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Category: Data Types


Max Vernon gives us three ways to change data types:

PARSE provides a mechanism to convert a wide variety of character based dates into a datetime data type. From the Docs:

Returns the result of an expression, translated to the requested data type in SQL Server.

Use PARSE only for converting from string to date/time and number types. For general type conversions, continue to use CAST or CONVERT. Keep in mind that there is a certain performance overhead in parsing the string value.

PARSE has its value, but comes at a cost, as Max shows.

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Data Type Conversions in Predicates

Bert Wagner takes us through a troublesome table design:

This table stores data for an application that has many different types of Pages. Each Page stores different types of data, but instead of creating a separate table for each type, we store all the different data in the varchar DataValue column and maintain the original data type in the DataType column.

This structure reduces the complexity required for maintaining our database (compared to creating possibly hundreds of tables, one for each PageName) and makes querying easier (only need to query one table). However, this design could also lead to some unexpected query results.

This is your daily reminder that an attribute should be a thing which describes an entity, not one of multiple things.

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Explaining Implicit Conversion

Monica Rathbun explains to us what implicit conversion is and when it can go wrong:

Another quick post of simple changes you can make to your code to create more optimal execution plans. This one is on implicit conversions. An implicit conversion is when SQL Server must automatically convert a data type from one type to another when comparing values, moving data or combining values with other values. When these values are converted, during the query process, it adds additional overhead and impacts performance.

Read on for more info, including a common scenario where implicit conversion causes performance degradation.

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Max Vernon shows us how to use the ROWVERSION data type to tell how much work you have to do to ETL data over from one table to another:

The OLTP table implements a rowversion column that is automatically updated whenever a row is updated or inserted. The rowversion number is unique at the database level, and increments monotonically for all transactions that take place within the context of that database. The dbo.OLTP_Updates table is used to store the minimum row version available inside the transaction used to copy data from the OLTP table into the OLAP table. Each time this code runs it captures incremental changes. This is far more efficient than comparing all the rows in both tables using a hashing function since this method doesn’t require reading any data other than the source data that is either new, or has changed.

I think this is the first time I’ve seen someone use ROWVERSION types successfully.

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Valid WAITFOR Data Types

Dave Mason investigates the valid data types you can use as inputs for WAITFOR:

There are certain design patterns in T-SQL that give me pause. They may not be “code smells” per se, but when I encounter them I find myself thinking “there’s got to be a more sensible way to accomplish this”. WAITFOR DELAY is one example. I’ve used it a few times here and there, mostly in one-off scripts. If I wrote some code that used it regularly in production, I’d be paranoid about putting my SPID to sleep forever. Maybe a little bit of paranoia is a good thing. But I digress.

A recent task found its way to me, and I’ve decided to use WAITFOR DELAY as part of my solution. (It hasn’t been tested or implemented yet–perhaps more on this in another post.) My usage this time has been more complex than in the past. What I already knew is that you can use a string literal for the time_to_pass argument. For example, this will delay for 3½ seconds:

WAITFOR DELAY '00:00:03.500'

Click through for a bunch of testing.

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Thoughts On UTF-8 Encoding In SQL Server 2019

Solomon Rutzky digs into UTF-8 support in SQL Server 2019 and has found a few bugs:

Let’s start with what we are told about this new feature. According to the documentation, the new UTF-8 Collations:

  1. can be used …

    1. as a database-level default Collation
    2. as a column-level Collation
    3. by appending “_UTF8” to the end of any Supplementary Character-Aware Collation (i.e. either having “_SC” in their name, or being of level 140 or newer)
    4. with only the CHAR and VARCHAR
  2. (implied) have no effect on NCHAR and NVARCHAR data (meaning: for these types, the UTF-8 Collations behave the same as their non-UTF-8 equivalents

  3. “This feature may provide significant storage savings, depending on the character set in use.” (emphasis mine)

Solomon takes his normal, thorough approach to the problem and finds several issues.

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Parsing Numeric Values From Multiple Cultures

Bert Wagner shows us a good way of converting strings to numbers when multiple cultures are in play:

Why are the salaries stored as nvarchar and formatted with commas, spaces, and periods?

Great question!  Someone wanted to make sure these amounts would look good in the UI so storing the formatted values in the database would be the way to go…

Pretty for the UI, not really great for needing to do analysis on.

Admittedly, TRY_PARSE is slow, but for a one-time conversion as you try to fix a bad idea, it’s fine.

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