Category: SQL Server

It’s My Blog It’s My Rules

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Since I know this’ll come out Friday, I get to have fun.

I have a feeling most of you know what Candy Crush is. It’s a mindless, infinite thumb scroll replacement.

When you’re playing, Candy Crush will suggest moves to you. It thinks they’re good ideas. Though I don’t know what the algorithm is, it seems to recommend some goofy stuff.

Take this example. It wants me to move the orange piece. It’s not a bad move, but it’s not the best possible move.

Savvy Candy Crush players will see the obvious choice. Make five in a row with the purple pieces, get the cookie thing, cookie thing drops down next to the explodey thing, and when you combine it with the wrapped candy thing, all the purple pieces turn into explodey pieces.

Level over, basically.

But Candy Crush isn’t thinking that far ahead. Neither are missing index requests.

SQL Server Does The Same Thing

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Let’s take this query, for example. It’s very Post-centric.

SELECT   p.OwnerUserId, p.Score, p.Title
FROM     dbo.Comments AS c
JOIN     dbo.Posts AS p
    ON p.OwnerUserId = c.UserId
WHERE    p.PostTypeId = 1
AND      p.ClosedDate >= '2018-06-01'
ORDER BY p.Score DESC;

Right now, the only indexes I have are clustered, and they’re not on any columns that help this query. Sure, they help other things, and having clustered indexes is usually a good idea.

This is the home run use-case for nonclustered indexes. You know. Organized copies of other parts of your data that users might query.

This is such an obvious  move that SQL Server is all like “hey, got a minute?”

This is where things get all jumpy-blinky. Just like in Candy Crush.

Hints-a-Hints

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This is the index SQL Server wants:

CREATE NONCLUSTERED INDEX [<Name of Missing Index, sysname,>]
ON [dbo].[Posts] ([PostTypeId],[ClosedDate])
INCLUDE ([OwnerUserId],[Score],[Title])

Would this be better than no index? Yes.

Is it the best possible index? No. Not by a long shot.

Let’s start with our predicates. SQL Server picked PostTypeId as the leading key column.

SELECT SUM(CASE p.PostTypeId WHEN 1 THEN 1 ELSE 0 END) AS [count_type_one],
       SUM(CASE WHEN p.ClosedDate >= '2018-06-01' THEN 1 ELSE 0 END) AS count_closed_date
FROM dbo.Posts AS p

Is it selective?

Regardless of selectivity, the missing index request mechanism will always put equality predicates first.

What I’m getting at is that the missing index request isn’t as well thought out as a lot of people hope. It’s just one possible index for a query weighted to helping us find data in the where clause.

With a human set of eyes on it, you may discover one or more better possible indexes. You may even discover one on for the Comments table, too.

Other Issues

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There’s also the issue of the included columns it chose. We’re ordering by Score. We’re joining on OwnerUserId.

Those may be helpful as key columns, depending on how much data we end up joining, and how much data we end up sorting.

Complicated Game

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If you don’t have anyone doing regular index tuning, missing index hints are worth following because they’re better than nothing.

They’re like… acceptable. Most of the time.

The big things you have to watch out for are the incredibly wide requests, duplicative requests, and ones that want big string columns.

Thanks for reading!

Going Further

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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.

You Fine And All

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You can read a lot about how indexes might improve queries.

But there are certain query mechanics that indexes don’t solve for, and you can not only get stuck with an index that isn’t really helping, but all the same query problems you were looking to solve.

Understanding how indexes and queries interact together is a fundamental part of query tuning. 

In this post, we’re going to look at some query patterns that indexes don’t do much to fix.

Part Of The Problem

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Indexes don’t care about your queries. They care about storing data in order. 

There are very definite ways that you can encourage queries to use them efficiently, and probably even more definite ways for you to discourage them from using them efficiently.

Even with syntax that seems completely transparent, you can get into trouble.

Take the simple example of two date columns: there’s nothing in your index that tracks how two dates in a row relate to each other.

Nothing tracks how many years, months, weeks, days, hours, minutes, seconds, milliseconds, or whatever magical new unit Extended Events has to make itself less usable.

Heck, it doesn’t even track if one is greater or less than another.

Soggy Flautas

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Ah, heck, let’s stick with Stack Overflow. Let’s even create an index.

CREATE INDEX whatever 
ON dbo.Posts(CreationDate, ClosedDate);

Now let’s look at these super important queries.

    SELECT   COUNT(*) AS records
    FROM     dbo.Posts AS p
    WHERE    p.CreationDate < p.ClosedDate;


    SELECT   COUNT(*) AS records
    FROM     dbo.Posts AS p
    WHERE    p.CreationDate > p.ClosedDate;

How much work will the optimizer think it has to do, here? How many rows will it estimate? How will it treat different queries, well, differently? If you said “it won’t”, you’re a smart cookie. Aside from the “Actual rows”, each plan has the same attributes across.

Treachery

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Neither of these query plans is terrible on its own.

The problem is really in larger plans, where bad decisions like these have their way with other parts of a query plan.

Nasty little lurkers they are, because you expect things to get better when creating indexes and writing SARGable predicates.

Yet for both queries, SQL Server does the same thing, based on the same guesses on the perceived number of rows at play. It’s one of those quirky things — if it’s a data point we care about, then it’s one we should express somehow.

A computed column might work here:

    ALTER TABLE dbo.Posts 
        ADD created_less_closed AS 
            CONVERT(BIT, CASE WHEN CreationDate < ClosedDate 
                              THEN 1
                              WHEN CreationDate > ClosedDate
                              THEN 0
                         END)


    CREATE INDEX apathy 
        ON dbo.Posts (created_less_closed);

Which means we’d have to change our queries a bit, since expression matching has a hard time reasoning this one out:

    SELECT   COUNT(*) AS records
    FROM     dbo.Posts AS p
    WHERE    1 = CONVERT(BIT, CASE WHEN CreationDate < ClosedDate 
                              THEN 1
                              WHEN CreationDate > ClosedDate
                              THEN 0
                         END)
    AND 1 = (SELECT 1);


    SELECT   COUNT(*) AS records
    FROM     dbo.Posts AS p
    WHERE    0 = CONVERT(BIT, CASE WHEN CreationDate < ClosedDate 
                              THEN 1
                              WHEN CreationDate > ClosedDate
                              THEN 0
                         END)
    AND 1 = (SELECT 1);

What Would Be Better?

---

Practically speaking, it isn’t the job of an index (or statistic) to track things like this. We need to have data that represents things that are important to users.

Though neither of these plans is terrible in isolation, bad guesses like these flow all through query plans and can lead to other bad decisions and oddities.

It’s one of those terrible lurkers just waiting to turn an otherwise good query into that thing you regard with absolute loathing every time it shows up in [your favorite monitoring tool].

Knowing this, we can start to design our data to better reflect data points we care about. A likely hero here is a computed column to return some value based on which is greater, or a DATEDIFF of the two columns.

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.

You Fine And All

---

You can read a lot about how indexes might improve queries.

But there are certain query mechanics that indexes don’t solve for, and you can not only get stuck with an index that isn’t really helping, but all the same query problems you were looking to solve.

Understanding how indexes and queries interact together is a fundamental part of query tuning. 

In this post, we’re going to look at some query patterns that indexes don’t do much to fix.

Part Of The Problem

---

Indexes don’t care about your queries. They care about storing data in order. 

There are very definite ways that you can encourage queries to use them efficiently, and probably even more definite ways for you to discourage them from using them efficiently.

Even with syntax that seems completely transparent, you can get into trouble.

Take the simple example of two date columns: there’s nothing in your index that tracks how two dates in a row relate to each other.

Nothing tracks how many years, months, weeks, days, hours, minutes, seconds, milliseconds, or whatever magical new unit Extended Events has to make itself less usable.

Heck, it doesn’t even track if one is greater or less than another.

Soggy Flautas

---

Ah, heck, let’s stick with Stack Overflow. Let’s even create an index.

CREATE INDEX whatever 
ON dbo.Posts(CreationDate, ClosedDate);

Now let’s look at these super important queries.

    SELECT   COUNT(*) AS records
    FROM     dbo.Posts AS p
    WHERE    p.CreationDate < p.ClosedDate;


    SELECT   COUNT(*) AS records
    FROM     dbo.Posts AS p
    WHERE    p.CreationDate > p.ClosedDate;

How much work will the optimizer think it has to do, here? How many rows will it estimate? How will it treat different queries, well, differently? If you said “it won’t”, you’re a smart cookie. Aside from the “Actual rows”, each plan has the same attributes across.

Treachery

---

Neither of these query plans is terrible on its own.

The problem is really in larger plans, where bad decisions like these have their way with other parts of a query plan.

Nasty little lurkers they are, because you expect things to get better when creating indexes and writing SARGable predicates.

Yet for both queries, SQL Server does the same thing, based on the same guesses on the perceived number of rows at play. It’s one of those quirky things — if it’s a data point we care about, then it’s one we should express somehow.

A computed column might work here:

    ALTER TABLE dbo.Posts 
        ADD created_less_closed AS 
            CONVERT(BIT, CASE WHEN CreationDate < ClosedDate 
                              THEN 1
                              WHEN CreationDate > ClosedDate
                              THEN 0
                         END)


    CREATE INDEX apathy 
        ON dbo.Posts (created_less_closed);

Which means we’d have to change our queries a bit, since expression matching has a hard time reasoning this one out:

    SELECT   COUNT(*) AS records
    FROM     dbo.Posts AS p
    WHERE    1 = CONVERT(BIT, CASE WHEN CreationDate < ClosedDate 
                              THEN 1
                              WHEN CreationDate > ClosedDate
                              THEN 0
                         END)
    AND 1 = (SELECT 1);


    SELECT   COUNT(*) AS records
    FROM     dbo.Posts AS p
    WHERE    0 = CONVERT(BIT, CASE WHEN CreationDate < ClosedDate 
                              THEN 1
                              WHEN CreationDate > ClosedDate
                              THEN 0
                         END)
    AND 1 = (SELECT 1);

What Would Be Better?

---

Practically speaking, it isn’t the job of an index (or statistic) to track things like this. We need to have data that represents things that are important to users.

Though neither of these plans is terrible in isolation, bad guesses like these flow all through query plans and can lead to other bad decisions and oddities.

It’s one of those terrible lurkers just waiting to turn an otherwise good query into that thing you regard with absolute loathing every time it shows up in [your favorite monitoring tool].

Knowing this, we can start to design our data to better reflect data points we care about. A likely hero here is a computed column to return some value based on which is greater, or a DATEDIFF of the two columns.

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.

Moved!

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This post has been moved here.

Root cause: This one is haunted.

Enjoy the rest of your (now cursed) day.