Predicate Execution Order on Mixed Data Type Columns

Published on: 2019-05-21

Watch this week’s episode on YouTube.

SQL Server’s cost-based query optimizer does a pretty good job of figuring out what order to filter your data to get fast query executions. It considers things like index coverage, data distribution, and much more to decide how to retrieve your query’s data.

However, these good intentions can become problematic in certain situations where you know more about your data than SQL Server does. When this happens, the order SQL Server chooses to execute predicates is important not just for performance of your query but for the business logic as well.

A Column With Mixed Data Types

Let’s look at the following example table and data:

USE master;
USE MixedDatatypes;

	Id int identity,
	PageName varchar(20),
	DataValue varchar(100),
	DataType varchar(20),
INSERT INTO dbo.Pages VALUES ('StringsOnlyPage 1','abc','string')
GO 2000
INSERT INTO dbo.Pages VALUES ('NumbersOnlyPage 1','1.20','decimal')
GO 2000
INSERT INTO dbo.Pages VALUES ('NumbersOnlyPage 2','1.20','decimal')
GO 2000
INSERT INTO dbo.Pages VALUES ('MixedDataTypesPage 1','abc','string')
GO 1000
INSERT INTO dbo.Pages VALUES ('MixedDataTypesPage 1','1.20','decimal')
GO 1000

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.

Filtering Mixed Data Values

Let’s say we want to retrieve all data from one table with where the value is 1.2:

SELECT PageName,DataValue
FROM dbo.Pages
WHERE PageName = 'NumbersOnlyPage 1' AND DataValue = '1.2'

This query runs fine. The problem is since our original data type was a decimal with a value of 1.20, this string-based comparison doesn’t work. What we really want to have happen is a numeric comparison in our predicate:

SELECT PageName,DataValue
FROM dbo.Pages
WHERE PageName = 'NumbersOnlyPage 1' AND DataValue = 1.2
Implicit Conversions

While the implicit conversion occurring on the table’s DataValue column is not ideal, if the number of rows it needs to convert is small it’s not so bad (plus, this isn’t the point of today’s post, so try and look past it for a few more moments).

Here comes the fun: what if we want to check all our Pages that contain numeric data for values of 1.2? We could write this query in a couple of different ways:

SELECT PageName,DataValue
FROM dbo.Pages
WHERE PageName like 'NumbersOnlyPage%' AND DataValue = 1.2
SELECT PageName,DataValue
FROM dbo.Pages
WHERE PageName in ('NumbersOnlyPage 1','NumbersOnlyPage 2') AND DataValue = 1.2

For both queries, we receive the error “Error converting data type varchar to numeric”.

Error converting data type varchar to numeric

Why? In this case SQL Server decides to do the implicit conversions on the DataValue column first before filtering on our PageName columns.

Up until this last query, SQL Server was deciding that it would be more efficient to filter the rows down to the specific Page first and then do the implicit conversions on the DataValue column. However, now that we are selecting more than one table, SQL Server says determines it has to scan everything anyway, it might as well do all of the implicit conversions first and filter on table names later.

The problem of course is that all our DataValue values are not numeric. In this case the order of the predicates does matter, not for performance but to be able to correctly execute the business logic that we defined as part of our query.

Not Good Solutions

One way we can fix this is to tempt SQL Server to filter on PageName first by adding an index:

CREATE NONCLUSTERED INDEX IX_PageName ON dbo.Pages (PageName) INCLUDE (DataValue);

SELECT PageName,DataValue
FROM dbo.Pages
WHERE PageName like 'NumbersOnlyPage%' AND DataValue = 1.2

This works great. SQL Server decides that since this index covers all the fields in our query, and because the index key is PageName, it will filter the rows on PageName first and perform the implicit conversions on the remaining rows.

The problem with this is that it’s not guaranteed. Something may happen that will cause SQL Server not use this index in the future: our index doesn’t cover our query anymore, we add some additional filtering, the index is removed so it can be replaced by a different index that will no longer be selected for this particular query, etc…

It just isn’t a reliable option.

Plus it doesn’t work in all scenarios. Let’s say we parameterize the PageName and use the STRING_SPLIT() function to filter our Pages to only those passed in:

DECLARE @PageNames varchar(100) = 'NumbersOnlyPage 1,NumbersOnlyPage 2';
FROM dbo.Pages
WHERE PageName in (SELECT value FROM string_split(@PageNames,',')) AND DataValue = 1.2

We are back to square one since in this case STRING_SPLIT() needs to parse the PageName data first and then join it in with the rest of the data, causing our original failure scenario (this is the estimated execution plan):

estimated table split

Other Options

So while indexing seems to fix the solution, it’s not guaranteed to work 100% of the time.

Obviously we could not store data in this format, but that would add complexity to the database and app.

We could try to add the PageName filter into a derived table and force the join order, but that’s ugly and will force us to read the table multiple times.

Since we also have data type information available for each row, we might consider utilizing that information:

SELECT PageName,DataValue
FROM dbo.Pages
WHERE PageName like 'NumbersOnlyPage%' AND DataValue = 1.2 AND DataType = 'decimal'

But once again if this works it’s through sheer luck.

TRY_CONVERT() is another option. This function returns nulls if it can’t convert to a decimal:

SELECT PageName,DataValue
FROM dbo.Pages
WHERE PageName like 'NumbersOnlyPage%' AND TRY_CONVERT(decimal(2,1),DataValue) = 1.2 

This is actually a pretty good option since it’s guaranteed to work regardless of which column SQL Server filters on first. If the number of DataValues you have to TRY and CONVERT is relatively small though, this may be your best choice.

For better performance, you can create a second column that contains data in decimal (or any other type) format:

ADD DataValueDecimal AS TRY_CONVERT(decimal(2,1),DataValue) PERSISTED

You could index both DataValue* columns and your performance would be pretty good. The downside here of course is that your app queries will have to change to match the new table structure:

SELECT PageName,DataValue
FROM dbo.Pages
WHERE PageName like 'NumbersOnlyPage%' AND CASE WHEN DataType = 'decimal' THEN DataValueDecimal ELSE DataValue END = 1.2 

In conclusion, it’s tough to say what the best option is for this type of scenario. However, it’s important to keep in mind that if you decide to structure and write your queries in this format, you need to plan for order of operation issues and handle errors gracefully.

Thanks for reading. You might also enjoy following me on Twitter.

Want to learn even more SQL?

Sign up for my newsletter to receive weekly SQL tips!

Data Type Precedence and Implicit Conversions

Published on: 2019-04-09

Watch this week’s episode on YouTube.

SQL Server needs to make sure data types match when performing operations that involve multiple pieces of data.

When the data types do not match, SQL Server has to implicitly convert the data before performing any operations.

While most of the time these implicit conversions go unnoticed, they are important to understand since they can lead to unexpected results.

When 4.4/.44 Doesn’t Equal 10

Let’s start with this example:


Ignoring for a moment that our denominator is of type VARCHAR, if we do some quick mental math or use a calculator, we can see that the answer should be 10:

Calculating some simple math

However, if we look at the result SQL Server returns, it’s strangely 11:

Eleven? Why eleven?

To understand why this happens, we need to understand SQL Server’s data type precedence logic.

Data Type Precedence

If we start with a simpler version of this example, we’ll see SQL Server does in fact know how to perform math and return an answer of 10:

SELECT 4.4/.44

We can use the SQL_VARIANT_PROPERTY() function to see what data types SQL Server is assuming we are using in our calculation:

Data types

In this case, since the data types of both components are numeric, SQL Server doesn’t have to break a sweat making any drastic conversions to give us our expected answer. If we instead give it something a little more challenging:

Still a 10

Here we still return the result of 10, however SQL Server had to do a little more work. We know from the previous example that 4.4 on its own is of type numeric, and in this example we are explicitly casting .44 to a float. SQL Server isn’t able to perform the division operation directly on two different data types, so it refers to its data type precedence table to decide which value to convert to a matching datatype.

In the linked table above, the float data type appears higher on the list than numeric (synonym: decimal) data type. This causes SQL Server to convert our numeric 4.4 to a float before dividing.

While SQL Server is doing extra work behind the scenes that we didn’t explicitly request, we can’t be too angry with it since it still is giving us the “correct” answer.

“Incorrect” Conversions

Let’s look at something a little more dastardly:


You might think this should also return 10 based on the previous example, but in fact it returns 9.090909:

Not quite 10

While we are still witnessing implicit conversion here (the numeric gets converted to a float in order to allow SQL Server to perform the division), we are also experiencing a case of default data type precision and scale. If we use the SQL_VARIANT_PROPERTY() function again to not only reveal base type but also precision and scale, we’ll notice that when we let SQL Server “guess” the scale, it correctly chooses 1 decimal place, while when we use the default scale associated with numeric we get 0:


Two numerics with different precisions and scales

What this really means is that the decimal portion of 4.4 is getting chopped off, leaving us with an equation of 4 / .44 = 11.

Putting Everything Together

So back to our original example that returns 11:


What exactly is going on here? Well for starters, data type precedence is forcing SQL Server to convert the VARCHAR .44 to a numeric. But a numeric with what precision and scale?

Precision 2, scale 1

Since SQL Server determines that our numerator is NUMERIC(2,1), it converts the denominator to a NUMERIC(2,1) as well. This means instead of dividing by .44, we end up dividing by .4 which results in 11:

-- An explicit version of the same calculation

SQL Server Isn’t Wrong

While it’s easy to blame SQL Server for not knowing how to perform simple mathematical operations, it’s us the developers who are to blame. SQL Server is a piece of software following rules for how to handle unclear situations that we input into it.

Having SQL Server throw an error instead of assuming data types and implicitly converting data on our behalf would make things less ambiguous, but it would also make simple operations a lot more tedious (looking at you SSIS).

In reality, the best solution is to accurately define data types from initial creation and to always be explicit with data types when the value of your computations matters.

Thanks for reading. You might also enjoy following me on Twitter.

Want to learn even more SQL?

Sign up for my newsletter to receive weekly SQL tips!

Testing with Temporary Stored Procedures

Published on: 2019-04-02

Watch this week’s episode on YouTube.

A while back I learned that it’s possible to create¬†temporary stored procedures in SQL Server.

I never put that knowledge into practice however because I struggled to think of a good use case for when a temporary stored procedure would be preferable to a permanent stored procedure.

Not long ago I encountered a scenario where using a temporary stored procedure was the perfect solution to my problem.

Building New Tables

Recently, I had to build a new version of a legacy table because the legacy table’s upstream data source was going to be retired.

The new table would contain all the same data as the legacy table, but populated from the new data source. Additionally, the new table would also include additional rows and columns. After building the final table, the plan was to create a view to replace the functionality of the legacy table.

I had to spend quite a bit of time writing a fairly elaborate query to make the data for the new data source match what was appearing in the legacy table (remember my gaps and islands post from a few weeks back? Imagine that on steroids).

Before building out the new ETL and tables though, I wanted to be able to test that the new query was producing the correct results. This was challenging because some of the data sources were on other servers. I was in a catch 22: couldn’t test the data because I didn’t build the ETL yet, but I didn’t want to build the ETL until I tested the results.

Less Than Ideal Options

To make matters worse, I was only able to test my query using data in the production environment.

I thought of putting my new query into a stored procedure to make the logic easier to test. But with the production elevate process being length and restricted, I couldn’t easily put my parameterized query into a permanent stored procedure to test my query with.

At this point I had a few options:

  • Don’t test anything and just build out the tables and ETL in production. Cross my fingers and hope it all works, fixing any issues after the fact.
  • Create a permanent stored procedure with the query and elevate it to production. Hope that I don’t have to make changes and go through the slow elevate process again.
  • Run the query over and over again with different parameters.

I didn’t like the first two options because of the amount of time I would lose trying to elevate new tables or procedures into production.

The third option wasn’t ideal either because while it would allow me to iterate quickly, documenting all of my tests would involve a massive file that would not be easy to navigate or change.

What I needed was a way to run a query through many different parameters in a concise manner without making any permanent production changes.

Temporary Stored Procedures for Regression Testing

Since I have access to create temporary stored procedures in production, I was able to create a temporary procedure containing my complex query:

CREATE PROCEDURE #ComplexBusinessLogic
  @parm1 int
  /* This isn't the actual query.  The real one was ugly and hundreds of lines long.*/
  SELECT CASE @parm1
    WHEN 1 THEN 'A'
    WHEN 2 THEN 'B'
    WHEN 3 THEN 'C'
    END AS ResultValue

As I mention in the comment of the stored procedure, this query was huge. If I wanted to execute it multiple times with different parameter values to test with, I’d have a gigantic file that would be difficult to navigate and easy to make errors in.

Instead, the temporary stored procedure made it easy to document my tests and execute the query as many times as needed:

/* Test for when parameter is 1 */
EXEC #ComplexBusinessLogic @parm1 = 1;

/* Test for scenario 2 */
EXEC #ComplexBusinessLogic @parm1 = 2;

/* The rarely occuring but very important test scenario 3 */
EXEC #ComplexBusinessLogic @parm1 = 3

I was able then to clearly define all of my tests and run them against the production data without creating any permanent production objects. This was great because I did find errors with my logic, but I was able to fix them and retry my tests over and over again until everything ran without issues.

Testing in Production

Ideally I wouldn’t have to use this solution. It would have been much better to have data to test with in a non-production environment. Using a temporary stored procedure to test in production is a hack to get around environment restrictions.

However, what is ideal and what is real-world doesn’t always align. Sometimes a hack helps meet deadlines when better options aren’t available. In this instance, temporary stored procedures helped make testing a breeze.

Thanks for reading. You might also enjoy following me on Twitter.

Want to learn even more SQL?

Sign up for my newsletter to receive weekly SQL tips!