Category Archives: SQL

SQL Macros – Some Less Obvious Facts (Part 2)

This second part of the “less obvious facts” series is about the COLUMNS pseudo-operator. It has been introduced in 18c as one of the variadic pseudo-operators, intended to operate with a variable number of operands. Unfortunately, no other pseudo-operators have been introduced since then, so that it is worth looking at what exactly the COLUMNS operator is, how to use it and how it can potentially be “misused”.

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SQL Macros – Some Less Obvious Facts (Part 1)

Since I have written a lot about SQL macros, I have tested some less obvious, sometimes surprising cases. But expanding about them in the original posts would just overload them, so I decided to make a dedicated post for that. The other good reason for this is that the SQL macros are, in my opinion, not documented in enough detail. So additional research and testing can be useful.

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Beware of NO_DATA_FOUND in your PTF!

A few days ago I received a comment/question on the older post about dynamically transposing rows to column with Polymorphic Table Functions (PTF). Back then I overlooked a bug in the example code, but the explanation takes a bit longer, so i decided to write a new post about it. The PTF was working initially but after inserting new data started to return wrong results – all NULL’s for some columns where we know there are actually values present. So, what’s going on?

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How to get the final SQL after macro expansion

In my very first post about SQL macros, I mentioned that for table macros, there is a simple way to see the SQL statement after macro expansion using dbms_utility.expand_sql_text. However, for scalar SQL macros, there is no such straightforward method. We can activate a CBO trace (also known as event 10053) and find the final statement in the trace file. This approach works for both scalar and table SQL macros. In this post, we will explore how to do this, and we will use… a SQL macro for that! Well, at least we will give it a try…

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Null Values? NOT IN my Subquery! #JoelKallmanDay

I had to learn from this mistake more than once in my SQL career, until it became a kind of reflex: always think about the NULL values when you are writing a NOT IN subquery. Well, you should ALWAYS keep the NULL values in mind as a SQL developer, but in this case the consequences can be really nasty! And it drives me crazy how many times I’ve seen this error in my consulting life, even in production code. So, what is the point here?

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What does ChatGPT “think” about SQL Macros?

After some weeks of reading and hearing in almost every news report about generative AI and ChatGPT in particular, and how it will change a developer’s life or even make developers obsolete, I was really curious to check it out myself and ask some developer questions. Since I’ve posted quite lot about SQL macros in the past few years, I thought aksing about them could be a good start point. One more argument to start with the topic I’m pretending to know lot about, is to be able to easily distinguish the truth from AI hallucination.

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Comparing Rows using a SQL Macro

After the previous post about building an UNPIVOT query via SQL macro to output table rows like key-value pairs, I thought of another use case for the UNPIVOT operator, where developing a reusable SQL macro will make sense. From time to time you need to spot the difference between almost identical rows. You know they are different and you can easily check this using MINUS or GROUP BY, but if you want to know in what column(s) exactly the difference is, you need another approach.

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Yet another PRINT_TABLE – as a SQL Macro!

A few weeks ago Jonathan Lewis has published a note about Tom Kyte’s print_table – a small PL/SQL procedure to output each row in a table as a list of (column_name , value). And since this note has gained some comments with other implementations, here is my contribution. Guess how? Of course with a SQL macro.

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