Joining on NULLs

Published on: 2019-03-26

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It’s important to be aware of columns that allow NULL values since SQL Server may handle NULLs differently than you might expect.

Today I want to look at what things to consider when joining on columns containing NULL values.

Natural, Composite, NULLable keys

Let’s pretend we have an Account table containing the accounts of various users and an AccountType table describing the different types of accounts:

Account and AccountType tables

These tables have the unfortunate design characteristics of:

  1. They use a natural, composite key of YearOpened and AccountType
  2. NULL is the valid default for AccountType

Not that either of the above attributes are outright bad, just that we need to handle them appropriately. For example, if we want to bring back a description of each user’s account, we might write a query with an inner join like this:

SELECT
    a.UserId,
    at.YearOpened,
    at.AccountType,
    at.Description
FROM
    dbo.Account a
    INNER JOIN dbo.AccountType at
        ON a.YearOpened = at.YearOpened
        AND a.AccountType = at.AccountType

Only to discover the rows with NULLs are not present:

Inner join with no NULLs

Joining on NULLs

Since it’s not possible to join on NULL values in SQL Server like you might expect, we need to be creative to achieve the results we want.

One option is to make our AccountType column NOT NULL and set some other default value. Another option is to create a new column that will act as a surrogate key to join on instead.

Both of the above options would fix the problem at the source, but what about if we can only make changes to our queries?

One common approach is to convert the NULLs to some other non-NULL value using a function like COALESCE or ISNULL:

SELECT
    a.UserId,
    at.YearOpened,
    at.AccountType,
    at.Description
FROM
    dbo.Account a
    INNER JOIN dbo.AccountType at
        ON a.YearOpened = at.YearOpened
        AND ISNULL(a.AccountType,'`') = ISNULL(at.AccountType,'`')
Correct results with ISNULL

While this returns the results we want, there are two major issues with this approach:

  1. In the above example we converted NULLs to the ` character. If we had a valid ` character in our data, we would get logically incorrect joins.
  2. Our query can no longer perform index seeks.

The first issue isn’t a huge deal if you can guarantee the character you are replacing NULLs with will never appear in the column of data.

The second issue is more important since ISNULL prevents your query from being SARGable and will cause poor performance on large tables of data.

SARG Killer

Those Compute Scalar operators are forcing SQL Server to Scan the indexes and compute a value for every row.

A More Efficient Solution

If using a function like ISNULL hurts the performance of our queries, what can we do instead?

SELECT
    a.UserId,
    at.YearOpened,
    at.AccountType,
    at.Description
FROM
    dbo.Account a
    INNER JOIN dbo.AccountType at
        ON a.YearOpened = at.YearOpened
        AND (a.AccountType = at.AccountType OR (a.AccountType IS NULL AND at.AccountType IS NULL))
Correct results with efficiency

This produces the same exact results while allowing SQL Server to Seek when possible and avoid costly row by row computations:

There are no seeks here since I don’t have any additional filters, but the lack of Compute Scalar operators should be enough to prove the point.

While there are a few more variations that can achieve the same results using different execution plans (writing a query that joins non-nulls and unioning it with a query that selects only the nulls, using a computed column to convert the NULLs to non-null values, etc…) the key to good performance is to choose a solution that will not force SQL Server to compute values for every single row.

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4 Reasons To Avoid VARCHAR(8000)

Published on: 2019-03-19

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When first learning SQL Server, I had the brilliant idea of defining every column on my table as VARCHAR(8000). After all, the VARCHAR datatype only consumes as much storage space as the string itself (plus two bytes), so even if I defined the max length of a column as 8000, I wouldn’t be wasting storage space if my data was smaller than that.

My development life would be easier too since I wouldn’t have to think about the kind of data I was storing in my columns; I could define everything as VARCHAR(8000) once and never have to go back to make any changes. Brilliant!

While I was correct about not wasting storage space, it turns out the idea of making every column VARCHAR(8000) is a terrible idea.

Continue reading “4 Reasons To Avoid VARCHAR(8000)”

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Gaps and Islands Across Date Ranges

Published on: 2019-03-12

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In a traditional gaps and islands problem, the goal is to identify groups of continuous data sequences (islands) and groups of data where the sequence is missing (gaps).

While many people encounter gaps and islands problems when dealing with ranges of dates, and recently I did too but with an interesting twist:

How do you determine gaps and islands of data that has overlapping date ranges?

Continue reading “Gaps and Islands Across Date Ranges”

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