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Dodo

We’ve got a set of missing index requests for a single table, and we’ve got the queries asking for them.

Going back to our queries and our index requests, all the queries have two things in common:

• They filter on OwnerUserId
• They order by Score

There are of course other elements in the where clause to attend to, but our job is to come up with one index that helps all of our queries.

Query Real Hard

To recap, these are our queries.

SELECT TOP (10)
    p.*
FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
ORDER BY p.Score DESC;
GO 10

SELECT TOP (10) 
    p.*
FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.CreationDate >= '20130101'
ORDER BY p.Score DESC;

SELECT TOP (10)
    p.*
FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.PostTypeId = 1
ORDER BY p.Score DESC;

SELECT TOP (10)
    p.*
FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.LastActivityDate >= '20130101'
ORDER BY p.Score DESC;

SELECT TOP (10) 
    p.*
FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.Score > 0
ORDER BY p.Score DESC;

Index Real Dumb

Which means that all of our missing index requests are going to be on maybe a couple key columns, and then include every other column in the Posts table.

This is a bad idea, so we’re going to dismiss the includes and focus on keys.

CREATE INDEX [OwnerUserId_LastActivityDate_Includes] 
    ON [StackOverflow2013].[dbo].[Posts] ([OwnerUserId], [LastActivityDate]);

CREATE INDEX [OwnerUserId_Score_Includes] 
    ON [StackOverflow2013].[dbo].[Posts] ([OwnerUserId], [Score]);

CREATE INDEX [OwnerUserId_PostTypeId_Includes] 
    ON [StackOverflow2013].[dbo].[Posts] ([OwnerUserId], [PostTypeId]);

CREATE INDEX [OwnerUserId_CreationDate_Includes] 
    ON [StackOverflow2013].[dbo].[Posts] ([OwnerUserId], [CreationDate]);

CREATE INDEX [OwnerUserId_Includes] 
    ON [StackOverflow2013].[dbo].[Posts] ([OwnerUserId]);

Now that we’ve got a more sane bunch of requests to focus on, let’s do something thinking.

I hate thinking, so we won’t do a lot of it.

Indexes put data in order, and equality predicates preserve ordering of secondary index columns. That makes putting the key on (OwnerUserId, Score) a no-brainer. One could make an entire career out of avoiding sorting in the database.

But now we have three other columns to think about: LastActivityDate, PostTypeId, and CreationDate.

We could spend a whole lot of time trying to figure out the best order here, considering things like: equality predicates vs inequality predicates, and selectivity, etc.

But what good would it do?

Dirty Secret

No matter what order we might put index key columns in after Score, it won’t matter. Most of our queries don’t search on OwnerUserId and then Score. Only one of them does, and it doesn’t search on anything else.

That means that most of the time, we’d be seeking to OwnerUserId, and then performing residual predicates against other columns we’re searching on.

On top of that, we’d have whatever overhead there is of keeping things in order when we modify data in the key of the index. Not that included columns are free-of-charge to modify, but you get my point. There’s no order preserved in them.

In reality, a good-enough-index for the good-enough-optimizer to come up with a good-enough-plan looks like this:

CREATE INDEX good_enough
    ON dbo.Posts
        (OwnerUserId, Score)
    INCLUDE 
        (PostTypeId, CreationDate, LastActivityDate);

Planama

The index above does two things:

• It helps us search on a selective predicate on OwnerUserId
• It keeps Score in order after the quality so the order by is free
• It has all the other potential filtering elements so we can apply predicates locally
• It teaches us that include column order doesn’t matter

All of the query plans will look roughly like this, regardless of the where clause:

What Difference Does It Make?

Alright, so we’ve got one good-enough index for a bunch of different queries. By adding the index, we got all of them to go from taking ~600ms to taking 0ms.

What else did we do?

• We made them faster without going parallel
• They no longer need memory to sort data

And we did it without creating a gigantic covering index.

Of course, the optimizer still thinks we need indexes…

But do we really need them?

No.

77% of nothing is nothing.

Thanks for reading!

Deedoo

In yesterday’s post, we talked a little about different ways to approach looking at missing index requests, and how their benefit is calculated in sp_BlitzIndex.

If you’re on SQL Server 2019, you may be able to get some idea which queries are generating missing index requests. It’s not documented yet 🤔, but that’s never stopped anyone from using anything in production.

Let’s look at the queries asking for them.

Natural Ruckus

Let’s take a look at what queries are causing those missing index requests.

Since I’m running them, I don’t have to do any work.

SELECT TOP (10)
    p.*
FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
ORDER BY p.Score DESC;

SELECT TOP (10) 
    p.*
FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.CreationDate >= '20130101'
ORDER BY p.Score DESC;

SELECT TOP (10)
    p.*
FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.PostTypeId = 1
ORDER BY p.Score DESC;

SELECT TOP (10)
    p.*
FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.LastActivityDate >= '20130101'
ORDER BY p.Score DESC;

SELECT TOP (10) 
    p.*
FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.Score > 0
ORDER BY p.Score DESC;

Without any helpful indexes, all of these take about the same amount of time — between 600 and 700ms, and most of the time is spent scanning the clustered index on Posts, which is around 17 million rows, just to return ~27k rows..

That’s Not “Slow”

Usually when people are complaining about slow queries, they’re talking about stuff that drags on for several seconds or longer.

At just about half a second, most people wouldn’t getting a running start to jump through hoops to make these faster.

But they all have a “high cost” of a touch over 3000 query bucks. If you’re the type of person who only focuses on one metric, you might be overlooking a whole lot of things that need attention.

If we made this query 99% faster, it would mean a rather miniscule improvement in elapsed time. The flip side of the one-metric thing is using duration alone as a metric. Queries might have a very long duration because they’re blocked, but use minimal resource when they’re finally allowed to run by the gods of ACID compliance.

My favorite queries to find and tune are ones that:

• Unnecessarily run for a long time, using a lot of CPU
• Unnecessarily ask for large memory grants
• Have issues with parameter sniffing

Let’s pretend there might be some value to tuning these, though. Maybe we’re upset that they’re going parallel. Maybe we have something against scanning clustered indexes. Maybe we just don’t have anything else to do.

In tomorrow’s post, we’ll look at how to take all of those requests and come up with one good enough-index for our queries.

Just like how the optimizer comes up with one “good enough” plan for all queries.

Thanks for reading!

Church

I often find myself reviewing missing index requests during consulting engagements. Not because they’re so awesome, but because they’re often just good enough to provide some quick relief before more fine-tuned efforts are explored.

More to the point: if someone has no idea which queries they need to tune, and everything is pretty slow, this is a good starting place.

Given sufficient server uptime, of course.

World Tour

Sometimes you’ll see that slam-dunk missing index request with lots of uses, and you can tie it to a query that you know is bad. Of course, I’m quite partial to using sp_BlitzIndex to analyze indexes. There are a few different places that missing indexes will be detailed in.

• Mode 0: the most important stuff
• Mode 4: anything and everything
• Mode 3: just missing index requests
• Table Mode: analyzing just one table

The easiest way to find examples like I’ll be talking about is to look at just one table. In this case, the Posts table.

EXEC sp_BlitzIndex @TableName = 'Posts';

If you have missing index requests for a table, they’ll look something like this:

That estimated benefit number is pretty big here, so it jumps out a bit. Normally I don’t start really paying attention until that number is >5 million. That’s not terribly scientific, but you have to draw the line somewhere.

Of course, one very sneaky thing to consider is when you have a set of duplicative requests with low-ish estimated benefit individually, but combined they just might add up to something quite useful.

Just A Kid

The estimated benefit number is just a function of the three feedback metrics that get logged with missing index requests: uses * impact * average query cost.

Uses is a fairly reliable metric, but impact and average query cost are a little more hand-wavy. Even high-cost queries can be very fast. It doesn’t mean that they can’t be tuned or don’t need indexes, but they might not be your worst-performers.

In tomorrow’s post, we’ll look at that, and how you can come up with a good-enough index for a bunch of similar queries.

Thanks for reading!