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Category: Data Types

Resulting Data Types from a UNION Operation

Andy Brownsword puts on the lab coat and performs some experiments:

The UNION and UNION ALL operators allow us to combine results, but there’s no guarantee that each set of results uses the same data types. So what data types are returned?

For the longest time I thought the data types from the first set of results were used for the final results. That’s not the case.

Read on to see what the rules look like.

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Domains in ANSI SQL

Joe Celko describes a domain:

For example, if there though is that there is a domain called voltage which has a base unit called “volt” that’s otherwise meaningless. Yes, you can get a voltmeter you can watch the needle, you can be told what the IEEE specification for defining how much work a volt should do or shock you. I’ve discussed scales and types of measurements in a previous article, It’s worth mentioning that you should not confuse domain with the representation and symbols of the units being used. Some domains are limited, such as degrees that measure planar angles. An angle can be from 0 to 360°, or it can be between zero and 2π radians.

Joe has an explanation but doesn’t have any concrete examples in psql. Here’s one from the PostgreSQL documentation:

CREATE DOMAIN us_postal_code AS TEXT
CHECK(
   VALUE ~ '^\d{5}$'
OR VALUE ~ '^\d{5}-\d{4}$'
);

The idea of a domain here is that you define a valid slice of some data type. We can do something similar with check constraints on an attribute, but the difference is that we’d need to create the check constraint for each relevant attribute, whereas the domain would include this check automatically, making it quite useful if we have multiple instances of, say, us_postal_code in our database. Then, we wouldn’t need to worry about creating a check constraint on each instance and ensuring that the code remains the same across the board.

This also leads to a very common sentiment in functional programming: make invalid states unrepresentable. In other words, make it impossible for a person or piece of code to generate a result in an invalid state. By defining a domain with the scope of our valid state, we make it impossible for someone to create a US postal code value that does not pass our check, and so we can’t have dirty data of this sort in our database.

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Integer Conversion and Rounding in SQL Server

Steve Jones points out a bit of rounding math:

Imagine that I have someone enter a value for the number of hours to include in a report. I enter 5 and the report divides this in half to go back 2.5 hours and forward 2.5 hours. I run this code at the top of my code block:

Click through for Steve’s example. This ultimately has to do with integer division. If you run the following code, you’ll still get 2 as the result:

SELECT CAST(5.99 / 2) AS INT;

This is because SQL Server discards the decimal during integer casting. DATEADD() simply works with the end result, post-cast.

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String Data Types in MySQL and PostgreSQL

Aisha Bukar compares two products:

A very common task in creating a database is to store string data. For example, words, paragraph(s) or even documents. String data types allow you to do just that and store and represent text. They handle everything from simple names and addresses to complex data.

A string is simply a sequence of characters. These characters can be letters, numbers, symbols, or even spaces. For example, “Simple Talk”, “MySQL and PostgreSQL”, “1234” are all strings. Think of each character as a building block. A string is made up of these blocks, arranged in a specific order.

As always, when dealing with different data platform technologies, the small differences are big.

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SQL Server Views and Implicit Data Types

Kendra Little takes a peek at a view:

Views let you do dumb things by accident in SQL Server. Then they make you have to think way too hard to fix them.

Most of the time when people create views, they start by refining a SELECT query, then turn it into a view. People also often create multiple views that pull different slices of data and UNION the results together.

Combined, these two things easily lead to undeclared datatypes in views with problematic implicit conversions.

Read on for an example of this problem in action. Kendra’s example involved a view and a separate table, but you can also see this kind of thing pop up in a view that itself contains set operators like UNION.

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Calculating the Distance between Points via T-SQL

Sebastiao Pereira gets out the measuring tape:

How do you calculate the distance between two different points on a sphere using TSQL in SQL Server? In this article, we look at how this can be done to calculate different points on the globe.

Sebastiao first shows the raw calculation, then uses the GEOGRAPHY data type to simplify the job. Note that these are “as the crow flies” distances and not travel distances, as there’s no information on roads.

Also, these are calculations specific to Earth. Which sounds like the setup for a joke, but it’s really not. If you have a smaller sphere (or oblong spheroid, if you will) and you want to calculate the distance, use the GEOMETRY data type instead.

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The JSON Data Type in Azure SQL DB

Koen Verbeeck takes a peek at what’s coming in SQL Server 2025:

We have data coming into our database from a REST API endpoint. The data is formatted as JSON documents. Is there an efficient way to handle JSON data within the SQL Server ecosystem? In this article, we look at the new JSON data types for SQL Server.

The end result is not particularly clear-cut here: reads slightly faster, writes considerably slower. There aren’t any indexes on the data type at this time, so no opportunity for improvement there.

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Entity Framework and Default Data Lengths

Brent Ozar points out one issue you might run into when using Entity Framework:

Most of the time, I love Entity Framework, and ORMs in general. These tools make it easier for companies to ship applications. Are the apps perfect? Of course not – but they’re good enough to get to market, bring in revenue to pay salaries, and move a company forwards.

However, just like any tool, if you don’t know how to use it, you’re gonna get hurt.

One classic example popped up again last month with a client who’d used EF Core to design their database for them. The developers just had to say which columns were numbers, dates, or strings, and EF Core handled the rest.

Read on for the scenario.

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Comparing Numeric Data Types between MySQL and PostgreSQL

Aisha Bukar compares data types:

Numeric data types serve as a foundation in DBMS. It helps represent numbers in different forms which helps in storing and manipulating arithmetic operations on numerical data.

When dealing with numeric data types in databases like MySQL and PostgreSQL, it’s important to understand key concepts like range, precision(fixed-point numbers, floating-point numbers), and storage. This helps you understand how data is stored, processed, and retrieved.

Read on for quick descriptions of the three primary properties of numeric data types (range, precision, and storage), as well as how the two relational database platforms handle numeric data.

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