sql temp table Archives | Page 103 of 138

A Week In Other Databases: CockroachDB

Posted on May 19, 2022May 5, 2022 by Erik Darling

Cool Cool Cool

A while back, the CMU Database group started hosting a series of database talks that gave the folks behind new and interesting databases a chance to talk about what they’re working on, why they built it, how they’re different from what’s around, and more.

I really enjoyed a bunch of them, and I think you might too, so this week I’m posting my favorite ones.

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.

A Week In Other Databases: Snowflake

Posted on May 18, 2022May 5, 2022 by Erik Darling

Cool Cool Cool

I really enjoyed a bunch of them, and I think you might too, so this week I’m posting my favorite ones.

Thanks for reading!

Going Further

A Week In Other Databases: MongoDB

Posted on May 17, 2022May 5, 2022 by Erik Darling

Cool Cool Cool

I really enjoyed a bunch of them, and I think you might too, so this week I’m posting my favorite ones.

Thanks for reading!

Going Further

A Week In Other Databases: Jepsen.io

Posted on May 16, 2022May 5, 2022 by Erik Darling

Cool Cool Cool

I really enjoyed a bunch of them, and I think you might too, so this week I’m posting my favorite ones.

Thanks for reading!

Going Further

The Building Blocks for Self-Driving PostgreSQL

Posted on May 13, 2022April 29, 2022 by Erik Darling

Grand Bonne

I know what you’re thinking: Who cares about that free database?

Well, it’s not necessarily the Postgres part that you might care about, but more the fact that a third party is developing software to do what major vendors aren’t doing.

This sort of thing might come to SQL Server someday, and it probably should. The self-tuning features in Azure are ass.

Thanks for reading.

Going Further

Indexed Views In SQL Server: No Filtered Indexes Or Filtered Statistics

Posted on May 12, 2022May 16, 2022 by Erik Darling

Half Baked

In my quest to love indexed views more, I’m always trying new things with them to solve problems.

Occasionally, I am pleasantly surprised by what can be accomplished with them. Occasionally.

Today was not an occasion. Let’s take an unfortunate look.

CREATE TABLE
    dbo.IndexedViewMe
(
    id int PRIMARY KEY CLUSTERED
);
GO 

CREATE VIEW 
    dbo.TheIndexedView
WITH SCHEMABINDING
AS
SELECT
    ivm.id
FROM dbo.IndexedViewMe AS ivm;
GO 

CREATE UNIQUE CLUSTERED INDEX
    uqi
ON dbo.TheIndexedView
    (id);

INSERT 
    dbo.IndexedViewMe
(
    id
)
SELECT
    x.c
FROM 
(
    SELECT 1 
      UNION ALL 
    SELECT 2
) AS x(c);

This gives us a tiny little table and indexed view. If we try to do either of these things, it doesn’t go well:

CREATE INDEX 
    i
ON dbo.TheIndexedView
    (id)
WHERE 
    id = 2;

Msg 10610, Level 16, State 1, Line 40

Filtered index ‘i’ cannot be created on object ‘dbo.TheIndexedView’ because it is not a user table. Filtered indexes are only supported on tables.

If you are trying to create a filtered index on a view, consider creating an indexed view with the filter expression incorporated in the view definition.

CREATE STATISTICS 
    s
ON dbo.TheIndexedView
    (id)
WHERE 
    id = 2;

Msg 10623, Level 16, State 1, Line 47

Filtered statistics ‘s’ cannot be created on object ‘dbo.TheIndexedView’ because it is not a user table. Filtered statistics are only supported on user tables.

Sort of a bummer, that. And it strikes me that it’s an odd limitation — especially for the statistics — but what can you do?

Indexed views haven’t changed aside from bug fixes in forever and a day. I doubt there’ll be any real investment in enhancing them anytime soon.

Thanks for reading!

Going Further

Using Views To Reduce Memory Grants In SQL Server

Posted on May 11, 2022May 16, 2022 by Erik Darling

We All Have It

You know those tables, right? The ones where developers went and got lazy or didn’t know any better and decided every string column was going to be gigantic.

They may have read, of course, that SQL Server’s super-smart variable length data types only consume necessary space.

It’s free real estate.

Except it isn’t, especially not when it comes to query memory grants.

The bigger a string column’s defined byte length is, the bigger the optimizer’s memory grant for it will be.

Memory Grant Primer

In case you need some background, the short story version is:

All queries ask for some memory for general execution needs
Sorts, Hashes, and Optimized Nested Loops ask for additional memory grants
Memory grants are decided based on things like number of rows, width of rows, and concurrently executing operators
Memory grants are divided by DOP, not multiplied by DOP
By default, any query can ask for up to 25% of max server memory for a memory grant
Approximately 75% of max server memory is available for memory grants at one

Needless to say, memory grants are very sensitive to misestimates by the optimizer. Going over can be especially painful, because that memory will most often get pulled from the buffer pool, and queries will end up going to disk more.

Underestimates often mean spills to disk, of course. Those are usually less painful, but can of course be a problem when they’re large enough. In particular, hash spills are worth paying extra attention to.

Memory grant feedback does supply some relief under modern query execution models. That’s a nice way of saying probably not what you have going on.

Query Noogies

Getting back to the point: It’s a real pain in the captain’s quarters to modify columns on big tables, even if it’s reducing the size.

SQL Server’s storage engine has to check page values to make sure you’re not gonna lose any data fidelity in the process. That’ a nice way of saying you’re not gonna truncate any strings.

But if you do something cute like run a MAX(LEN(StringCol) and see what you’re up against, you can use a view on top of your table to assuage SQL Server’s concerns about such things.

After all, functions are temporary. Data types are forever (usually).

An easy way to illustrate what I mean is to look at the details of these two queries:

SELECT TOP (1000)
    p.Body
FROM dbo.Posts AS p
ORDER BY p.Score DESC
OPTION(RECOMPILE);

SELECT TOP (1000)
    Body = 
        CONVERT
        (
            nvarchar(100), 
            p.Body
        )
FROM dbo.Posts AS p
ORDER BY p.Score DESC
OPTION(RECOMPILE);

Some of this working is dependent on the query plan, so let’s look at those.

Pink Belly Plans

You can ignore the execution times here. The Body column is not a good representation of an oversized column.

It’s defined as nvarchar(max), but (if I’m remembering my Stack lore correctly) is internally limited to 30k characters. Many questions and answers are longer than 100 characters anyway, but on to the plans!

In the plan where the Body column isn’t converted to a smaller string length, the optimizer asks for a 16GB memory grant, and in the second plan the grant is reduced to ~3.5GB.

This is dependent on the compute scalar occurring prior to the Top N Sort operator, of course. This is where the convert function is applied to the Body column, and why the grant is reduced

If you were to build a view on top of the Posts table with this conversion, you could point queries to the view instead. That would get you the memory grant reduction without the pain of altering the column, or moving the data into a new table with the correct definition.

Thanks for reading!

Going Further

SQL Server Performance Mysteries: Why Does Production Get A Bad Plan That Development Doesn’t?

Posted on May 10, 2022May 16, 2022 by Erik Darling

Statistical Legacy

A client question that I get quite a bit is around why queries in production get a bad query plan that queries in dev, QA, or staging don’t get is typically answered by looking at statistics.

Primarily, it’s because of the cardinality estimates that queries get around ascending keys. It usually gets called the ascending key problem, but the gist is that:

You have a pretty big table
You’re using the legacy cardinality estimator
A whole bunch of rows get inserted, but not enough to trigger an auto stats update
You’re not using compatibility level >= 130 or trace flag 2371
Queries that look for values off an available histogram get a one row estimate using the legacy Cardinality Estimator or a 30% estimate using the default Cardinality Estimator

Which is a recipe for potentially bad query plans.

Reproductive Script

Here’s the full repro script. If you’re using a different Stack Overflow database, you’ll need to adjust the numbers.

USE StackOverflow2013;

/*Figure out the 20% mark for stats updates using legacy compat levels*/
SELECT
    c = COUNT_BIG(*),
    c20 = CEILING(COUNT_BIG(*) * .20)
FROM dbo.Users AS u;

/*Stick that number of rows into a new table*/
SELECT TOP (493143)
    u.*
INTO dbo.Users_Holder
FROM dbo.Users AS u
ORDER BY u.Id DESC;


/*Delete that number of rows from Users*/
WITH 
    del AS
(
SELECT TOP (493143)
    u.*
FROM dbo.Users AS u
ORDER BY u.Id DESC
)
DELETE
FROM del;

/*I'm using this as a shortcut to turn off auto stats updates*/
UPDATE STATISTICS dbo.Users WITH NORECOMPUTE;

/*Put the rows back into the Users Table*/
SET IDENTITY_INSERT dbo.Users ON;

INSERT
    dbo.Users
(
    Id,
    AboutMe,
    Age,
    CreationDate,
    DisplayName,
    DownVotes,
    EmailHash,
    LastAccessDate,
    Location,
    Reputation,
    UpVotes,
    Views,
    WebsiteUrl,
    AccountId
)
SELECT
    uh.Id,
    uh.AboutMe,
    uh.Age,
    uh.CreationDate,
    uh.DisplayName,
    uh.DownVotes,
    uh.EmailHash,
    uh.LastAccessDate,
    uh.Location,
    uh.Reputation,
    uh.UpVotes,
    uh.Views,
    uh.WebsiteUrl,
    uh.AccountId
FROM dbo.Users_Holder AS uh;

SET IDENTITY_INSERT dbo.Users OFF;

/*Figure out the minimum Id we put into the holder table*/
SELECT
    m = MIN(uh.Id)
FROM dbo.Users_Holder AS uh;

/*Compare estimates*/
SELECT
    c = COUNT_BIG(*)
FROM dbo.Users AS u
WHERE u.Id > 2623772
OPTION(USE HINT('FORCE_LEGACY_CARDINALITY_ESTIMATION'));


SELECT
    c = COUNT_BIG(*)
FROM dbo.Users AS u
WHERE u.Id > 2623772
OPTION(USE HINT('FORCE_DEFAULT_CARDINALITY_ESTIMATION'));

/*Cleanup*/
UPDATE STATISTICS dbo.Users;

TRUNCATE TABLE dbo.Users_Holder;

Query Plans

Here are the plans for the stars of our show:

In these query plans, you can see the legacy cardinality estimator gets a one row estimate, and the default cardinality estimator gets a 30% estimate.

There isn’t necessarily a one-is-better-than-the-other answer here, either. There are times when both can cause poor plan choices.

You can think of this scenario as being fairly similar to parameter sniffing, where one plan choice does not fit all executions well.

Checkout

There are a lot of ways that you can go about addressing this.

In some cases, you might be better off using trace flag 2371 to trigger more frequent auto stats updates on larger tables where the ~20% modification counter doesn’t get hit quickly enough. In others, you may want to force one estimator over the other depending on which gets you a better plan for most cases.

Another option is to add hints to the query in question to use the default cardinality estimator (FORCE_DEFAULT_CARDINALITY_ESTIMATION), or to generate quick stats for the index/statistics being used (ENABLE_HIST_AMENDMENT_FOR_ASC_KEYS). Documentation for both of those hints is available here. Along these lines, trace flags 2389, 2390, or 4139 may be useful as well.

Of course, you could also try to address any underlying query or index issues that may additionally contribute to poor plan choices, or just plan differences. A common problem in them is a seek + lookup plan for the one row estimate that doesn’t actually make sense when the actual number of rows and lookup executions are encountered at runtime.

Thanks for reading!

Going Further

CPU & RAM Don’t Lie: Query Metrics I Care About For Tuning

Posted on May 9, 2022January 21, 2026 by Erik Darling

Discarded

There are metrics that I care and don’t care about when I’m looking for queries to tune.

Metrics I don’t care about:

Logical Reads
Costs

If a query does “a lot” of reads or has a high “cost”, I generally don’t care as long as they run quickly. Doing consistent physical reads is a slightly different story, but would probably fall more under server tuning or fixing memory grants.

Metrics I do care about:

CPU (taking parallelism into consideration)
Duration (compared to CPU)
Memory Grants (particularly when they’re not being fully utilized)
Writes (especially if it’s just a select)
Executions (mostly to track down scalar UDFs)

CPU and Duration

These two metrics get lumped together because they need to be compared in order to figure out what’s going on. First, you need to figure out what the minimum runtime of a query is that you want to tune.

In general, as query execution time gets faster, getting it to be much faster gets more difficult.

Bringing a query from 1 second to 100 milliseconds might be a small matter
Bringing that same query from 100 milliseconds to 1 millisecond might take more time than it’s worth

I say that because unless someone is querying SQL Server directly, smaller durations tend to be less detectable to end users. By the time they hit a button, send the request, receive the data, and have the application render it etc. they’re probably not aware of a 99 millisecond difference.

Of course, not everything is end-user centric. Other internal operations, especially any loop processing, might benefit greatly from reductions on the smaller side of things.

If duration and CPU are acceptable, leave it alone
If either is unacceptable, tune the darn thing
If CPU is much higher than duration, you have a parallel plan, and tuning is optional
If duration is much higher than CPU, you have blocking or another contention issue, and the query you’re looking at probably isn’t the problem
If duration and CPU are roughly equivalent, you either have a functional serial plan or a really crappy parallel plan

I give these the highest priority because reducing these is what makes queries faster, and reduces the surface area (execution time) of a query where something crappy might happen, like blocking, or deadlocks, or other resource contention.

Memory Grants

Using these as a tuning metric can have a lot of positive effects, depending on what kind of shape the system is in.

Consider a few scenarios:

PAGEIOLATCH_XX waits are high because large memory grants steal significant buffer pool space
RESOURCE_SEMAPHORE waits are high because queries suck up available memory space and prevent other queries from using it
Queries are getting too low of a memory grant and spilling significantly, which can slow them down and cause tempdb contention under high concurrency

Fixing memory grant issues can take many forms:

Getting better cardinality estimates for better overall grant estimates
Indexing to influence operator choices away from memory consumers
Using more appropriate string lengths to reduce memory grants
Fixing parallel skew issues that leaves some threads with inadequate memory
Rewriting the query to not ask for ordered data
Rewriting the query to ask for ordered data in smaller chunks
Rewriting the query to convert strings to better fitting byte lengths

That’s just some stuff I end up doing off the top of my head. There are probably more, but blog posts are only useful up to a certain length.

Like all other strings.

Writes and Selects

Modification queries are going to do writes. This seems intuitive and not at all shocking. If you have queries that are doing particularly large modifications, you could certainly look into tuning those, but it would be a standard exercise in query or index tuning.

Except that your index tuning adventure would most likely lead you to dropping unused and overlapping indexes to reduce the number of objects that you need to write to than to add an index.

But who knows. Boring anyway. I hear indexes tune themselves in the cloud.

When select queries do a large number of writes, then we’re talking about a much more interesting scenario.

Spills
Spools
Stats updates

Of course, stats updates are likely a pretty small write, but the read portion can certainly halt plan compilation for a good but on big tables.

Spills and Spools are going to be the real target here. If it’s a spill, you may find yourself tracking back to the memory grant section up above.

Spools, though! What interesting little creatures. I wrote a longer post about them here:

Understand Your Plan: Operators That Write Data (Spools, Spools, Spools)

It has a bit of a link roundup of other posts on my site and others that talk about them, too.

But since we’re living in this now, let’s try to be present. Here’s the short story on spools that we might try to fix:

The Spools we typically care about are Table or Index
They can be eager or lazy
They’ll show up on the inner side of Nested Loops
SQL Server uses them as a temporary cache for data
They are a good indicator that something is amok with your query or indexes

For eager index spools, the story is pretty simple around creating a better index for SQL Server to use.

For lazy table spools, you have more options:

Give SQL Server unique data to work with
Get the optimizer to not choose nested loops
Use the NO_PERFORMANCE_SPOOL hint to test the query without spools

Of course, there are times where you’re better off with a spool than without. So don’t walk away feeling disheartened if that’s the case.

Executions

These are on the opposite end of the spectrum from most of the queries I go after. If a query runs enough, and fast enough, to truly rack up a high number of executions, there’s probably not a ton of tuning you could do.

Sure, sometimes there’s an index you could add or a better predicate you could write, but I’d consider it more beneficial to get the query to not run so much.

That might result in:

Rewriting functions as inline table valued functions
Handing the queries off to app developers for caching

To learn how I rewrite functions, check out this video

I know, you can’t rewrite every single function like this, but it’s a wonderful thing to do when you can.

Anything Other Than

Again, metrics I don’t ever look at are logical reads or costs.

Doing reads doesn’t necessarily mean that queries are slow, or that there’s anything you can fix
Costs are a meme metric that should be removed from query plans in favor of operator times

Well, okay, maybe not completely removed, but they shouldn’t be front and center anymore.

There are many other more reliable metrics to consider that are also far more interesting.

Thanks for reading!

Video Summary

In this video, I discuss the challenges and performance implications of using scalar UDFs in SQL Server queries, particularly focusing on a function called “no bueno.” I walk through how to rewrite this function as an inline table-valued function (TVF) to improve performance. By leveraging `GETDATE()` and Common Table Expressions (CTEs), we can avoid the limitations that prevent scalar UDFs from being inlined, leading to more efficient query execution plans. The video also delves into why functions should generally be avoided in WHERE clauses due to their tendency to hinder parallelism and increase execution time. With a bit of humor and personal anecdotes about moving offices to escape stalkers, I aim to make the topic both engaging and informative for viewers who are new or experienced with SQL Server.

Full Transcript

Erik Darling here with Erik Darling Data. And I believe this is take 297 of this video. So I’m sorry if these jokes sound a little bit rehearsed. I’m very tired. Kidding. I’m fine. Everything’s good. This is my first take. This is the first time I’m doing this. Don’t worry about me. I would never make that. I would never do anything bad. Now, I apologize if I look a little bit weird. I have this ring light up over here because my office is for some reason exceptionally dark. I don’t know. I don’t know. I don’t know. So sorry if I look weirder than usual, but I’m used to it. This is actually probably the last video that I record in this office because I have many, many stalkers. I have decided to move my office to a different location. And I can’t disclose the location of this office because, again, I have too many stalkers. I was actually voted by the nice folks at Beer Gut Magazine, the consultant most likely to get murdered by a stalker. So I’m trying to avoid that. I’m going to move my office. Well, it probably shows some internal shots of it in a blog post coming up. But for now, all you need to know is that I am disappearing from this room. I don’t know what the new room is going to, I don’t know what the setup is going to look like yet. But it’s going to lead to grand things like me actually having the will to do live streaming and live classes and stuff again because this office is so small that it drained my will to do those things. So there we have it. Anyway, let’s talk about what this function does. And this function is called no bueno because scalar UDFs are in general no bueno. Now it’s going to take a couple things, a user ID and a start date. And we’re going to sue and it’s going to return a thing, right? This returns an integer. Wowee. And you know what’s funny is like you have an integer, but like on the flip side, all you have is a big int. There’s no like big integer. I think that doesn’t work. So I don’t really understand. I don’t really understand that. But there we go. I don’t know. Blame the summer intern again. But inside the function, we’re going to supercharge it, right? We’re going to give it the schema binding and returns null on null input attributes, right? So make this function go as fast as we can with those. And then inside the function, we’re going to declare a few parameters. I guess that’s four, which is one more than a few. But what do I know? I’m a high school dropout. I don’t know what numbers are called anyway.

So the first thing this function is going to do after declaring those variables is we’re going to see if start date is null. And if it’s null, then we’re just going to subtract a year from get date. And of course, this get date function is what’s going to lead to us needing to rewrite the function because this get date being in here, well, guess what? That’s a limitation of Senor Freud. If we have an intrinsic function like get date in our scalar UDF, function can’t get in line.

Okay, then. I find that especially curious. Actually, there’s two things I find curious on this page. One is that if the get date thing, and two is the CTE thing. You want to know why I find these things curious is because both of these things break scalar UDF inline.

But when we rewrite this function, I mean, spoiler alert, major spoiler alert here. When we rewrite this function as an inline table valued function, we are going to use both get date and a CTE. So it doesn’t make a whole lot of sense why I can’t get done.

So you’ll sort of figure out if you write an inline table valued function, a scalar UDF is like, oh, I’m bad. Hands off. Hands off. Too much going on there. Oops, I clicked on the wrong window. Pretend that didn’t happen. Not re-recording this thing for the 297th time.

And then, okay, so check out that null thing. This is what screws us up. But then we’re going to get the creation date and last access date for whatever user we’re passed in. And then if we greater than or equal to whatever start date we pass in from the function, then we’re also going to get a count of their posts.

So good job us there. And then we are going to, you, set this average post per day return variable, return thing, as the total posts divided by the days between the creation date and last access date. And of course, we’re going to use the wonderful, fabulous, talented null if function to make sure that we don’t hit any divide by zero errors.

Very important defensive T-SQL there, right? T-SQL pro tip. Nerd. So anyway, let’s look at… I assume if you’re watching this channel, you’ve heard me make fun of functions many times before.

But in case we have any newcomers, let’s talk about why we need to get rid of functions a little bit. So first thing is that when you look at the query plan for a query that calls a scalar UDF, one thing that you’re going to see if you are not on…

Well, I guess one thing that you’re going to see if you are not on SQL Server 2019 and you are not getting your functions inlined is this non-parallel plan reason. This cannot generate valid parallel plan.

And of course, if we look at the plan cache, we’re going to see something else interesting. We are going to see this function, right? We see we have our top 10 query and we have this create function thing.

And if we scroll over a little bit, we’re going to see that that query executed once, but that function executed 10 times. So the more rows that have to get passed through that function, the more times that function is going to run, right?

So like if that query is… Like in this case, if we return the top 1,000, that function would have to execute 1,000 times. If we were filtering on that function, like if we were saying select top 10 from users who have more than 20 average posts per day, we would have to pass the entire users table.

Well, that’s 2.4 million rows. We have to pass 2.4 million rows through that function in order to generate a resultant filter on it. That would just be ugly. That would be horrible.

And I don’t want to make you sit through a video where I do that, so I’m not going to do that. Bottom line, don’t put functions in your where clause. Actually, yeah, just don’t put functions in your where clause. That’s it. But of course, the real performance hit from this function doesn’t really come from what the function itself does.

The function itself is pretty fast when it runs on its own. There’s a bike gang outside. This might be more of my stalkers coming to get me. But thanks, bike gang. Bike sounds like farts.

Doesn’t sound like a tough bike at all. But anyway, where functions really start to hurt queries is when they prevent a larger query that could and should go parallel from going parallel using multiple chords.

They force that query to run single-threaded, so that query just runs for a longer time, just having one thread have to deal with a whole bunch of rows. You can see now this thing, even though it still returns 10 rows, and we found a bunch of really low-impact people, zero rows per day.

This thing all runs for 11 and a half seconds. And of course, our scalar UDF forces the query to run serially. And if we went and looked at the plan cache again, we’d probably see 20 executions of that function now because we did another top 10.

But that’s about all the times that I want to go look at the plan cache right now. So we’re not going to do that again. I don’t need to prove that to you twice. You can figure it out on your own. You’re a smart person.

You’re capable. Everyone loves you. So let’s rewrite this function using two constructs that force inlining to not work. Let’s use the getDate function, and let’s use CTE.

So now, you don’t necessarily need to do this. And I totally cop to the fact that, you know, you could probably rewrite this using fewer CTE or, like, you know, move things around a little bit.

But when I’m rewriting a function, I like to rewrite things in exactly the order that the scalar UDF does them in so that when I’m reviewing the logic, if I, like, run something and I see, oh, that’s different results, then I can compare apples to apples, like, the steps that I’m taking, and I can figure out where the problem is, right?

So the first thing, the first CTE is going to do what the last, the first procedural bit of logic and the other function did, which is set start date to a year ago if start date is null, right?

So that’s the first thing we’re going to do. Then down here in the user dates CTE, we’re going to do exactly what we did before. We’re going to get the creation date and the last access date for the user ID, right? And we’re going to have to cross join that start date CTE, which is fine because it’s only ever going to return one row, and then filter on that S.startDate column.

The next thing we’re going to do is go get the total posts, right? So we’re going to go select count from posts in here, and we’re going to go get all the posts for that user.

And then we’re just going, the final thing that we’re going to do, we’re all done with the CTE, is we’re going to get the average posts per day, which is the total posts from the table above. And we’re going to null if, to have some divide by zero protection.

We’re going to take the creation date and the last access date from above, and then we’re going to select from users. And again, we’re going to cross join here, but again, the cross join is okay because total posts is only ever going to be one row, and the result is only ever going to be one row.

So we’re all right there, right? So let’s create this function. Create or alter, I suppose. And now let’s double check our work. So one thing that’s very important to do whenever you’re rewriting code like this is make sure that it’s logically equivalent.

So let’s take the top 100 people from the post table, and let’s just spot check to make sure that we’re returning the same rows or the same average posts per day for all of these.

And just a quick spot check there looks pretty good. So I assume that I nailed the logic on that. It wasn’t an overly complex query, so we’re good there. And now let’s look at the results and make sure that we return the correct results from both of these, right?

So we’re going to run these. And one thing that you might notice is that the inline table-valued function version, right? Now, one thing that’s very important is that how we call the inline table-valued function is a little bit different than how we call the scalar UDF.

Since it’s returning a table, we have to put it into sort of a subquery-looking thing like this, where we see the average post per day is equal to open paren, select from the inline table-valued function, passing in those same two columns, close paren, and all that good stuff.

And then one thing that we’re going to notice here is that the inline table-valued function plan sure runs a lot faster, right? That finishes in 3.1 seconds. And, of course, the scalar UDF plan still, well, actually, that was 12.5 seconds that time.

I don’t know where we got that extra second from after the last execution, but, wow, that’s scary stuff, isn’t it? But, you know, I think the main difference if we were going to look at these two queries, I mean, aside from the fact that the plan shapes might be a tiny bit different, or maybe a lot bit different, is that this query up here was allowed to go parallel.

We can see all sorts of parallel operators in this plan where they didn’t exist in this plan. And that is, of course, one of the things that sets this query that has to sort of process a lot of rows to get down to that top 10.

It sets it free so that it can go and be fast and all that other stuff. So, anyway, that’s it for me. I’m going to go get some food now because I am starving.

It’s 2 in the afternoon. I haven’t eaten all day. I know it’s hard to believe. I think I just wake up and snort caviar, and I wish someday. Someday I’m going to wake up and snort caviar.

I learned somewhat recently that the best way to eat caviar is off the little space between your thumb and finger. The skin contact is supposed to do something. So, I don’t know.

I don’t know that I have refined enough a palette to taste the difference, but I’m willing to do what the cool kids tell me to do so that I fit in, which doesn’t explain why I’m doing SQL Server stuff at all.

I got some thinking to do. Boy. Anyway, thanks for watching. I hope you learned something.

I hope you enjoyed yourself. I’m going to be doing some more stuff like this and be talking about getting around some of those limitations that we looked at in Freud, different ways you can rewrite functions to sort of get around those.

And, I don’t know. Goodbye. Thanks. Again. Bye.

Going Further

How I Learned To Hate SQL Server’s Plan Cache

Posted on May 6, 2022May 16, 2022 by Erik Darling

Used To Love Her

I used to get so excited about looking in the plan cache, and writing all sorts wacky XML queries to parse plans and dig for goodies.

Then I started to ask some tough questions about it, like:

How many plans are in here?
What’s the oldest one?
How many duplicate plans are there?
Where’s the parameter sniffing details?
Why is optimize for adhoc workloads the worst setting that everyone said is a best practice for eons?

As I wrote queries to look at a lot of these things, stuff got… weird. And disappointing.

What’s In There?

The plan cache has limits for how many plans it’ll keep, and how big of a cache it’ll keep. Even if there’s a lot of plans, there’s no guarantee that they’re older than a few hours.

You may even find that simple parameterization makes things confusing, and that things get cleared out at inopportune times.

One situation I’ve run into well-more than once is the plan cache getting cleared out due to query memory pressure, and then any chance of figuring out which queries were responsible disappears along with it.

Memory is important, someone once said.

On top of that, a lot of SQL Server consumers have way too much faith in bunk metrics, like query and operator costs. Sure, there are plenty of corroborating views to get resource usage metrics, but if all you’ve got is a query plan, all you’ve got is a bunch of nonsense costs to tweedle yourself with.

Sniff Sniff Pass

Another big miss with the plan cache is that it is almost no help whatsoever with parameter sniffing.

Sure, you can sort of figure out based on wide variances in various metrics if a plan sometimes does way more work or runs way longer than other times, but… You still just see the one plan, and its compile values. You don’t see what the plan was, or could be.

Or should be, but that’s a story for another day.

This is where Query Store is awesome, of course! You can see regressions and all that jazz.

But in the plan cache, boy howdy, you get a whole lotta nothing. No Rosie at all.

And this is why I hate the plan cache.

Thanks for reading!