Why Comparing Columns Doesn’t Always Seek


When writing queries, sometimes you have the pleasure of being able to pass a literal value, parameter,  or scalar expression as a predicate.

With a suitable index in place, any one of them can seek appropriately to the row(s) you care about.

But what about when you need to compare the contents of one column to another?

It gets a little bit more complicated.

All About Algorithms

Take this query to start with, joining Users to Posts.

    c = COUNT_BIG(*)
FROM dbo.Users AS u
JOIN dbo.Posts AS p
    ON p.OwnerUserId = u.Id;

The OwnerUserId column doesn’t have an index on it, but the Id column on Users it the primary key and the clustered index.

But the type of join that’s chosen is Hash, and since there’s no where clause, there’s no predicate to apply to either table for filtering.


This is complicated slightly by the Bitmap, which is created on the OwnerUserId column from the Posts table and applied to the Id column from the Users table as an early filter.

However, it’s a useless Bitmap, and Bitmaps don’t really seek anyway.

The same pattern can generally be observed with Merge Joins.  Where things are a bit different is with Nested Loops.

Shoop Da Loop

If we use a query hint, we can see what would happen with a Nested Loops Join.

    c = COUNT_BIG(*)
FROM dbo.Users AS u
JOIN dbo.Posts AS p
    ON p.OwnerUserId = u.Id

The plan looks like this now, with a Seek on the Users table.

petting motions

The reason is that this flavor of Nested Loops, known as Apply Nested Loops, takes each row from the outer input and uses it as a scalar operator on the inner input.

An example of Regular Joe Nested Loops™ looks like this:

    c = COUNT_BIG(*)
FROM dbo.Users AS u
WHERE u.Reputation = 
    FROM dbo.Posts AS p

Where the predicate is applied at the Nested Loops operator:

and bert

Like most things, indexing is key, but there are limits.


Let’s create this index:

CREATE INDEX ud ON dbo.Users(UpVotes, DownVotes);

And run this query:

    c = COUNT_BIG(*)
FROM dbo.Users AS u
WHERE u.UpVotes = u.DownVotes;

The resulting query plan looks like this:

did a cuss

But what other choice is there? If we want a seek, we need a particular thing or things to seek to.

    c = COUNT_BIG(*)
FROM dbo.Users AS u
WHERE u.UpVotes = u.DownVotes
AND   u.UpVotes = 1;
name game

We seek to everyone with an UpVote of 1, and then somewhat awkwardly search the DownVotes column for values >= 1 and <= 1.

But again, these are specific values we can search for.

Thanks for reading!


How SQL Server 2019 Helps You Link Queries To Missing Index Requests

Not Another Upgrade

When dm_db_missing_index_group_stats_query got documented, I was really happy. After all, this has been a peeve of mine for ages.

“Wow look at all these missing index requests. Where’d they come from?”

So this is neat! And it’s better than nothing, but there are some quirks.

And what’s a quirk, after all, but a twerk that no one enjoys.


The first thing to note about this DMV is that there are two columns purporting to have sql_handles in them. No, not that sql_handle.

One of them can’t be used in the traditional way to retrieve query text. If you try to use last_statement_sql_handle, you’ll get an error.

    query_text = 
            (ddmigsq.last_statement_start_offset / 2) + 1,
                    CASE ddmigsq.last_statement_end_offset 
                        WHEN -1 
                        THEN DATALENGTH(dest.text) 
                        ELSE ddmigsq.last_statement_end_offset 
                    - ddmigsq.last_statement_start_offset 
                ) / 2 
            ) + 1
FROM sys.dm_db_missing_index_group_stats_query AS ddmigsq
CROSS APPLY sys.dm_exec_sql_text(ddmigsq.last_statement_sql_handle) AS dest;

Msg 12413, Level 16, State 1, Line 27
Cannot process statement SQL handle. Try querying the sys.query_store_query_text view instead.

Is Vic There?

One other “issue” with the view is that entries are evicted from it if they’re evicted from the plan cache. That means that queries with recompile hints may never produce an entry in the table.

Is this the end of the world? No, and it’s not the only index-related DMV that behaves this way: dm_db_index_usage_stats does something similar with regard to cached plans.

As a quick example, if I execute these two nearly-identical queries, the DMV only records one potential use of the index:

FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.Score < 0;

FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.Score < 0

Italic Stallion

You may have noticed that may was italicized in when talking about whether or not plans with recompile hints would end up in here.

Some of them may, if they’re part of a larger batch. Here’s an example:

FROM dbo.Posts AS p
WHERE p.OwnerUserId = 22656
AND   p.Score < 0

FROM dbo.Users AS u
JOIN dbo.Posts AS p
   ON p.OwnerUserId = u.Id
JOIN dbo.Comments AS c
    ON c.PostId = p.Id
WHERE u.Reputation = 1
AND   p.PostTypeId = 3
AND   c.Score = 0;

Most curiously, if I run that batch twice, the missing index request for the recompile plan shows two uses.



You may have also noticed something odd in the above screenshot, too. One query has produced three entries. That’s because…

The query has three missing index requests. Go ahead and click on that.

lovecraft, baby

Another longstanding gripe with SSMS is that it only shows you the first missing index request in green text, and that it might not even be the “most impactful” one.

That’s the case here, just in case you were wondering. Neither the XML, nor the SSMS presentation of it, attempt to order the missing indexes by potential value.

You can use the properties of the execution plan to view all missing index requests, like I blogged about here, but you can’t script them out easily like you can for the green text request at the top of the query plan.

something else

At least this way, it’s a whole heck of a lot easier for you to order them in a way that may be more beneficial.


Of course, I don’t expect you to write your own queries to handle this. If you’re the type of person who enjoys Blitzing things, you can find the new 2019 goodness in sp_BlitzIndex, and you can find all the missing index requests for a single query in sp_BlitzCache in a handy-dandy clickable column that scripts out the create statements for you.

Thanks for reading!

Spools Are Just Crappy Temp Tables

But My Tempdb

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.


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.


Another benefit is that each branch that does a distinct aggregation is largely in a parallel zone until the global aggregate.


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!

INTO #better_spool
FROM dbo.Votes AS v
JOIN dbo.Posts AS p
    ON p.Id = v.PostId;

    PostId = COUNT_BIG(DISTINCT s.PostId),
    UserId = COUNT_BIG(DISTINCT s.UserId), 
    BountyAmount = COUNT_BIG(DISTINCT s.BountyAmount), 
    VoteTypeId = COUNT_BIG(DISTINCT s.VoteTypeId), 
    CreationDate = COUNT_BIG(DISTINCT s.CreationDate)
FROM #better_spool AS s;


Multiple Distinct Aggregates: Still Harmful (Without Batch Mode)


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.

   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.

<Object name="Votes" schema_name="dbo">
    <Lock resource_type="OBJECT" request_mode="IS" request_status="GRANT" request_count="9" />
    <Lock resource_type="PAGE" page_type="*" index_name="PK_Votes__Id" request_mode="S" request_status="GRANT" request_count="14" />

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.

   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.


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.


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!

Compressed Indexes And The Buffer Pool

Mail Drag

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:

ON dbo.Posts

ON dbo.Posts

Looking at what’s in memory:


Now let’s create a couple indexes with compression:

ON dbo.Posts

ON dbo.Posts

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.


Now in memory: way less stuff.

So there you go.

Thanks for reading!

Tuning I/O Is Often About Tuning Indexes

One Metric Ton Of Indexes

Let’s say you hate your storage. Let’s say you hate it so much that you want you SQL Serve to touch it as little as possible.

You’re most of the people I talk to. Congratulations.

But how do you do that?

Let’s talk about a few things.

How SQL Server Works With Data

It doesn’t matter if a query wants to read or modify data, all those itty-bitty little data pages need to end up in memory.

How much ends up in memory depends on how big your tables are, and how helpful your indexes are.

Likewise, the more indexes you need to modify, the more need to be in memory for that to happen.

You need to design indexes so that you can support your queries by making it easy for them to locate data. That’s your where clause, and guess what?

Your modification queries have where clauses, too.

How You Can Make Indexing Better

Make sure you’re reviewing your indexes regularly. Things that you need to keep an eye on:

  • Duplicative indexes
  • Under-utilized indexes

Even when indexes are defined on the same columns, they’re separate sets of pages within your data files.

  • If you have indexes that are on very similar sets of columns, or supersets/subsets of columns, it’s probably time to start merging them
  • If you have indexes that just aren’t being read, or aren’t being read anywhere near as much as they’re written to, you should think about ditching them

Cleaning up indexes like this gives you more breathing room to add in other indexes later.

It also gives you far fewer objects competing for space in memory.

That means the ones you have left stand a better chance of staying there, and your queries not having to go to disk for them.

How You Can Make Indexes Better

There are all sorts of things you can do to make indexes better, too. I don’t mean rebuilding them, either!

I mean getting smarter about what you’re indexing.

Things like filtered indexes and index compression can net you big wins when it comes to reducing the overall size of indexes.

My friend Andy Mallon has some Great Posts™ about compression over on his blog:

And of course, computed columns can help if you’ve got a wonky schema.

Smaller indexes that take up less space in memory make more efficient use of the space you have, which means you can fit more in there.

How You Can Make Tables Better

There are some obvious bits here, like being extra careful with choosing string length.

LOB data can lead to weird locking, and mess with memory grants.

And of course, overly-wide, non-normalized tables can also lead to issues.

If you’re running an OLTP workload, you may also want to make sure that your critical tables aren’t heaps.

Those things tend to take up more space in memory than they need to.

And of course, if you need any help fixing these types of issues, drop me a line!

Thanks for reading!

Documentation for dm_db_missing_index_group_stats_query

No, It’s New

When I was checking out early builds of SQL Server 2019, I noticed a new DMV called dm_db_missing_index_group_stats_query, that I thought was pretty cool.

It helped you tie missing index requests to the queries that requested them. Previously, that took a whole lot of heroic effort, or luck.

With this new DMV, it’s possible to combine queries that look for missing indexes with queries that look for tuning opportunities in the plan cache or in Query Store.

It seems to tie back to dm_db_missing_index_groups, on the index_group_handle column in this DMV joined to the group handle column in the new DMV.

If you’re wondering why I’m not giving you any code samples here, it’s because I’m going to get some stuff built into sp_BlitzIndex to take advantage of it, now that it’s documented.

Special thanks to William Assaf (b|t) for helping to get this done.

Thanks for reading!

Parameter Sniffing Is Usually A Good Thing

Tick Tock

I talk to a lot of people about performance tuning. It seems like once someone is close enough to a database for long enough, they’ll have some impression of parameter sniffing. Usually a bad one.

You start to hear some funny stuff over and over again:

  • We should always recompile
  • We should always use local variables
  • We should always recompile and use local variables

Often, even if it means writing unsafe dynamic SQL, people will be afraid to parameterize things.

Between Friends

To some degree, I get it. You’re afraid of incurring some new performance problem.

You’ve had the same mediocre performance for years, and you don’t wanna make something worse.

The thing is, you could be making things a lot better most of the time.

  • Fewer compiles and recompiles, fewer single-use plans, fewer queries with multiple plans
  • Avoiding the local variable nonsense is, more often than not, going to get you better performance

A Letter To You

I’m going to tell you something that you’re not going to like, here.

Most of the time when I see a parameter sniffing problem, I see a lot of other problems.

Shabbily written queries, obvious missing indexes, and a whole list of other things.

It’s not that you have a parameter sniffing problem, you have a general negligence problem.

After all, the bad kind of parameter sniffing means that you’ve got variations of a query plan that don’t perform well on variations of parameters.

Once you start taking care of the basics, you’ll find a whole lot less of the problems that keep you up at night.

If that’s the kind of thing you need help with, drop me a line.

Thanks for reading!

A General Indexing Strategy For Normal Queries

Find Your Data First

Most queries will have a where clause. I’ve seen plenty that don’t. Some of’em have surprised the people who developed them far more than they surprised me.

But let’s start there, because it’s a pretty important factor in how you design your indexes. There are all sorts of things that indexes can help, but the first thing we want indexes to do in general is help us locate data.

Why? Because the easier we can locate data, the easier we can eliminate rows early on in the query plan. I’m not saying we always need to have an index seek, but we generally want to filter out rows we don’t care about when we’re touching the table they’re in.


When we carry excess rows throughout the query plan, all sorts of things get impacted and can become less efficient. This goes hand in hand with cardinality estimation.

At the most severe, rows can’t be filtered when we touch tables, or even join them together, and we have to filter them out later.

I wrote about that here and here.

When that happens, it’s probably not your indexes that are the problem — it’s you.

You, specifically. You and your awful query.

We can take a page from the missing index request feature here: helping queries find the rows we care about should be a priority.

Sweet N’ Low

When people talk about the order predicates are evaluated in, the easiest way to influence that is with the order of columns in the key of your index.

Since that defines the sort order of the index, if you want a particular column to be evaluated first, put it first in the key of the index.

Selectivity is a decent attribute to consider, but not the end all be all of index design.

Equality predicates preserve ordering of other key columns in the index, which may or may not become important depending on what your query needs to accomplish.

Post Where

After the where clause, there are some rather uncontroversial things that indexes can help with:

  • Joins
  • Grouping
  • Ordering

Of course, they help with this because indexes put data in order.

Having rows in a deterministic order makes the above things either much easier (joining and grouping), or free (ordering).

How we decide on key column order necessarily has to take each part of the query involved into account.

If a query is so complicated that creating one index to help it would mean a dozen key columns, you probably need to break things down further.


When you’re trying to figure out a good index for one query, you usually want to start with the where clause.

Not always, but it makes sense in most cases because it’s where you can find gains in efficiency.

If your index doesn’t support your where clause, you’re gonna see an index scan and freak out and go in search of your local seppuku parlor.

After that, look to other parts of your query that could help you eliminate rows. Joins are an obvious choice, and typically make good candidates for index key columns.

At this point, your query might be in good enough shape, and you can leave other things alone.

If so, great! You can make the check out to cache. I mean cash.

Thanks for reading!