Blog Post

CHECKSUM vs HashBytes

Hashing can be useful in Data Warehousing  as well ;)  It can give you the ability to break large problems into smaller more manageable sizes  or scale out your ETL process 😉 PDW even uses a similar method to distribute data internally among all the compute nodes.

In reality there is no competition between CHECKSUM and HashBytes. CHECKSUM is a clear loser all the way around as BOL clearly? states:

“CHECKSUM satisfies the properties of a hash function: CHECKSUM applied over any two lists of expressions returns the same value if the corresponding elements of the two lists have the same type and are equal when compared using the equals (=) operator. For this definition, null values of a specified type are considered to compare as equal. If one of the values in the expression list changes, the checksum of the list also generally changes. However, there is a small chance that the checksum will not change. For this reason, we do not recommend using CHECKSUM to detect whether values have changed, unless your application can tolerate occasionally missing a change. Consider using HashBytes instead. When an MD5 hash algorithm is specified, the probability of HashBytes returning the same result for two different inputs is much lower than that of CHECKSUM.”

The CHECKSUM algorithm is very simple but fast. Depending on the data types used or if there are null values collisions can frequently occur. I have used GUIDs to produce collisions in just a few thousand rows!

Thomas Kejser has written a great series of articles about Hash functions

Exploring Hash Functions in SQL Server:

Hash distributing rows is a wonderful trick that I often apply. It forms one of the foundations for most scale-out architectures. It is therefore natural to ask which hash functions are most efficient, so we may chose intelligently between them.

In this blog post, I will benchmark the build in function in SQL Server. I will focus on answering two questions:

  1. How fast is the hash function?
  2. How well does the hash function spread data over a 32-bit integer space

I know there is also the question about how cryptographically safe the function is, but this is not a necessary property for scale-out purposes – hence, that aspect is out of scope for this blog.

Writing New Hash Functions for SQL Server: In this blog, I will explore ideas for extending SQL Server with new, fast hash functions. As will be shown, the high speed, built in functions CHECKSUM and BINARY_CHECKSUM are not always optimal hash functions, when you require the function to spread data evenly over an integer space. I will show that it can be useful to extend SQL Server with a new CLR User Defined Function (UDF).