Erik Darling, Author at Darling Data

When Does Scalar UDF Inlining Work In SQL Server?

Posted on December 15, 2020May 15, 2022 by Erik Darling

The Eye

UPDATE: After writing this and finding the results fishy, I reported the behavior described below in “Somewhat Surprising” and “Reciprocal?” and it was confirmed a defect in SQL Server 2019 CU8, though I haven’t tested earlier CUs to see how far back it goes. If you’re experiencing this behavior, you’ll have to disable UDF inlining in another way, until CU releases resume in the New Year.

With SQL Server 2019, UDF inlining promises to, as best it can, inline all those awful scalar UDFs that have been haunting your database for ages and making queries perform terribly.

But on top of the long list of restrictions, there are a number of other things that might inhibit it from kicking in.

For example, there’s a database scoped configuration:

ALTER DATABASE SCOPED CONFIGURATION SET TSQL_SCALAR_UDF_INLINING = ON/OFF; --Toggle this

SELECT 
    dsc.*
FROM sys.database_scoped_configurations AS dsc
WHERE dsc.name = N'TSQL_SCALAR_UDF_INLINING';

There’s a function characteristic you can use to turn them off:

CREATE OR ALTER FUNCTION dbo.whatever()
RETURNS something
WITH INLINE = ON/OFF --Toggle this
GO

And your function may or not even be eligible:

SELECT 
    OBJECT_NAME(sm.object_id) AS object_name,
    sm.is_inlineable
FROM sys.sql_modules AS sm
JOIN sys.all_objects AS ao
    ON sm.object_id = ao.object_id
WHERE ao.type = 'FN';

Somewhat Surprising

One thing that caught me off guard was that having the database in compatibility level 140, but running the query in compatibility level 150 also nixed the dickens out of it.

DBCC FREEPROCCACHE;
GO 

ALTER DATABASE StackOverflow2013 SET COMPATIBILITY_LEVEL = 140;
GO 

WITH Comments AS 
(
    SELECT
        dbo.serializer(1) AS udf, --a function
        ROW_NUMBER() 
            OVER(ORDER BY 
                     c.CreationDate) AS n
    FROM dbo.Comments AS c
)
SELECT 
    c.*
FROM Comments AS c
WHERE c.n BETWEEN 1 AND 100
OPTION(USE HINT('QUERY_OPTIMIZER_COMPATIBILITY_LEVEL_150'), MAXDOP 8);
GO

Our query has all the hallmarks of one that has been inflicted with functions:

SQL Server Query Plan — it can’t go parallel

And if you’re on SQL Server 2016+, you can see that it executes once per row:

SELECT 
    OBJECT_NAME(defs.object_id) AS object_name,
    defs.execution_count,
    defs.total_worker_time,
    defs.total_physical_reads,
    defs.total_logical_writes,
    defs.total_logical_reads,
    defs.total_elapsed_time
FROM sys.dm_exec_function_stats AS defs;

Reciprocal?

There’s an odd contradiction here, though. If we repeat the experiment setting the database compatibility level to 150, but running the query in compatibility level 140, the function is inlined.

DBCC FREEPROCCACHE;
GO 

ALTER DATABASE StackOverflow2013 SET COMPATIBILITY_LEVEL = 150;
GO 

WITH Comments AS 
(
    SELECT
        dbo.serializer(c.Id) AS udf,
        ROW_NUMBER() 
            OVER(ORDER BY 
                     c.CreationDate) AS n
    FROM dbo.Comments AS c
)
SELECT 
    c.*
FROM Comments AS c
WHERE c.n BETWEEN 1 AND 100
OPTION(USE HINT('QUERY_OPTIMIZER_COMPATIBILITY_LEVEL_140'), MAXDOP 8);
GO

Rather than seeing a non-parallel plan, and non-parallel plan reason, we see a parallel plan, and an attribute telling us that a UDF has been inlined.

And if we re-check the dm_exec_function_stats DMV, it will have no entries. That seems more than a little bit weird to me, but hey.

I’m just a lowly consultant on SSMS 18.6

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.

Announcing The Darling Data Vantablack Friday Sale!

Posted on December 14, 2020May 16, 2022 by Erik Darling

A Word From Our Sponsors

First, a huge thank you to everyone who has bought my training so far. You all are incredible, and I owe all of you a drink.

Your support means a lot to me, and allows me to do nice stuff for other people, like give training away for free.

So far, I’ve donated $45k (!!!) worth of training to folks in need, no questions asked.

Next year, I’d like to keep doing the same thing. I’d also like to produce a whole lot more training to add value to the money you spend. In order to do that, I need to take time off from consulting, which isn’t easy to do. I’m not crying poor, but saying no to work for chunks of time isn’t easy for a one-person party.

I’m hoping that I can make enough in training bucks to make that possible.

Because this sale is extra sale-y, I’ve decided to name it after the blackest black known to man.

Going Further

Annoyances When Indexing For Windowing Functions In SQL Server

Posted on December 11, 2020May 15, 2022 by Erik Darling

One Day

I will be able to not care about this sort of thing. But for now, here we are, having to write multiple blogs in a day to cover a potpourri of grievances.

Let’s get right to it!

First, without a where clause, the optimizer doesn’t think that an index could improve one single, solitary metric about this query. We humans know better, though.

WITH Votes AS 
(
    SELECT
        v.Id,
        ROW_NUMBER() 
            OVER(PARTITION BY 
                     v.PostId 
                 ORDER BY 
                     v.CreationDate) AS n
    FROM dbo.Votes AS v
)
SELECT *
FROM Votes AS v
WHERE v.n = 0;

The tough part of this plan will be putting data in order to suit the Partition By, and then the Order By, in the windowing function.

Without any other clauses against columns in the Votes table, there are no additional considerations.

Two Day

What often happens is that someone wants to add an index to help the windowing function along, so they follow some basic guidelines they found on the internet.

What they end up with is an index on the Partition By, Order By, and then Covering any additional columns. In this case there’s no additional Covering Considerations, so we can just do this:

CREATE INDEX v2 ON dbo.Votes(PostId, CreationDate);

If you’ve been following my blog, you’ll know that indexes put data in order, and that with this index you can avoid needing to physically sort data.

Three Day

The trouble here is that, even though we have Cost Threshold For Parallelism (CTFP) set to 50, and the plan costs around 195 Query Bucks, it doesn’t go parallel.

Creating the index shaves about 10 seconds off the ordeal, but now we’re stuck with this serial calamity, and… forcing it parallel doesn’t help.

Our old nemesis, repartition streams, is back.

Even at DOP 8, we only end up about 2 seconds faster. That’s not a great use of parallelism, and the whole problem sits in the repartition streams.

This is, just like we talked about yesterday, a row mode problem. And just like we talked about the day before that, windowing functions generally do benefit from batch mode.

Thanks for reading!

Going Further

An Overlooked Benefit Of Batch Mode For Parallel Query Plans In SQL Server

Posted on December 10, 2020May 15, 2022 by Erik Darling

Make It Count

When queries go parallel, you want them to be fast. Sometimes they are, and it’s great.

Other times they’re slow, and you end up staring helplessly at a repartition streams operator.

Sometimes you can reduce the problem with higher DOP hints, or better indexing, but overall it’s a crappy situation.

Snap To

Let’s admire a couple familiar looking queries, because that’s been working really well for us so far.

WITH Comments AS 
(
    SELECT
        ROW_NUMBER() 
            OVER(PARTITION BY 
                     c.UserId
                 ORDER BY 
                     c.CreationDate) AS n
    FROM dbo.Comments AS c
)
SELECT *
FROM Comments AS c
WHERE c.n = 0
OPTION(USE HINT('QUERY_OPTIMIZER_COMPATIBILITY_LEVEL_140'));

WITH Comments AS 
(
    SELECT
        ROW_NUMBER() 
            OVER(PARTITION BY 
                     c.UserId
                 ORDER BY 
                     c.CreationDate) AS n
    FROM dbo.Comments AS c
)
SELECT *
FROM Comments AS c
WHERE c.n = 0
OPTION(USE HINT('QUERY_OPTIMIZER_COMPATIBILITY_LEVEL_150'));

One is going to run in compatibility level 140, the other in 150, as foretold by ancient alien prophecy.

The two query plans will have a bit in common, but…

The second query, which runs in batch mode, runs about 15 seconds faster. One big reason why is that we skip that most unfortunate repartition streams operator.

It’s a cold sore. An actual factual cold sore.

The only ways I’ve found to fix it completely are:

Induce batch mode
Use the parallel apply technique

But the parallel apply technique doesn’t help much here, because of local factors.

In this case, me generating the largest possible result set and then filtering it down to nothing at the end.

Thanks for reading!

Going Further

An Overlooked Benefit Of Batch Mode With Windowing Functions In SQL Server

Posted on December 9, 2020May 15, 2022 by Erik Darling

Lavender

If you ask people who tune queries why batch mode is often much more efficient with windowing functions, they’ll tell you about the window aggregate operator.

That’s all well and good, but there’s another, often sneaky limitation of fully row mode execution plans with windowing functions.

Let’s go take a look!

Global Aggregates

One thing that causes an early serial zone in execution plans is if you use a windowing function that only has the order by

For example, let’s look at the plans for these two queries:

WITH Comments AS 
(
    SELECT
        ROW_NUMBER() 
            OVER(ORDER BY 
                     c.CreationDate) AS n
    FROM dbo.Comments AS c
)
SELECT *
FROM Comments AS c
WHERE c.n = 0;

WITH Comments AS 
(
    SELECT
        ROW_NUMBER() 
            OVER(PARTITION BY 
                     c.UserId
                 ORDER BY 
                     c.CreationDate) AS n
    FROM dbo.Comments AS c
)
SELECT *
FROM Comments AS c
WHERE c.n = 0;

The resulting estimated plans look like this, using the 140 compatibility level:

In the top plan, where the windowing function only has an order by, the serial zone happens immediately before the Segment operator. In the second plan, the parallel zone carries on until right before the select operator.

If you’re wondering why we’re only looking at estimated plans here, it’s because repartition streams ruins everything.

In The Year 2000

In compatibility level 150, things change a bit (yes, a window aggregate appears):

And the window aggregate appears within the parallel zone. The parallel zone does end before the filter operator, which may or may not be a disaster depending on how restrictive your filter is, and how many rows end up at it.

Also note the distinct lack of a repartition streams operator ruining everything. We’ll talk about that tomorrow.

Thanks for reading!

Going Further

Batch Mode On Row Store vs Batch Mode Tricks For Performance Tuning SQL Server Queries

Posted on December 8, 2020May 15, 2022 by Erik Darling

Quiet Time

I think Batch Mode is quite spiffy for the right kind of query, but up until SQL Server 2019, we had to play some tricks to get it:

Do a funny join to an empty table with a column store index
Create a filtered column store index with no data in it

If you’re on SQL server 2019 Enterprise Edition, and you’ve got your database in compatibility level 150, you may heuristically receive Batch Mode without those tricks.

One important difference between Batch Mode Tricks™ and Batch Mode On Rowstore (BMOR) is that the latter allows you to read from row mode tables using Batch Mode, while the former doesn’t.

Tricks have limits, apparently.

Squish Squish

To cut down on typing, I’ll often create a helper object like this:

CREATE TABLE dbo.t
(
    id int NULL,
    INDEX c CLUSTERED COLUMNSTORE
);

If you read this post, you’ll understand more why.

Now, let’s compare these two queries:

SELECT 
    p.OwnerUserId,
    COUNT_BIG(*) AS records
FROM dbo.Posts AS p
LEFT JOIN dbo.t
    ON 1 = 0
WHERE p.Score < 50
GROUP BY p.OwnerUserId
HAVING COUNT_BIG(*) > 2147483647
OPTION(USE HINT('QUERY_OPTIMIZER_COMPATIBILITY_LEVEL_140'), MAXDOP 8);

SELECT 
    p.OwnerUserId,
    COUNT_BIG(*) AS records
FROM dbo.Posts AS p
WHERE p.Score < 50
GROUP BY p.OwnerUserId
HAVING COUNT_BIG(*) > 2147483647
OPTION(USE HINT('QUERY_OPTIMIZER_COMPATIBILITY_LEVEL_150'), MAXDOP 8);

One executes in compatibility level 140, the other in 150.

Splish Splash

There are a couple interesting things, here.

Even though both queries have operators that execute in Batch Mode (Filter, Hash Match), only the second query can read from the row store clustered index in Batch Mode. In this case, that shaves a couple hundred milliseconds off the seek.

There is likely some additional invisible benefit to not having to convert the row mode seek to a batch mode hash join at the next operator, since one executes for 501ms, and the other executes for 278ms. There’s nothing in the query plan to signal that happening, so you’ll just have to use your imagination.

Thanks for reading!

Going Further

Announcing The Darling Data Vantablack Friday Sale!

Posted on December 7, 2020May 16, 2022 by Erik Darling

A Word From Our Sponsors

First, a huge thank you to everyone who has bought my training so far. You all are incredible, and I owe all of you a drink.

Your support means a lot to me, and allows me to do nice stuff for other people, like give training away for free.

So far, I’ve donated $45k (!!!) worth of training to folks in need, no questions asked.

I’m hoping that I can make enough in training bucks to make that possible.

Because this sale is extra sale-y, I’ve decided to name it after the blackest black known to man.

Going Further

Making The Most Of Temp Tables Part 4: Batch Mode

Posted on December 4, 2020May 16, 2022 by Erik Darling

Le Big Zoom Zoom

When you have queries that need to process a lot of data, and probably do some aggregations over that lot-of-data, batch mode is usually the thing you want.

Originally introduced to accompany column store indexes, it works by allowing CPUs to apply instructions to up to 900 rows at a time.

It’s a great thing to have in your corner when you’re tuning queries that do a lot of work, especially if you find yourself dealing with pesky parallel exchanges.

Oh, Yeah

One way to get that to happen is to use a temp table with a column store index on it.

SELECT 
    v.UserId, 
    SUM(v.BountyAmount) AS SumBounty
FROM dbo.Comments AS c
JOIN dbo.Votes AS v
    ON  v.PostId = c.PostId
    AND v.UserId = c.UserId
GROUP BY v.UserId
ORDER BY SumBounty DESC;

CREATE TABLE #t(id INT, INDEX c CLUSTERED COLUMNSTORE);

SELECT 
    v.UserId, 
    SUM(v.BountyAmount) AS SumBounty
FROM dbo.Comments AS c
JOIN dbo.Votes AS v
    ON  v.PostId = c.PostId
    AND v.UserId = c.UserId
LEFT JOIN #t AS t 
    ON 1 = 0
GROUP BY v.UserId
ORDER BY SumBounty DESC;

Keep in mind, this trick won’t work if you’re on SQL Server 2019 and using in memory tempdb. But aside from that, you’re free to rock and roll with it.

If you end up using this enough, you may just wanna create a real table to use, anyway.

Remarkable!

If we look at the end (or beginning, depending on how you read your query plans) just to see the final times, there’s a pretty solid difference.

The first query takes around 10 seconds, and the second query takes around 4 seconds. That’s a pretty handsome improvement without touching anything else.

Thanks for reading!

Going Further

Making The Most Of Temp Tables Part 3: More Opportune Indexes To Make Queries Go Faster

Posted on December 3, 2020May 16, 2022 by Erik Darling

I Know You

You have too many indexes on too many tables already, and the thought of adding more fills you with a dread that has a first, middle, last, and even a confirmation name.

This is another place where temp tables can save your bacon, because as soon as the query is done they basically disappear.

Forever. Goodbye.

Off to buy a pack of smokes.

That Yesterday

In yesterday’s post, we looked at how a temp table can help you materialize an expression that would otherwise be awkward to join on.

If we take that same query, we can see how using the temp table simplifies indexing.

SELECT
    p.OwnerUserId,
    SUM(p.Score) AS TotalScore,
    COUNT_BIG(*) AS records,
    CASE WHEN p.PostTypeId = 1 
         THEN p.OwnerUserId
         WHEN p.PostTypeId = 2
         THEN p.LastEditorUserId
    END AS JoinKey
INTO #Posts
FROM dbo.Posts AS p
WHERE p.PostTypeId IN (1, 2)
AND   p.Score > 100
GROUP BY CASE
             WHEN p.PostTypeId = 1 
             THEN p.OwnerUserId
             WHEN p.PostTypeId = 2 
             THEN p.LastEditorUserId
         END,
         p.OwnerUserId;

CREATE CLUSTERED INDEX c ON #Posts(JoinKey);

SELECT *
FROM #Posts AS p
WHERE EXISTS
(
    SELECT 1/0
    FROM dbo.Users AS u
    WHERE p.JoinKey = u.Id
);

Rather than have to worry about how to handle a bunch of columns across the where and join and select, we can just stick a clustered index on the one column we care about doing anything relational with to get the final result.

Thanks for reading!

Going Further

Making The Most Of SQL Server Temporary Tables Part 2: Materializing Expressions

Posted on December 2, 2020May 16, 2022 by Erik Darling

Bad Data

A lot of the time when I see queries that are written with all sorts of gymnastics in the join or where clause and I ask some questions about it, people usually start complaining about the design of the table.

That’s fine, but when I ask about changing the design, everyone gets quiet. Normalizing tables, especially for Applications Of A Certain Age™ can be a tremendously painful project. This is why it’s worth it to get things right the first time. Simple!

Rather than make someone re-design their schema in front of me, often times a temp table is a good workaround.

Egg Splat

Let’s say we have a query that looks like this. Before you laugh, and you have every right to laugh, keep in mind that I see queries like this all the time.

They don’t have to be this weird to qualify. You can try this if you have functions like ISNULL, SUBSTRING, REPLACE, or whatever in joins and where clauses, too.

SELECT
    p.OwnerUserId,
    SUM(p.Score) AS TotalScore,
    COUNT_BIG(*) AS records
FROM dbo.Users AS u
JOIN dbo.Posts AS p
    ON u.Id = CASE 
                   WHEN p.PostTypeId = 1 
                   THEN p.OwnerUserId
                   WHEN p.PostTypeId = 2
                   THEN p.LastEditorUserId
              END
WHERE p.PostTypeId IN (1, 2)
AND   p.Score > 100
GROUP BY p.OwnerUserId;

There’s not a great way to index for this, and sure, we could rewrite it as a UNION ALL, but then we’d have two queries to index for.

Sometimes getting people to add indexes is hard, too.

People are weird. All day weird.

Steak Splat

You can replace it with a query like this, which also allows you to index a single column in a temp table to do your correlation.

SELECT
    p.OwnerUserId,
    SUM(p.Score) AS TotalScore,
    COUNT_BIG(*) AS records,
    CASE WHEN p.PostTypeId = 1 
         THEN p.OwnerUserId
         WHEN p.PostTypeId = 2
         THEN p.LastEditorUserId
    END AS JoinKey
INTO #Posts
FROM dbo.Posts AS p
WHERE p.PostTypeId IN (1, 2)
AND   p.Score > 100
GROUP BY CASE
             WHEN p.PostTypeId = 1 
             THEN p.OwnerUserId
             WHEN p.PostTypeId = 2 
             THEN p.LastEditorUserId
         END,
         p.OwnerUserId;

SELECT *
FROM #Posts AS p
WHERE EXISTS
(
    SELECT 1/0
    FROM dbo.Users AS u
    WHERE p.JoinKey = u.Id
);

Remember that temp tables are like a second chance to get schema right. Don’t waste those precious chances.

Thanks for reading!