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Category: Syntax

Joins When No Join Types Are Valid

Published 2018-08-08 by Kevin Feasel

Hugo Kornelis has a brain-teaser for us:

The query below can be executed in any version of the AdventureWorks sample database. Don’t bother understanding the logic, there is none. It is merely constructed to show how SQL Server handles what appears to be an impossible situation.

If you look at the descriptions of the various join operators in the Execution Plan Reference, you will see that this query poses the optimizer for what appears to be an insolvable problem: none of the join operators can be used for this query!

But it’s possible, and Hugo explains exactly what happens, as well as places where the optimizer could be better at solving the impossible (or at least marginally difficult).

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Implementation Matters: CTEs In Postgres And SQL Server

Published 2018-08-07 by Kevin Feasel

Brent Ozar looks at a couple of places where Postgres and SQL Server differ in implementation details:

In SQL Server, if you write this query:

SQL Server builds a query plan for the entire operation at once, and passes the WHERE clause filter into the CTE. The resulting query plan is efficient, doing just a single clustered index seek.

In Postgres, CTEs are processed separately first, and subsequent WHERE clauses aren’t applied until later. That means the above query works just fine – but performs horribly. You’ll get much better results if you include your filters inside each CTE, like this:

That’s less than ideal.

The comments are valuable here as well.

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The Value Of CROSS APPLY

Published 2018-07-25 by Kevin Feasel

Kendra Little talks about one of my favorite T-SQL operators:

Here’s my top 3 favorite uses for CROSS APPLY and OUTER APPLY:

  1. APPLY is fantastic for calling table valued functions. I didn’t include questions about those in the quiz, simply for the purposes of keeping the code simple, and because I wanted the quiz to be about thinking through how apply works — but it’s still the #1 use.

  2. Another thing I might use it for is when a query needs a correlated subquery — somewhat like an inline function.

  3. And I also like it for queries that have a calculation that needs to be done and which is referenced in multiple columns in the query, or perhaps also in a predicate and the select. You can perform the computation once in the apply and then reference it multiple times. That way if you have to change the formula later on, you only have to change it in once place, plus I find it’s easier to read in some cases.

Item number three is something I learned from an older blog post Kendra wrote and regularly use APPLY for this purpose.

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Creating An Inline Table-Valued Function In SQL Server

Published 2018-07-18 by Kevin Feasel

Jeanne Combrinck looks at inline table-valued functions in SQL Server:

Lets start off with what is a table-valued function (TVF)? A TVF is a dynamic table produced at the time of execution, depending on parameters. Like a view, a TVF creates a result set only when it’s executed, but, unlike a view, it can be parameterized.

You get two types of TVFs, an Inline Table-Valued Function (ITVFs) and Multi-statement Table-Valued Function (MTVFs). I find them easy to remember, think of the “I” in ITVF as 1 (single statement) and the “M” in MTVF as “many” (multiple statements).

As you can imagine, a TVF produces a result set that can be used as a virtual table or view. Yes, you can actually select data from a TVF, or join it with some other tables, views, or even other TVFs. In this post I will go into more detail on ITVFs.

In my experience, the best part about using inline table-valued function is the 3-9x performance improvement you get when removing them and inlining their code.  It’s a great idea but the performance cost is just too high for me.

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COUNT And NULL Values

Published 2018-07-17 by Kevin Feasel

Kenneth Fisher digs into the COUNT() function and sees how it deals with NULL values:

Count the number of values
1
2
3
SELECT COUNT(FieldName) FROM TableName;
-- or
SELECT COUNT(ALL FieldName) FROM TableName;

The ALL argument is the default and is unnecessary (I didn’t even know it existed until I started this post). Here you are counting the number of non NULL values in FieldName. So in a column with (1, NULL, 1, 2, 3, NULL, 1) you’ll get a count of 5. You do get a nice warning (depending on your ANSI_WARNINGS setting) if there was a NULL value though.

By the way, the ALL operator isn’t useful there, but can be useful along with its counterparts SOME and ANY.  I rarely keep them in my mind, so I instead tend to write EXISTS and NOT EXISTS statements which have operate on an equivalent function.

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Grouping And Aggregating In SQL, R, And Python

Published 2018-07-10 by Kevin Feasel

Dejan Sarka has a few examples of aggregation in different languages, including SQL, R, and Python:

The query calculates the coefficient of variation (defined as the standard deviation divided the mean) for the following groups, in the order as they are listed in the GROUPING SETS clause:

  • Country and education – expression (g.EnglishCountryRegionName, c.EnglishEducation)
  • Country only – expression (g.EnglishCountryRegionName)
  • Education only – expression (c.EnglishEducation)
  • Over all dataset- expression ()

Note also the usage of the GROUPING() function in the query. This function tells you whether the NULL in a cell comes because there were NULLs in the source data and this means a group NULL, or there is a NULL in the cell because this is a hyper aggregate. For example, NULL in the Education column where the value of the GROUPING(Education) equals to 1 indicates that this is aggregated in such a way that education makes no sense in the context, for example aggregated over countries only, or over the whole dataset. I used ordering by NEWID() just to shuffle the results. I executed query multiple times before I got the desired order where all possibilities for the GROUPING() function output were included in the first few rows of the result set. Here is the result.

GROUPING SETS is an underappreciated bit of SQL syntax.

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OFFSET – FETCH Versus ROWNUM In Oracle

Published 2018-06-28 by Kevin Feasel

Lukas Eder compares the OFFSET FETCH logic versus using ROWNUM for grabbing an ordered sub-selection of rows in Oracle:

Now, while the SQL transformation from FETCH FIRST to ROW_NUMBER() filtering is certainly correct, the execution plan doesn’t really make me happy. Consider the ROWNUM based query:

---------------------------------------------------------
| Id  | Operation                     | Name    | Rows  |
---------------------------------------------------------
|   0 | SELECT STATEMENT              |         |       |
|*  1 |  COUNT STOPKEY                |         |       |
|   2 |   VIEW                        |         |     1 |
|   3 |    TABLE ACCESS BY INDEX ROWID| FILM    |  1000 |
|   4 |     INDEX FULL SCAN           | PK_FILM |     1 |
---------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   1 - filter(ROWNUM=1)

And compare that to the FETCH FIRST query:

-------------------------------------------------
| Id  | Operation                | Name | Rows  |
-------------------------------------------------
|   0 | SELECT STATEMENT         |      |       |
|*  1 |  VIEW                    |      |     1 |
|*  2 |   WINDOW SORT PUSHED RANK|      |  1000 |
|   3 |    TABLE ACCESS FULL     | FILM |  1000 |
-------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   1 - filter("from$_subquery$_002"."rowlimit_$$_rownumber"<=1)
   2 - filter(ROW_NUMBER() OVER ( ORDER BY "FILM"."FILM_ID")<=1)

Lukas digs into this and is not the biggest fan of OFFSET-FETCH.  On the SQL Server side, my anecdotal experience has been that it doesn’t perform nearly as well as you’d like either.

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Strings And Identifiers

Published 2018-06-21 by Kevin Feasel

Kenneth Fisher explains the difference between a string and an identifier:

A common mistake, and one I make frequently myself is to use a string in place of an identifier, or vise-versa. So to start, let’s have some definitions, shall we?

String

a linear sequence of characters, words, or other data.

Identifier

a sequence of characters used to identify or refer to a program or an element, such as a variable or a set of data, within it.

And because I always find examples fairly useful.

Click through for the example as well as additional explanation.

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Identity Columns And Linked Servers

Published 2018-06-15 by Kevin Feasel

Kenneth Fisher points out an oddity when inserting data across a linked server into a table with an identity column:

So far so good. Now let’s throw in a twist. Let’s call it through a linked server.

INSERT INTO [(local)\sql2014cs].Test.dbo.IdentTest 
	VALUES ('Col1','Col2');

Msg 213, Level 16, State 1, Line 4
Column name or number of supplied values does not match table definition.

Well that’s a bit odd, right? I mean I used that exact command in the previous test. Turns out that when you do an insert across a linked server that identity column is not ignored. Which means we just need to include the identity value right? Nope.

INSERT INTO [(local)\sql2014cs].Test.dbo.IdentTest 
	VALUES (1,'Col1','Col2');

Msg 7344, Level 16, State 1, Line 4
The OLE DB provider “SQLNCLI11” for linked server “(local)\sql2014cs” could not INSERT INTO table “[(local)\sql2014cs].[Test].[dbo].[IdentTest]” because of column “Id”. The user did not have permission to write to the column.

Click through to see how to do this.

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