Joins Versus NOT IN Clause

Kevin Hill explains a potential performance difference between using NOT IN and using a left join:

Basic stuff, right?   Both will return 951 records (books) that I do not own.  And, very quickly…because the tables are tiny.   Sub-1 second is fast.

The issue here is HOW the rows are compared.

English version now, techy stuff later:

In the first query, this is equivalent to you standing at the bookstore and calling home to have someone check to see if the book in your hand is already in your collection.  EVERY time.  One by one.

In the second, you got really smart and brought a list with you, which you are comparing to the books on the shelf at the store.   You’ve got both “lists” in one place, so it is far more efficient.

Even in the case with a few hundred records, you can see why there’d be a performance difference.

While Loops

Kevin Feasel

2016-08-08

Syntax

Lukas Eder discovers that Oracle’s PL/SQL has WHILE loops:

In SQL, everything is a table (see SQL trick #1 in this article), just like in relational algebra, everything is a set.

Now, PL/SQL is a useful procedural language that “builds around” the SQL language in the Oracle database. Some of the main reasons to do things in PL/SQL (rather than e.g. in Java) are:

  • Performance (the most important reason), e.g. when doing ETL or reporting
  • Logic needs to be “hidden” in the database (e.g. for security reasons)
  • Logic needs to be reused among different systems that all access the database

Much like Java’s foreach loop, PL/SQL has the ability to define implicit cursors (as opposed to explicit ones)

The WHILE loop is a little more helpful in the SQL Server world for doing things like deleting lots of rows in small batches, but I agree with Lukas’s sentiment:  if you start writing a WHILE loop, it’s best to sit back and think about whether this is the best decision.

Common Table Expressions Aren’t Tables

Grant Fritchey shows that CTEs are not tables; they’re expressions:

The Common Table Expression (CTE) is a great tool in T-SQL. The CTE provides a mechanism to define a query that can be easily reused over and over within another query. The CTE also provides a mechanism for recursion which, though a little dangerous and overused, is extremely handy for certain types of queries. However, the CTE has a very unfortunate name. Over and over I’ve had to walk people back from the “Table” in Common Table Expression. The CTE is just a query. It’s not a table. It’s not providing a temporary storage space like a table variable or a temporary table. It’s just a query. Think of it more like a temporary view, which is also just a query.

Read the whole thing.

CTEs And Semi-Colons

Jason Brimhall discusses common table expressions:

Wait! Hold on two seconds there! Surely the semi-colon is an absolute requirement because we see it everywhere that it is a mandatory requirement.

The reality is that the semi-colon requirement is not really entirely accurate. If the CTE happens to be in the same batch, then the previous statement in the batch must be terminated by the semi-colon.

This post went down an unexpected path, and ended up being rather interesting.  Read the whole thing.

Comparing Nullable Columns

Daniel Hutmacher shows an elegant way to compare multiple nullable columns on two tables:

Because we’ve added OR conditions into the mix, we’re forced to use the Nested Loop join, which loops over table B for every single row in A. That’s a lot of index scans and it comes with a hefty price tag.

Here’s an absolutely eye-watering beautiful pattern that I found on the Interwebs (though I forgot where) the other day.

This is an interesting use of INTERSECT.  Check it out.

Quantified Comparsion Predicates

Kevin Feasel

2016-07-13

Syntax

Lukas Eder discusses the ALL, SOME, and ANY predicates:

Ultimately, you should always choose performance first, and then – most certainly – intuitiveness second (because some poor soul might need to maintain your query). But personally, I find these quantifiers quite elegant for three reasons:

  1. They express the quantification right where it belongs. With the comparison operator. Compare this with the solution using LIMIT, which may be far away, visually, from the greater-than operator. Quantifiers are much more concise, even than when using MAX() (in my opinion)

  2. They’re very set oriented. I like thinking in terms of sets when I work with SQL. Whenever I can omit the ORDER BY clause, I will. If only to avoid potentially slow operations (in case the database doesn’t optimise this, and a full O(N log N) sort operation is invoked)

  3. Quantified comparison predicates work on rows too, not just on single values.

I’ve known about these, but could probably count on one hand the number of times I’ve ever used one.

Unpivoting With APPLY

I have a post on using the APPLY operator to unpivot data:

This code works, but if you have dozens of years, it gets messy writing those case statements and you’re a bit more likely to make a mistake when refactoring code. Here’s a simpler version using CROSS APPLY:

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SELECT
    s.Product,
    y.[Year],
    y.Quantity,
    y.[Value]
FROM #Sales s
    CROSS APPLY (VALUES
        (2013, [Qty2013], [Val2013]),
        (2014, [Qty2014], [Val2014]),
        (2015, [Qty2015], [Val2015])
    ) y([Year], Quantity, [Value]);

It’s a little easier to read than the other version, and adding additional years is pretty straightforward.  That makes for a great tip when you’re trying to refactor poorly-thought-out tables or bring into your system potentially well-thought-out flat files.

APPLY is an elegant solution to so many different classes of problem.

Basic XML Querying

Kevin Feasel

2016-07-01

Syntax

Steve Jones discusses a couple peculiarities of XML querying within SQL Server:

This doesn’t work.

The reason this doesn’t work is that XML is case sensitive. Meaning ORDERID != OrderID. The former is in the query, the latter in the XML document. If I change the query, this works (note I have OrderID below).

Like Steve, I’m not a big fan of doing XML processing within SQL Server, but if it’s a necessary part of your workload, it’s worth knowing.

Watch Those Parentheses

Kenneth Fisher shows how to see open and close parenthetical locations:

You’ll notice that when I go over the parentheses the one I’ve selected and it’s pair turn yellow, unless there isn’t a pair of course. You can also use Ctrl-] to flip between the open and close parenthesis in a pair. This can be particularly useful to make sure that you remembered a close parenthesis at the end of a subquery. In this case that last close parenthesis doesn’t have a match. Now finding out that you are missing an open parenthesis doesn’t mean you know where it’s supposed to go. But you can track the different pairs, making sure that each time you open a parenthesis you close it in the correct place. In this case it belonged right at the beginning.

FYI yellow isn’t the default (it’s a light gray). I find the default hard to see (I’m getting old) so I changed it to yellow in the options under fonts and colors.

Read the whole thing.

LEN Is For Strings

Kenneth Fisher notes that the LEN function can behave oddly on non-string data types:

Which show you that the FLOAT had to be converted to VARCHAR. You can see the same thing if you try it with various versions of INT or DATE datatypes as well. Like I said earlier. No big deal with INT or even DATE. Those come back in a fairly expected format. (INTs look exactly the same and DATEs come back as ‘YYYY-MM-DD’). FLOAT and REAL however are floating point so they don’t always convert the same way. If you do the conversion deliberately you get this:

Understand your data types; otherwise, it might come back to hurt you later.

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