Category: Data Types

Chris Johnson shares some rules of thumb on when allowing a column to be nullable makes sense:

Now I want to talk a bit about when we should be using NULLs and when we shouldn’t, inspired again by some things I’ve seen in legacy code a few days ago. And when I say using, I mean when should we allow values to be NULL, in tables, and parameters, and anything else.

The first thing to remember is what does a NULL represent. It’s very simple, NULL means “I don’t know”. If you keep that in mind, and you’re practising some good development design where everything has a point, you should be able to tell if it’s possible to be unsure about any particular value for a row.

One of the tricky parts about NULL values and legacy code is that if I need to add a new column and there is not a good default, either I make the column nullable (regardless of whether it should be) or I take downtime by blocking table access until my change is in place. As a result, quite often, I simply need to make something nullable because I can’t afford to block the table that long.

Michael J. Swart finds a creative way around a problem, and then a creative way around the problem it causes, and so on:

The table has over 2 billion rows and it looks like it’s going to run out of space soon because the LogId column is defined as an INT. I need to change this table so that it’s a BIGINT. But changing an INT to a BIGINT is known as a “size of data” operation. This means SQL Server has to process every row to expand the LogId column from 4 to 8 bytes. But it gets trickier than that.

The biggest challenge is that the table has to remain “online” (available for queries and inserts).

Compression?
Gianluca Sartori (spaghettidba) had the idea of enlarging the columns with no downtime using compression. It’s promising, but I discovered that for this to work, all indexes need to be compressed not just the ones that contain the changed column. Also, any indexes which use the column need to be disabled for this to work.

Cheating
I gave up on solving this problem in general and constrained my focus to the specific problem I was facing. There’s always some context that lets us bend the rules. In my case, here’s what I did.

Read the whole thing. Also, as soon as I saw Michael’s title, I immediately thought of this:

Randolph West continues a series on SQL Server data type storage:

If an integer or decimal amount is a precise representation of a value, a floating point is the closest approximation of that value in binary. Programming languages and databases use floating point numbers to trade storage (and memory) costs against precision. A floating point value is imprecise, but even that is underselling the problem.

Randolph also breaks all of the rules and writes out the largest FLOAT value you can have.

Randolph West thinks about ways to store money values in SQL Server:

I completely agree with this statement. Never store values used in financial calculations as floating point values, because a floating point is an approximate representation of a decimal value, stored as binary. In most cases it is inaccurate as soon as you store it. You can read more in this excellent — if a little dry — technical paper.

With that out of the way, we get into an interesting discussion about the correct data type to store currency values.

Randolph states an argument around why DECIMAL(19,4) is fine. And it’s great for most cases, though the one “real” financial system I’ve worked with have money stored as integer types (with SQL Server, that’d be BIGINT) because of precision, especially when working with exchange rates. But for most cases—especially when you’re not building the system of record for financial transactions or accounts—I agree with Randolph that DECIMAL is fine. Dave Wentzel has a great comment explaining even further the reasoning behind integer values for certain monetary columns.

Randolph West wants to talk to us about how we use our MONEY:

While MONEY and DECIMAL(19,4) are functionally the same, they are stored differently on disk, and this is where it gets interesting. Let’s use a random amount of $ 4,513.19. Since it’s small enough to fit in both MONEY and SMALLMONEY, we can do a simple experiment. When we ask SQL Server to store this value in a MONEY data type, it will store it (byte-reversed) as 0x7CA8B00200000000. The SMALLMONEY version of this amount would look almost identical, stored as 0x7CA8B002 (without the leading zeroes). A quick look at this byte-reversed value (0x02B0A87C) in a hex calculator gives us the amount of 45,131,900. After moving the decimal point four places to the left, we get our starting value of 4513.1900.

But read on to see how that compares to other data types.

Vladimir Klimov walks us through what happens when we save data to an XML data type:

When working on the release of dbForge Transaction Log, among other tasks, our team had to puzzle out how to properly store typed XML data.

To start with, it is worth mentioning that SQL Server does not store XML in the format it was entered. An XML string is parsed, split to tags, and thus is stored in a compressed format. Description elements that the server considers unnecessary are discarded.

It also should be kept in mind that, if the data type of a column is specified as simple XML, the server will store this data as Unicode strings.

Click through for a couple of examples and their data storage requirements.

Eitan Blumin does not like the limitations of metadata-only column changes with SQL Server 2016:

This is an excellent mechanism on the one hand…

However, it’s completely useless when the column you want to change has a CLUSTERED INDEX defined on it (regardless of whether it’s also a PRIMARY KEY or not).

Such a scenario would especially be common with IDENTITY columns (which, ironically, is exactly the kind of examples that Paul presented in his post).

Click through to understand the scope of this limitation.