Does Using TOP In A SQL Server Common Table Expression Materialize It?

Nope.


The sad news for you here is that nothing aside from selecting a CTE into a real or temporary table will materialize the result of the query within in it.

WITH 
    cte AS
(
    SELECT TOP (1)
        u.Id
    FROM dbo.Users AS u
)
SELECT
    c1.*
FROM cte AS c1
JOIN cte AS c2
    ON c1.Id = c2.Id
JOIN cte AS c3
    ON c1.Id = c3.Id;

This query will still have to touch the Users table three times. I’ve blogged about this part before, of course.

SQL Server Query Plan
butty

Bounce


You may notice something interesting in there, though, once you get past the disappointment of seeing three scans of the Users table.

Each scan is preceded by the TOP operator. This can sometimes be where people confuse the behavior of TOP in a Common Table Expression or Derived Table.

It’s not a physical manifestation of the data into an object, but (at least for now) it is a logical separation of the query semantics.

In short, it’s a fence.

The reason why it’s a fence is because using TOP sets a row goal, and the optimizer has to try to meet (but not exceed) that row goal for whatever part of the query is underneath it.

Strange


Take this query for example, which loads a bunch of work into a Common Table Expression with a TOP in it:

WITH
    Posts AS
(
    SELECT TOP (1000)
         p.*
    FROM dbo.Posts AS p
    WHERE p.PostTypeId = 1
    AND   p.Score > 500
    AND   EXISTS
          (
              SELECT
                  1/0
               FROM dbo.Users AS u
               WHERE u.Id = p.OwnerUserId
          )
    AND EXISTS
        (
            SELECT
                1/0
            FROM dbo.Badges AS b
            WHERE b.UserId = p.OwnerUserId
        )
    ORDER BY p.Score DESC
)
SELECT
    u.DisplayName,
    p.PostTypeId,
    p.Score
FROM dbo.Users AS u
JOIN Posts AS p
    ON p.OwnerUserId = u.Id
ORDER BY u.Reputation DESC;

And the plan for it looks like this:

SQL Server Query Plan
lucky one

All the work within the Common Table Expression is fenced by the top.

There are many times you can use this to your advantage, when you know certain joins or predicates can produce a very selective result.

Care Control


As a final note, just be really careful how you position your TOPs. They insert loads of semantic differences to the query.

If you don’t believe me, put a TOP in the wrong place and watch your results change dramatically.

Thanks for reading!

Going Further


If this is the kind of SQL Server stuff you love learning about, you’ll love my training. I’m offering a 75% discount on to my blog readers if you click from here. I’m also available for consulting if you just don’t have time for that and need to solve performance problems quickly.

A Parameterization Performance Puzzle With TOP PERCENT in SQL Server

Lawdy


There was a three-part series of posts where I talked about a weird performance issue you can hit with parameterized top. While doing some query tuning for a client recently, I ran across a funny scenario where they were using TOP PERCENT to control the number of rows coming back from queries.

With a parameter.

So uh. Let’s talk about that.

Setup Time


Let’s start with a great index. Possibly the greatest index ever created.

CREATE INDEX whatever 
ON dbo.Votes
    (VoteTypeId, CreationDate DESC)
WITH
(
    MAXDOP = 8,
    SORT_IN_TEMPDB = ON
);
GO

Now let me show you this stored procedure. Hold on tight!

CREATE OR ALTER PROCEDURE dbo.top_percent_sniffer 
(
    @top bigint, 
    @vtid int
)
AS
SET NOCOUNT, XACT_ABORT ON;
BEGIN
    SELECT TOP (@top) PERCENT
        v.*
    FROM dbo.Votes AS v
    WHERE v.VoteTypeId = @vtid
    ORDER BY v.CreationDate DESC;

END;

Cool. Great.

Spool Hardy


When we execute the query, the plan is stupid.

EXEC dbo.top_percent_sniffer
    @top = 1,
    @vtid = 6;
GO
SQL Server Query Plan
the louis vuitton of awful

We don’t use our excellent index, and the optimizer uses an eager table spool to hold rows and pass the count to the TOP operator until we hit the correct percentage.

This is the least ideal situation we could possibly imagine.

Boot and Rally


A while back I posted some strange looking code on Twitter, and this is what it ended up being used for (among other things).

The final version of the query looks like this:

CREATE OR ALTER PROCEDURE dbo.top_percent_sniffer 
(
    @top bigint, 
    @vtid int
)
AS
SET NOCOUNT, XACT_ABORT ON;
BEGIN;
    
    WITH pct AS
    (
        SELECT
            records = 
                CONVERT(bigint, 
                    CEILING(((@top * COUNT_BIG(*)) / 100.)))
        FROM dbo.Votes AS v
        WHERE v.VoteTypeId = @vtid
    )
    SELECT
        v.*
    FROM pct
    CROSS APPLY
    (
        SELECT TOP (pct.records)
            v.*
        FROM dbo.Votes AS v
        WHERE v.VoteTypeId = @vtid
        ORDER BY v.CreationDate DESC
    ) AS v;

END;
GO
SQL Sever Query Plan
better butter

Soul Bowl


This definitely has drawbacks, since the expression in the TOP always gives a 100 row estimate. For large numbers of rows, this plan could be a bad choice and we might need to do some additional tuning to get rid of that lookup.

There might also be occasions when using a column store index to generate the count would be benefit, and the nice thing here is that since we’re accessing the table in two different ways, we could use two different indexes.

But for reliably small numbers of rows, this is a pretty good solution.

Thanks for reading!

Going Further


If this is the kind of SQL Server stuff you love learning about, you’ll love my training. I’m offering a 75% discount on to my blog readers if you click from here. I’m also available for consulting if you just don’t have time for that and need to solve performance problems quickly.

A SQL Server Parameterization Puzzle With TOP: Part 1

The Gift That Keeps On Giving


I love when a demo written for one purpose turns into an even better demo for another purpose.

While working with a client recently, they ran into a performance issue when trying to promote plan reuse by parameterizing the user-input number for TOP.

In part 1, I’m going to show you what happened and why, and in part 2 I’ll discuss some workarounds.

Regresso Chicken Face Soup


When executed with a literal value in the top, this query runs for around 10 seconds.

I’m not saying that’s great, but it’s a good enough starting place.

SELECT TOP (38)
        u.DisplayName,
        b.Name
FROM dbo.Users u
CROSS APPLY 
(
    SELECT TOP (1) 
            b.Name
    FROM dbo.Badges AS b
    WHERE b.UserId = u.Id
    ORDER BY b.Date DESC
) AS b
WHERE u.Reputation >= 10000
ORDER BY u.Reputation DESC;
SQL Server Query Plan
glamping

If we take that same query, put it in a proc, and run it with an identical value in the TOP, things will turn out not-so-well.

CREATE OR ALTER PROCEDURE dbo.SniffedTop (@Top INT)
AS
BEGIN
SET NOCOUNT, XACT_ABORT ON;

    SELECT TOP (@Top)
            u.DisplayName,
            b.Name
    FROM dbo.Users u
    CROSS APPLY 
    (
        SELECT TOP (1) 
                b.Name
        FROM dbo.Badges AS b
        WHERE b.UserId = u.Id
        ORDER BY b.Date DESC
    ) AS b
    WHERE u.Reputation >= 10000
    ORDER BY u.Reputation DESC;

END
GO 

EXEC dbo.SniffedTop @Top = 38;

The query runs for a significantly longer amount of time.

SQL Server Query Plan
half-day

What Happened?


Unofficially, when TOP uses a constant and the constant is “small” (under 101), the exchange packets are allowed to send earlier than normal, as long as the exchange is below the TOP operator. They’re allowed to send as soon as they have a row, rather than waiting for them to fill up completely.

This can only happen with constants (or…!), and the behavior is true going back to 2005. It may change in the future, so if you’re reading this at some far off date, please don’t be too harsh on me in the comments.

When you parameterize TOP, it’s considered unsafe to send the exchange buffers early. After all, you could stick anything in there, up through the BIGINT max. In cases where you’ve got a BIG TOP, sending, say, 9,223,372,036,854,775,807 rows one at a time would be significantly ickier than sending over a smaller number of full exchange buffers.

If you’re surprised to hear that parallel exchange buffers can send at different times, you’re not alone. I was also surprised.

SQL Server: Full Of Surprises. Horrible surprises.

In the second query, where exchange buffers are sent when full, we spend a lot longer waiting for them to fill up. This isn’t exposed anywhere in the plan, and you’d need either a debugger or this blog post to figure it out.

Yep.

Yep. Yep. Yep. Yep. Yep. Yep.

Thanks for reading!

Going Further


If this is the kind of SQL Server stuff you love learning about, you’ll love my training. I’m offering a 75% discount on to my blog readers if you click from here. I’m also available for consulting if you just don’t have time for that and need to solve performance problems quickly.

Starting SQL: A Little TOP Can Have A Big Sort In SQL Server Query Plans

Shortcuts


To illustrate this problem a little bit better, I need to use a slightly different query.

While a tidy group by was nice to illustrate some of the simpler points about lookups, it overly complicates things when we want to involve other columns.

The second we need anything else in the select or order by portions of a query, we need to apply aggregates to them, or include them in the group by.

And you see, once you set up a query to return the TOP N rows, there’s an expectation that users get to choose the order they start seeing rows in. As long as we stick to columns whose ordering is supported by an index, things will be pretty stable.

Once we go outside that, a TOP can be rough on a query.

Order By CreationDate


Even if we order by CreationDate in descending order, with the index created in ascending order, we don’t need an explicit sort operation to put data in order.

SELECT TOP (1000)
       p.CreationDate,
       p.OwnerUserId,
       p.Score
FROM dbo.Posts AS p
WHERE p.CreationDate >= '20131015'
ORDER BY p.CreationDate DESC;

There are some additional possibilities for this kind of thing with multi-key indexes that we’ll talk about later in the series, but for now this is a good enough illustration of indexes putting data in order.

SQL Server Query Plan
big chain

Order By Score


Let’s ask for data in a different order now. Score is a convenient villain, because there are lots of times when you might want to see things by a highest whatever metric is commendable to be high. Like not blood pressure, probably.

SELECT TOP (1000)
       p.CreationDate,
       p.OwnerUserId,
       p.Score
FROM dbo.Posts AS p
WHERE p.CreationDate >= '20131015'
ORDER BY p.Score DESC;
SQL Server Query Plan
blown out

Without an index that has Score in the key, we need to physically put the data in order to fit the requirements of the query. Note that in this case, the optimizer no longer sees any benefit to using our nonclustered index.

Why Do We Care About Sorts?


Without jumping too far ahead, Sorts need extra memory to run.

You know, that stuff you cache data in, and you don’t have enough of already?

Yeah, that memory.

The same memory that gives you a conniption every time PLE fluctuates.

How much they ask for is going to depend on:

  • How much memory you have
  • What max server memory is set to
  • The size of the data you need to sort

If we aren’t able to get enough memory, or if we don’t ask for enough up front, data could spill from memory to disk.

When spills get big enough, they can sometimes cause performance issues.

This is a good time to start talking a little bit more about indexes, so we can understand more about how they work and help us solve problems.

Thanks for reading!

Going Further


If this is the kind of SQL Server stuff you love learning about, you’ll love my training. I’m offering a 75% discount on to my blog readers if you click from here. I’m also available for consulting if you just don’t have time for that and need to solve performance problems quickly.

SQL Server T-SQL Protip: watch those TOPs without ORDER BY

In the documentation for TOP, the following is listed as a best practice:

In a SELECT statement, always use an ORDER BY clause with the TOP clause. Because, it’s the only way to predictably indicate which rows are affected by TOP.

Let’s work through a real world example.

The good


One of the great things about the “Top Resource Consuming Queries” query store SSMS report is that it is always able to render the query plan, even for very complex queries. I’m not aware of a pure T-SQL solution that can avoid requiring the end user to save xml to files in all cases. The report nearly always takes a long time to run, so it’s easy to capture the T-SQL that powers the grid details version:

DECLARE @results_row_count INT = 100,
@interval_start_time DATETIMEOFFSET = '2019-05-24 15:30:00 +00:00',
@interval_end_time DATETIMEOFFSET = '2019-05-24 18:00:00 +00:00';
 
SELECT TOP (@results_row_count)
    p.query_id query_id,
    q.object_id object_id,
    ISNULL(OBJECT_NAME(q.object_id),'') object_name,
    qt.query_sql_text query_sql_text,
    ROUND(CONVERT(float, SUM(rs.avg_duration*rs.count_executions))*0.001,2) total_duration,
    SUM(rs.count_executions) count_executions,
    COUNT(distinct p.plan_id) num_plans
FROM sys.query_store_runtime_stats rs
    JOIN sys.query_store_plan p ON p.plan_id = rs.plan_id
    JOIN sys.query_store_query q ON q.query_id = p.query_id
    JOIN sys.query_store_query_text qt ON q.query_text_id = qt.query_text_id
WHERE NOT (rs.first_execution_time > @interval_end_time OR rs.last_execution_time < @interval_start_time)
GROUP BY p.query_id, qt.query_sql_text, q.object_id
HAVING COUNT(distinct p.plan_id) >= 1
ORDER BY total_duration DESC;

Note the presence of the ORDER BY. I get exactly the results that I was expecting:

The bad


If I ask for extra details (who doesn’t want more details?), a significantly more complex query is generated:

-- grid format query with additional details
-- grid format query with additional details
DECLARE @results_row_count INT = 100,
@interval_start_time DATETIMEOFFSET = '2019-05-24 15:30:00 +00:00',
@interval_end_time DATETIMEOFFSET = '2019-05-24 18:00:00 +00:00';
 
With wait_stats AS
(
SELECT
    ws.plan_id plan_id,
    ws.execution_type,
    ROUND(CONVERT(float, SUM(ws.total_query_wait_time_ms)/SUM(ws.total_query_wait_time_ms/ws.avg_query_wait_time_ms))*1,2) avg_query_wait_time,
    ROUND(CONVERT(float, SQRT( SUM(ws.stdev_query_wait_time_ms*ws.stdev_query_wait_time_ms*(ws.total_query_wait_time_ms/ws.avg_query_wait_time_ms))/SUM(ws.total_query_wait_time_ms/ws.avg_query_wait_time_ms)))*1,2) stdev_query_wait_time,
    CAST(ROUND(SUM(ws.total_query_wait_time_ms/ws.avg_query_wait_time_ms),0) AS BIGINT) count_executions,
    MAX(itvl.end_time) last_execution_time,
    MIN(itvl.start_time) first_execution_time
FROM sys.query_store_wait_stats ws
    JOIN sys.query_store_runtime_stats_interval itvl ON itvl.runtime_stats_interval_id = ws.runtime_stats_interval_id
WHERE NOT (itvl.start_time > @interval_end_time OR itvl.end_time < @interval_start_time)
GROUP BY ws.plan_id, ws.runtime_stats_interval_id, ws.execution_type ),
top_wait_stats AS
(
SELECT TOP (@results_row_count)
    p.query_id query_id,
    q.object_id object_id,
    ISNULL(OBJECT_NAME(q.object_id),'') object_name,
    qt.query_sql_text query_sql_text,
    ROUND(CONVERT(float, SUM(ws.avg_query_wait_time*ws.count_executions))*1,2) total_query_wait_time,
    SUM(ws.count_executions) count_executions,
    COUNT(distinct p.plan_id) num_plans
FROM wait_stats ws
    JOIN sys.query_store_plan p ON p.plan_id = ws.plan_id
    JOIN sys.query_store_query q ON q.query_id = p.query_id
    JOIN sys.query_store_query_text qt ON q.query_text_id = qt.query_text_id
WHERE NOT (ws.first_execution_time > @interval_end_time OR ws.last_execution_time < @interval_start_time)
GROUP BY p.query_id, qt.query_sql_text, q.object_id
),
top_other_stats AS
(
SELECT TOP (@results_row_count)
    p.query_id query_id,
    q.object_id object_id,
    ISNULL(OBJECT_NAME(q.object_id),'') object_name,
    qt.query_sql_text query_sql_text,
    ROUND(CONVERT(float, SUM(rs.avg_duration*rs.count_executions))*0.001,2) total_duration,
    ROUND(CONVERT(float, SUM(rs.avg_cpu_time*rs.count_executions))*0.001,2) total_cpu_time,
    ROUND(CONVERT(float, SUM(rs.avg_logical_io_reads*rs.count_executions))*8,2) total_logical_io_reads,
    ROUND(CONVERT(float, SUM(rs.avg_logical_io_writes*rs.count_executions))*8,2) total_logical_io_writes,
    ROUND(CONVERT(float, SUM(rs.avg_physical_io_reads*rs.count_executions))*8,2) total_physical_io_reads,
    ROUND(CONVERT(float, SUM(rs.avg_clr_time*rs.count_executions))*0.001,2) total_clr_time,
    ROUND(CONVERT(float, SUM(rs.avg_dop*rs.count_executions))*1,0) total_dop,
    ROUND(CONVERT(float, SUM(rs.avg_query_max_used_memory*rs.count_executions))*8,2) total_query_max_used_memory,
    ROUND(CONVERT(float, SUM(rs.avg_rowcount*rs.count_executions))*1,0) total_rowcount,
    ROUND(CONVERT(float, SUM(rs.avg_log_bytes_used*rs.count_executions))*0.0009765625,2) total_log_bytes_used,
    ROUND(CONVERT(float, SUM(rs.avg_tempdb_space_used*rs.count_executions))*8,2) total_tempdb_space_used,
    SUM(rs.count_executions) count_executions,
    COUNT(distinct p.plan_id) num_plans
FROM sys.query_store_runtime_stats rs
    JOIN sys.query_store_plan p ON p.plan_id = rs.plan_id
    JOIN sys.query_store_query q ON q.query_id = p.query_id
    JOIN sys.query_store_query_text qt ON q.query_text_id = qt.query_text_id
WHERE NOT (rs.first_execution_time > @interval_end_time OR rs.last_execution_time < @interval_start_time)
GROUP BY p.query_id, qt.query_sql_text, q.object_id
)
SELECT TOP (@results_row_count)
    A.query_id query_id,
    A.object_id object_id,
    A.object_name object_name,
    A.query_sql_text query_sql_text,
    A.total_duration total_duration,
    A.total_cpu_time total_cpu_time,
    A.total_logical_io_reads total_logical_io_reads,
    A.total_logical_io_writes total_logical_io_writes,
    A.total_physical_io_reads total_physical_io_reads,
    A.total_clr_time total_clr_time,
    A.total_dop total_dop,
    A.total_query_max_used_memory total_query_max_used_memory,
    A.total_rowcount total_rowcount,
    A.total_log_bytes_used total_log_bytes_used,
    A.total_tempdb_space_used total_tempdb_space_used,
    ISNULL(B.total_query_wait_time,0) total_query_wait_time,
    A.count_executions count_executions,
    A.num_plans num_plans
FROM top_other_stats A LEFT JOIN top_wait_stats B on A.query_id = B.query_id and A.query_sql_text = B.query_sql_text and A.object_id = B.object_id
WHERE A.num_plans >= 1
ORDER BY total_duration DESC
)

Now we have not 1, not 2, but THREE TOP operators! But only one of them has an ORDER BY. The results are completely different, and are pretty much useless:

The ugly


This has nothing to do with TOP as far as I know, but I included it just for fun:

Final thoughts


All of you developers out there should watch your TOPs and make sure you’re using ORDER BY as needed. Otherwise, you might end up with annoyed end users writing blog posts about your code.

Thanks for reading!

Going Further


If this is the kind of SQL Server stuff you love learning about, you’ll love my training. I’m offering a 75% discount on to my blog readers if you click from here. I’m also available for consulting if you just don’t have time for that and need to solve performance problems quickly.

Are Self Joins Ever Better Than Key Lookups In SQL Server?

Sorta Topical


Like most tricks, this has a specific use case, but can be quite effective when you spot it.

I’m going to assume you have a vague understanding of parameter sniffing with stored procedures going into this. If you don’t, the post may not make a lot of sense.

Or, heck, maybe it’ll give you a vague understanding of parameter sniffing in stored procedures.

One For The Money


Say I have a stored procedure that accepts a parameter called @Reputation.

The body of the procedure looks like this:

    SELECT TOP (1000) 
	        u.*
    FROM dbo.Users AS u
    WHERE u.Reputation = @Reputation
    ORDER BY u.CreationDate DESC;

In the users table, there are a lot of people with a Reputation of 1.

There are not so many with a Reputation of 2.

+------------+---------+
| Reputation | records |
+------------+---------+
|          1 | 1090043 |
|          2 |    1854 |
+------------+---------+

Two For The Slow


Data distributions like this matter. They change how SQL Server approaches coming up with an execution plan for a query.

Which indexes to use, what kind of joins to use, how to aggregate data, if the plan should be serial or parallel…

The list goes on and on.

In this case, we have a narrow-ish nonclustered index:

    CREATE INDEX whatever 
        ON dbo.Users (Reputation, Age, CreationDate);

When I run my stored procedure and look for Reputation = 2, the plan is very fast.

EXEC dbo.WORLDSTAR @Reputation = 2;
SQL Server Query Plan
Getting to know you.

This is a great plan for a small number of rows.

When I run it for a large number of rows, it’s not nearly as fast.

EXEC dbo.WORLDSTAR @Reputation = 1;
SQL Server Query Plan
Stuck on you.

We go from a fraction of a second to over three seconds.

This is bad parameter sniffing.

If we run it for Reputation = 1 first, we don’t have the same problem.

That’s good(ish) parameter sniffing.

Better For Everyone


Many things that prevent parameter sniffing will only give you a so-so plan. It may be better than the alternative, but it’s certainly not a “fix”.

It’s possible to get a better plan for everyone in this situation by re-writing the Key Lookup as a self join

    SELECT TOP (1000) 
	        u2.*
    FROM dbo.Users AS u
    JOIN dbo.Users AS u2
        ON u.Id = u2.Id
    WHERE u.Reputation = @Reputation
    ORDER BY u.CreationDate DESC;

The reason why is slightly complicated, but I’ll do my best to explain it simply.

Here’s what the bad parameter sniffing plan looks like for each query.

Note that the Key Lookup plan still runs for ~3 seconds, while the self-join plan runs for around half a second.

SQL Server Query Plan
DAWG CHILL

While it’s possible for Key Lookups to have Sorts introduced to optimize I/O… That doesn’t happen here.

The main difference between the two plans (aside from run time), is the position of the Sort.

In the Key Lookup plan (top), the Key Lookup between the nonclustered and clustered indexes runs to completion.

In other words, for everyone with a Reputation of 1, we go to the clustered index to retrieve the columns that aren’t part of the nonclustered index.

In the self-join plan (bottom), all rows go into the Sort, but only the 1000 come out.

Different World


The difference is more obvious when viewed with Plan Explorer.

SQL Server Query Plan
Get’Em High

In the Key Lookup plan, rows aren’t narrowed until the end so a seek occurs ~1mm times.

In the self-join plan, they’re eliminated directly after the Index Seek, so the join only runs for 1000 rows and produces 1000 seeks.

This doesn’t mean that Top N Sorts are bad, it just means that they may not produce the most optimal plans for Key Lookups.

When This Doesn’t Work


Without a TOP, the self-join pattern isn’t as dramatically faster, but it is about half a second better (4.3s vs. 3.8s) for the bad parameter sniffing scenario, and far less for the others.

Of course, an index change to put CreationDate as the second key column fixes the issue by removing the need to sort data at all.

    CREATE INDEX whatever --Current Index
        ON dbo.Users (Reputation, Age, CreationDate);
    GO 
    
    CREATE INDEX apathy --Better Index For This Query
	    ON dbo.Users (Reputation, CreationDate, Age);
    GO

But, you know, not everyone is able to make index changes easily, and changing the key column order can cause problems for other queries.

Thanks for reading!

Going Further


If this is the kind of SQL Server stuff you love learning about, you’ll love my training. I’m offering a 75% discount on to my blog readers if you click from here. I’m also available for consulting if you just don’t have time for that and need to solve performance problems quickly.

In SQL Server Stored Procedures, Parameters In TOP Are Sniffed Too

Thank You For Your Business


In yesterday’s post, we looked at how row goals affected performance when data matching our join and where clauses didn’t exist.

Today we’re going to look at something similar, and perhaps a workaround to avoid the same issues.

Here’s our index setup:

CREATE INDEX whatever ON dbo.Votes(CreationDate DESC, VoteTypeId)
GO

Now we’re gonna wrap our query in a stored procedure.

CREATE OR ALTER PROCEDURE dbo.top_sniffer (@top INT, @vtid INT)
AS
BEGIN

    SELECT   TOP (@top) v.*
    FROM     dbo.Votes AS v
    WHERE    v.VoteTypeId = @vtid
    ORDER BY v.CreationDate DESC;

END;

Video Killed MTV


You know what? I don’t wanna write all this stuff. Let’s do a video.

Thanks for reading!

Going Further


If this is the kind of SQL Server stuff you love learning about, you’ll love my training. I’m offering a 75% discount on to my blog readers if you click from here. I’m also available for consulting if you just don’t have time for that and need to solve performance problems quickly.