthPeriod), 2),
PercentAnswered =
CONVERT(DECIMAL(18, 2),
(SUM(x.AnsweredQuestion * 1.) /
(COUNT_BIG(*) * 1.)) * 100.)
FROM
(
SELECT
YearPeriod = YEAR(p.CreationDate),
MonthPeriod = MONTH(p.CreationDate),
oa.AnsweredQuestion
FROM dbo.Posts AS p
OUTER APPLY
(
SELECT
AnsweredQuestion =
CASE
WHEN v.Id IS NOT NULL
THEN 1
ELSE 0
END
FROM dbo.Votes AS v
WHERE v.PostId = p.AcceptedAnswerId
AND v.VoteTypeId = 1
) oa
WHERE p.PostTypeId = 1
) AS x
GROUP BY
x.YearPeriod,
x.MonthPeriod
ORDER BY
x.YearPeriod ASC,
x.MonthPeriod ASC;
This changes the type of join chosen, and runs for about 3 seconds total.
buttercup
We avoid all of the problems that the parallel many-to-many Merge Join brought us.
Thanks, Hash Join.
It’s also worth noting that the OUTER APPLY plan asks for an index that would help us a bit, though like most missing index requests it’s a bit half-baked.
USE [StackOverflow2013]
GO
CREATE NONCLUSTERED INDEX [<Name of Missing Index, sysname,>]
ON [dbo].[Votes] ([VoteTypeId])
INCLUDE ([PostId])
GO
Index Plus
Any human could look at this query and realize that having the PostId in the key of the index would be helpful, since we’d have it in secondary order to the VoteTypeId column
CREATE INDEX v
ON dbo.Votes(VoteTypeId, PostId)
WITH(MAXDOP = 8, SORT_IN_TEMPDB = ON);
If we add that index, we can make the subquery fairly competitive, at about 4.5 seconds total.
bloop join
But the issue here is now rather than poorly choosing a Sort > Merge Join, we go into a Nested Loops join for ~6 million rows. That’s probably not a good idea.
This index doesn’t leave as profound a mark on the APPLY version of the query. It does improve overall runtime by about half a second, but I don’t think I’d create an index just to get a half second better.
astro weiner
But hey, who knows? Maybe it’d help some other queries, too.
Indexes are cool like that.
Back On The Map
If you’ve got subqueries in your select list that lead to poor plan choices, you do have options. Making sure you have the right indexes in place can go a long way.
You may be able to get competitive performance gains by rewriting them as OUTER APPLY. You really do need to use OUTER here though, because it won’t restrict rows and matches the logic of the subquery. CROSS APPLY would act like an inner join and remove any rows that don’t have a match. That would break the results.
Thanks for reading!
Going Further
If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.
Using the scenario from yesterday’s post as an example of why you might want to think about rewriting queries with Table Spools in them to use temp tables instead, look how the optimizer chooses a plan with an Eager Table Spool.
The “Eager” part means the entire set of rows is loaded into a temporary object at once.
drugas
That’s a lot of rows, innit? Stick some commas in there, and you might just find yourself staring down the barrel of a nine digit number.
Worse, we spend a long time loading data into the spool, and doing so in a serial zone. There’s no good way to know exactly how long the load is because of odd operator times.
If you recall yesterday’s post, the plan never goes back to parallel after that, either. It runs for nearly 30 minutes in total.
Yes Your Tempdb
If you’re gonna be using that hunka chunka tempdb anyway, you might as well use it efficiently. Unless batch mode is an option for you, either as Batch Mode On Rowstore, or tricking the optimizer, this might be your best bet.
Keep in mind that Standard Edition users have an additional limitation where Batch Mode queries are limited to a DOP of 2, and don’t have access to Batch Mode On Rowstore as of this writing. The DOP limitation especially might make the trick unproductive compared to alternatives that allow for MOREDOP.
For example, if we dump that initial join into a temp table, it only takes about a minute to get loaded at a DOP of 8. That is faster than loading data into the spool (I mean, probably. Just look at that thing.).
sweet valley high
The final query to do the distinct aggregations takes about 34 seconds.
lellarap
Another benefit is that each branch that does a distinct aggregation is largely in a parallel zone until the global aggregate.
muggers
In total, both queries finish in about a 1:45. A big improvement from nearly 30 minutes relying on the Eager Table Spool and processing all of the distinct aggregates in a serial zone. The temp table here doesn’t have that particular shortcoming.
In the past, I’ve talked a lot about Eager Index Spools. They have a lot of problems too, many of which are worse. Of course, we need indexes to fix those, not temp tables.
Thanks for reading!
Going Further
If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.
Well over 500 years ago, Paul White wrote an article about distinct aggregates. Considering how often I see it while working with clients, and that Microsoft created column store indexes and batch mode rather than allow for hash join hints on CLR UDFs, the topic feels largely ignored.
But speaking of all that stuff, let’s look at how Batch Mode fixes multiple distinct aggregates.
Jumbo Size
A first consideration is around parallelism, since you don’t pay attention or click links, here’s a quote you won’t read from Paul’s article above:
Another limitation is that this spool does not support parallel scan for reading, so the optimizer is very unlikely to restart parallelism after the spool (or any of its replay streams).
In queries that operate on large data sets, the parallelism implications of the spool plan can be the most important cause of poor performance.
What does that mean for us? Let’s go look. For this demo, I’m using SQL Server 2019 with the compatibility level set to 140.
SELECT
COUNT_BIG(DISTINCT v.PostId) AS PostId,
COUNT_BIG(DISTINCT v.UserId) AS UserId,
COUNT_BIG(DISTINCT v.BountyAmount) AS BountyAmount,
COUNT_BIG(DISTINCT v.VoteTypeId) AS VoteTypeId,
COUNT_BIG(DISTINCT v.CreationDate) AS CreationDate
FROM dbo.Votes AS v;
In the plan for this query, we scan the clustered index of the Votes table five times, or once per distinct aggregate.
skim scan
In case you’re wondering, this results in one intent shared object lock on the Votes table.
This query runs for 38.5 seconds, as the crow flies.
push the thing
A Join Appears
Let’s join Votes to Posts for no apparent reason.
SELECT
COUNT_BIG(DISTINCT v.PostId) AS PostId,
COUNT_BIG(DISTINCT v.UserId) AS UserId,
COUNT_BIG(DISTINCT v.BountyAmount) AS BountyAmount,
COUNT_BIG(DISTINCT v.VoteTypeId) AS VoteTypeId,
COUNT_BIG(DISTINCT v.CreationDate) AS CreationDate
FROM dbo.Votes AS v
JOIN dbo.Posts AS p
ON p.Id = v.PostId;
The query plan now has two very distinct (ho ho ho) parts.
problemium
This is part 1. Part 1 is a spoiler. Ignoring that Repartition Streams is bizarre and Spools are indefensible blights, as we meander across the execution plan we find ourselves at a stream aggregate whose child operators have executed for 8 minutes, and then a nested loops join whose child operators have run for 20 minutes and 39 seconds. Let’s go look at that part of the plan.
downstream
Each branch here represents reading from the same spool. We can tell this because the Spool operators do not have any child operators. They are starting points for the flow of data. One thing to note here is that there are four spools instead of five, and that’s because one of the five aggregates was processed in the first part of the query plan we looked at.
The highlighted branch is the one that accounts for the majority of the execution time, at 19 minutes, 8 seconds. This branch is responsible for aggregating the PostId column. Apparently a lack of distinct values is hard to process.
But why is this so much slower? The answer is parallelism, or a lack thereof. So, serialism. Remember the 500 year old quote from above?
Another limitation is that this spool does not support parallel scan for reading, so the optimizer is very unlikely to restart parallelism after the spool (or any of its replay streams).
In queries that operate on large data sets, the parallelism implications of the spool plan can be the most important cause of poor performance.
Processing that many rows on a single thread is painful across all of the operators.
Flounder Edition
With SQL Server 2019, we get Batch Mode On Row store when compatibility level gets bumped up to 150.
The result is just swell.
yes you can
The second query with the join still runs for nearly a minute, but 42 seconds of the process is scanning that big ol’ Posts table.
Grumpy face.
Thanks for reading!
Going Further
If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.
In this video, I dive into a real-world scenario where I used my SSMS tuning setup to optimize a query for one of my help reviews. The query in question was particularly slow and caused significant delays during live demos due to its execution time. After analyzing the execution plan, which took over 2 minutes to run, I decided to tackle the issue by rewriting the query to eliminate unnecessary join conditions and reduce redundant operations. This approach not only sped up the query significantly but also provided a clearer, more efficient execution path for SQL Server to follow. By sharing this experience, I hope to offer some practical insights into optimizing complex queries for smoother performance in real-world scenarios.
Full Transcript
Oh boy, oh my goodness. This is, this is quite a day, quite a Friday. Uh, Erik Darling here with Erik Darling Data. It would be Darling Data, but someone else, someone else got there first. I mean, for the domain. I mean, I still have the company. Whatever. I mean, no one else got here first. That would be awkward. Like, hostile takeover of my own company. Anyway. Uh, today we are drinking something red that my wife walked in and handed me, so I have no details. Uh, it smells nice though. And, uh, this video is, uh, going to build, oh geez, we’re building things, we’re building on things. There’s a pattern forming here. Uh, where we’re going to, uh, build a little bit on a video that I recorded, I think, last week. I don’t remember.
The weeks are still, time is still very strange to me. Uh, talking about, you know, how I set up SSMS to tune queries. And here’s an example of actually when I used it to do that same, to do that very thing. Same very thing. Uh, recently. To, uh, one of my help reviews. Uh, what’s up memory? And, uh, I don’t know why I named it that. I was, I guess because who is memory sounded stupid. Uh, so, yeah. Uh, so this, this, this help review will tell you, uh, what’s in, what’s in your buffer pool currently. And that’s helpful, I guess, because everyone wants to know what’s in their buffer pool constantly all the time.
Uh, I use it for demos that I do for things that, uh, I find helpful. So, I don’t know. Maybe you will too. I don’t know. If you don’t, go do something else with your life. I don’t care that much. Um, but, what I found is every time I wanted to use this thing on a, uh, on an instance with a good chunk of memory, it would be, it was slow. Terribly, dreadfully, painfully slow. If we look at the execution plan, uh, we see that it took about 2 minutes and 44 seconds to run.
And, uh, who could wait that long for anything? Uh, and it made, uh, certain demos very difficult to do live or even to, like, sort of do off-the-cuff recording. I would have to, like, pre-stage everything so that it would be set up here. So, we were just, like, like, sitting around waiting for this and awkwardly staring at the camera.
Or, like, doing a cut scene or a montage, lifting weights in my short shorts, running on a beach, karate-kicking seagulls, whatever. Uh, but, I mean, just, you know, kind of, like, digging through the execution plan. Again, very helpfully looking at the operator times under these things, right?
So, we no longer have to care about percentages because, I mean, not that they meant anything anyway. Shut up, car. But, it was helpful, it’s much more helpful to sort of, like, follow the yellow brick road of operator times to when things sort of drop off and pick up.
So, kind of framing this a little bit, we have this nested loops join that hits 2.15. And we have this concatenation that is at 0.001. And down here, we have this table spool, which takes a minute and 36 seconds.
Prior to that table spool, we do some nonsense where we hit this view, sysalloc units. And, uh, I’m mispronouncing that would have been deadly. Uh, and this one down here, sys.buffer descriptors.
And we sort of join those together. And then we spend a bunch of time in the spool and a bunch of time in this nested loops join. And this, and the spool, of course, is trying to save us some trouble with the nested loops join.
It’s like, oh, I don’t know how repetitive this stuff is going to be. I don’t know if I want to do everything down here over and over again. I’m going to use a spool to cache some information and reuse it if I can.
Uh, you know, I don’t know how helpful this spool actually was. We look at the properties of the spool. And we look at the, uh, the rebinds and the rewinds.
Well, you know, I guess, I guess it’s okay. Deal with it. But, uh, I don’t like this spool. And if you look at the nested loops join, and kind of zoom in on the tool tip, it’s all happening because of this very, very difficult predicate.
Uh, there’s a lot of and and or logic in this join condition. Let’s go zoom in on what the join condition looked like beforehand. Uh, and this is, you know, sort of many times looking at, uh, queries and looking at, uh, you know, things that people wrote up in the real world.
This is like the enemy of performance. This is not a good thing to do if you want queries to run quickly. Uh, especially, you know, bigger queries, more rows, things you have to worry about, performance. Ah, terrifying.
So, uh, you know, my initial reaction. And I guess we can just come over here. My initial reaction was just to try a query hint on here for no performance spools. Zero performance spools.
Which would get rid of, uh, actually gets rid of a few different kinds of spools in query plans. And get rid of row count spools. Uh, lazy table spools. And, uh, lazy index spools. It does not get rid of eager index spools.
Uh, the only thing that does that is a, uh, uh, turning off certain query rules via other things that we’re not talking about here. But, um, no, with the no performance spool hit, if we look at the query plan, we get a faster one. Still faster.
56 seconds. So we did better. It still takes a full minute. It’s still a lot of time to kill. We don’t feel like talking, really. It’s, uh, you know, kind of dragging along the query plan. You know, it’s kind of the same, same set of yuckiness.
Like, why does it take five seconds to seek into an index? Like, what, what are you doing? You know, who designs these things? Add an index.
Add a good index. Why is this? This is silly. Uh, and then this one, or three seconds. And, you know, like, like focusing in on the operator times. There’s a thing that’s like, we have big jumps, right? Not like, we don’t want like incremental ones. Incremental ones are boring.
What if that big jump to where we got up there? And, you know, I guess like a six second jump is pretty big, right? 5.7 seconds up to there. Okay. Well, you know, something to think about. Uh, but, you know, kind of, again, following this road, right?
So now we have that same join, I think. Uh, but we have, uh, like a bunch of stuff down here that just sort of takes a lot of, without the, without the spool, we have to do a lot of, the optimizer chooses a much different plan.
Whereas like a bunch of different stuff happens. Right. And, uh, I don’t know. I’m kind of got lost with what I was going to say about this one, but you know, it’s Friday and you’re not paying attention anyway.
So it doesn’t matter all that much. Does this do? No. Outer references. So this time we get a part, we get an apply nested loops where, uh, rather than the regular nested loops where everything happened at the join, where it was with the spool. Now we have, we get an apply nested loops where it takes, uh, the type and the, uh, owner ID and it pushes things down.
If I’m, if I’m looking at the right join, even at this point, I’m, I forget. Anyway, uh, that’s how I tune queries. I forget things. Uh, but yeah. So, uh, what I, what I, what I thought looking at it was, geez, this is a performance problem that I’ve solved for other people.
A lot of them, a whole bunch of times. Why don’t I solve it for myself for once? And so I rewrote the query and I’ll show you what I did in a minute. But now the execution plan, I, this finishes in about seven seconds.
So now everything that’s slow in here is purely Microsoft’s fault. There’s no like really, really big jumps in the query plan. Uh, you know, we still get, have no buffer descriptors was kind of annoying, but not the biggest deal in the world.
But anyway, let’s get onto the rewrite. Uh, all the stuff up here is superfluous. I mean, that’s just like display level stuff. Uh, what really ended up making a difference was, so the first time I was thinking about rewriting this, I thought that I would just do this thing in here and separate out each of the join conditions.
So in this one, I have one explicit join condition with no, uh, this space there. Ugh, I stink. Uh, and then another one with this explicit join condition. So no like and or in crap in here.
And, you know, looking there could cool. And at first I thought that I would do this and I would, uh, just preserve the original join logic, which up here is, uh, from buffer descriptors joined over to allocation units.
But, uh, I found that hitting that buffer descriptors view three times was painful. So I ended up pulling the buffer descriptors part out and just doing one join to it at the end once I had everything else, uh, done out here.
So now the query is just grabbing all this stuff, right? Having fun doing things, pulling out information. And then, uh, when we get outside of that sort of inner query where we, after we just, I just called that X because, uh, I don’t know, Mr. X.
And then, uh, we do our grouping and our ordering out here. And we do all the summing in whatnot up here with our case expressions here where they’re far less dangerous.
And of course, uh, repeating this query with three explicit joins is a lot faster. Now it’s a lot faster for reasons that I’ve talked about in other posts where, you know, if we’re writing a single purpose queries, it’s a lot easier for the optimizer to go, uh, to deal with that and to make an efficient query plan.
Um, I’ll, I’ll probably blog more about that in the future, but for now, that’s what you get because we’re about at the 10 minute mark and I’m about to get my Friday evening started. And, uh, I hope, well, I mean, by the time you see this, it’ll be Tuesday, but that’s your fault.
Anyway, thank you for watching.
Going Further
If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.
sp_PressureDetector now includes statement start and end offsets, in case you want to build plan guides for queries
WhatsUpMemory got a huge performance tuning, which I’ll talk about in tomorrow’s post!
Nothing else here. Happy downloading and analyzing.
Or analysing, if you’re from a refined country.
Thanks for reading!
Going Further
If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.
After my smash hit double diamond post about index tuning, I got a question questioning my assertion that compressed indexes are also compressed in the buffer pool.
Well, this should be quick. A quick question. Eighty hours later.
First, two indexes with no compression:
CREATE INDEX o
ON dbo.Posts
(OwnerUserId);
CREATE INDEX l
ON dbo.Posts
(LastEditorDisplayName);
Now let’s create a couple indexes with compression:
CREATE INDEX o
ON dbo.Posts
(OwnerUserId)
WITH(DATA_COMPRESSION = ROW);
CREATE INDEX l
ON dbo.Posts
(LastEditorDisplayName)
WITH(DATA_COMPRESSION = PAGE);
I’m choosing compression based on what I think would be sensible for the datatypes involved.
For the integer column, I’m using row compression, and for the string column I’m using page compression.
If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.
In this video, I delve into a minor but irritating graphical quirk in SQL Server Management Studio (SSMS) that has been bugging me for some time now. It’s not just an SSMS issue; it seems to manifest in other places within the SQL Server ecosystem as well. The problem arises when looking at execution plans, specifically in parallel operators, where the sorting of properties pane entries becomes erratic and frustratingly out of order—especially when dealing with more than nine threads. I walk through a workaround for this issue, explaining how to navigate around it by opening tooltips and manually selecting rows, but ultimately, I encourage viewers to voice their frustration through Microsoft’s User Voice platform. By voting on the relevant issue, we can collectively push for improvements in SSMS, making it a more pleasant environment for query tuning and analysis.
Full Transcript
Erik Darling. Bum, bum, bum. He still does not own ErikDarling.com because I’m a cheapskate and I don’t want to pay $1,500 for my own name on the internet. That seems a little outlandish to me. Maybe when it gets down under $1,000, I’ll do it. Anyway, it’s now Sunday, apparently, and today’s broadcast is brought to you by Dom Perignon 2009. And this was brought home from the lovely restaurant, per se. This is their wine stopper thing that they gave us for the bottle. I don’t know that they normally give these out, but it’s the coolest thing that I own now. So, if they want to sponsor me in other ways, I would happily take their sponsorship. But anyway, today’s video is about a minor graphical annoyance that I have with SQL Server Management Studio. Now, this is not the only place that you’ll see an oddity like this. There are many other places within SQL Server. Gosh, this smells good.
Oh, this is not the only place that you can do. Within SQL Server Management Studio where you’ll see this. Maybe it happens in Azure Data Studio, too. I don’t know. I refuse to install that. Anyway, here’s what it is. So, we have this query that does dumb things, admittedly dumb things. I know it’s dumb. I know it’s stupid looking. I know it serves absolutely no purpose in what it does. But when we look at the execution plan, more importantly, when we dive into the parallel operators of the execution plan, something aggravating happens. So, let’s go do what we should do as professional query tuners, always.
Open up the properties pane and let’s open this up. Now, completely ignoring the fact that this is the best parallel skew demo ever written. Open up the properties pane. What the hell is going on here? And then down here. Why? Why? Now, the other place where this used to come up is in the wait stats. So, I think it was… Maybe it was… I can’t remember if it was an SSMS version that fixed it or a SQL Server version that fixed it. It would make sense that it was an SSMS version that fixed it.
But who knows? If you looked at the wait stats, the wait stats would be sorted incorrectly, too. So, that actually is apparently fixed now. But, you know, good. But we still have this problem. And this problem persists across… Ah, come on. So, here’s another funny thing. Sometimes when you go to get the properties, the tooltip shows up. And then you can’t get them because the tooltip shows up over the properties.
And who knows where that is? So, what you have to do is be sneaky, a sneaky little rabbit. And you have to get the tooltip and then hit properties and then crack that open and then highlight that row so that we preserve it. We preserve the positioning across all these operators and go click. And look, they all sort wonky.
The sorting is a muck. Well, except on this one where there’s nothing happening because that’s a gather stream. So, if we crack this one open… Well, this one… I mean, at least the skew is gone here. Alright? At least we don’t have the skew here, but we still have this quite unfortunate sorting.
Anyway, if you’re the type of person who doesn’t like that, doesn’t like the way that looks, think that looks foolish, I have a user voice item that you can vote on. You can express your contempt and disdain for the current state of sorting parallel threads above 9, I guess, in SQL Server Management Studio.
And the link will be attached to the YouTube video and probably in the blog post as well. I just haven’t written it yet, but it’s there. So, watch out for that. Vote for it. Help me make SQL Server Management Studio a nicer place to live and do business.
Sorry, I can’t stop staring at this wine stopper. It’s like the neatest contraption. I bet that like… I could just order these from like a restaurant wholesale place. I bet it’s not even like some extra fancy thing.
I’m probably… It’s probably overblown just because it has a fancy restaurant name etched in the top. Anyway, I’m gonna go get back to enjoying the smell of this champagne on yet another rainy day. Thank you for watching. Thank you for hopefully voting on this issue.
And hopefully, democracy will win and we’ll get this whole sorting thing straightened out. Have a nice day.
Going Further
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In this video, I delve into an intriguing aspect of SQL Server query optimization by demonstrating how different database compatibility levels can influence execution plans and performance. I illustrate this concept using two databases: the master database set to compatibility level 150 (SQL Server 2019) and the stack overflow database at compatibility level 140. By running a specific query in each context, I show that the same operation yields different execution plans due to batch mode on rowstore being enabled only in the higher compatibility level. This example highlights how changing the database context can lead to more efficient query execution without altering the actual code or adding hints. I hope this video provides you with valuable insights into leveraging database compatibility levels for optimizing your queries, especially when dealing with mixed workloads across different databases.
Full Transcript
That champagne still smells wonderful. Legally wonderful. Anyway, I want to show you something kind of interesting. And you can interpret this in your own way, and you can implement this in your own way when it might suit you. And it may suit you someday. If you have certain groups of queries that work really well, when they do one thing but not really the same. really well when they do another thing. I’m going to show you exactly what I mean by that. Now, let’s start off by making very, very sure that we are all clear about what context this database, what context, what database context, that champagne may have smelt a little too good, what database context this query is taking place in. Let’s also be quite sure that we understand which compatibility levels of data. these two queries are taking place in across these two databases. The master database is in compatibility level 150 and the stack overflow database is in compatibility level 140. So master is in 2019 where we get all sorts of fancy things if we are on the most enterprising edition possible like batch mode on rowstore and well no, because scalar UDF inlining is both standard and enterprise.
So, I don’t know, there’s some other stuff in 2019 that I suppose is okay too. I’m not sure what though. Every time I install it, my computer just blue screens. I’m kidding. I’m kidding. It’s fine. It’s production ready. Go use it. Go crazy. Go crazy. And just to make extra sure, right? Like I’m not kidding with you. We are in the master database.
And yet, when I run this query to select a count of records from the post table in the stack overflow 2013 database, we get a very particular query plan. Now, if you’ve watched other videos of mine, you would know that this hash match aggregate to implement a global aggregate could only be done via stream aggregate in prior versions of SQL Server. So, with the proliferation of batch mode, this can now be a hash aggregate. We no longer have to use a stream aggregate.
So, this hash match aggregate is taking place in batch mode. That should be a vocal warm-up exercise. I’m going to tell my vocal coach about that. Hash match aggregate. So, this is a batch aggregate. As well as this clustered index scan are taking place in batch mode. So, the hash match aggregate is batched. I said that fast, didn’t I? And that’s interesting because the stack overflow 2013 database is in 140 compat level where batch mode on rowstore should not be possible.
This is, if you look at the storage. Shut up. Emergency. If you look at the storage, this is rowstore. And we’re not doing any tricky stuff like joining to another table with a clustered columnstore index it on it or something to get batch mode happening. This is a natural occurrence within the query. Right? It just happens. Right? It’s nice. It’s cool. So, great. We have this thing happening.
Now, if you come over here and we very, very clearly use the stack overflow database and we reiterate the fact that the stack overflow database is in 140 compat level and we look at masters and we only run the query in the context of the stack overflow database. And we actually get the query plan. Good job, me. We have a different execution plan. Don’t we?
We see that stream aggregate that was only, that had to be used in prior versions or, not in prior versions of SQL Server, just in the context of a rowstore only query. So, that’s that. And you may find this to be an attractive option if you have a group of, let’s say, reporting queries that you can execute from another database context that’s in compatibility level 150 against another database. Maybe that’s turned more transactional in nature in compatibility level 140 so that we don’t have to worry about hinting and changing all sorts of stuff.
We can just change the, we can just execute from a slightly different context and still get all the benefits of the optimization, the compatibility level and the optimizer abilities of the database where the query originates. So, take that as you will, implement it as you must. I hope you learned something. I hope you enjoyed this thankfully much shorter video so my champagne doesn’t get warm. Well, I don’t like the smell of warm champagne, especially on camera.
Thanks for watching.
Going Further
If this is the kind of SQL Server stuff you love learning about, you’ll love my training. Blog readers get 25% off the Everything Bundle — over 100 hours of performance tuning content. Need hands-on help? I offer consulting engagements from targeted investigations to ongoing retainers. Want a quick sanity check before committing to a full engagement? Schedule a call — no commitment required.
