In my last blog article I looked at implementing a hashing algorithm by trying different boolean mathematical operations on the constituent fields of our class. It was very clear that out of AND, OR and XOR, only XOR provided us with anything like a balanced hash code. However, although it worked well in the previous example (a music library), all is not quite as it seems. On closer inspection of the behaviour of the XOR hash, it turns out that this hashing algorithm has its own flaws and is not ideal in most situations.

**The Commutativity Issue**

The most obvious problem with the "XOR" all fields approach to hash codes is that any two fields XOR’d together will give you the same value, regardless of the order in which they are XOR’d (i.e. XOR is a commutative operation). This increases the chance that you will get hash code collision, which of course is a *bad* thing. Consider the following example (all fields contributing to hash):

Forename |
Middle name |
Surname |

John |
Paul |
Jones |

Paul |
John |
Jones |

Clearly the two people above are not the same person, but they will have the same hashcode if we generate our hash with a simple XOR of the hash codes of the constituent fields.

As mentioned in previous posts, a hashcode is *not* a unique identifier, and the fact both people have the same hash-code won’t *break* a hashtable. However, it will lead to a less efficient hashtable, as both our people will end up in the same bucket of the hashtable when, ideally, they shouldn’t.

**The Range Issue**

Another problem that the XOR approach doesn’t address is that of the potential range of values of a hashcode. In my previous post I showed that the XOR implementation of hash code seemed acceptable for my music library example. However, in reality I was relying on the properties of the individual hash codes that made up my overall hash code. Specifically, many of the fields that contributed to the hash were strings, and the BCL implementation of System.String.GetHashCode() has a pretty good distribution. Had my music track entity not contained several string fields, things would have looked very different.

But what about fields that may have a poor "innate" distribution? Given that System.Integer.GetHashCode() returns the integer itself, what happens if I have an field representing a person’s age? The spread of values is, at best, 0 < age < 120, which is hardly the distribution across integer-space that you might want. A person’s height in cm? 0 < height < 250.

You see the problem? I don’t really want to be combining all these hash codes in the very low integer ranges using the XOR approach because it means my final "cumulative" hash code will be stuck within this range as well.

**Examining the flaws in more detail**

I don’t pretend to be an expert on hashing algorithms, but I can see that we have issues here and so the best way of discovering more (for me, at least) is to work through a broken example and see what I can do to fix it.

I very quickly settled on the idea of trying to write a replacement String.GetHashCode() implementation. I would get a dictionary of words (all in lower case), and try and write a hash code implementation based on the ASCII code of each letter in the word. Given that the the hashcode of our ASCII codes would be the ASCII code itself (since the hashcode of an integer is the integer itself), we would have a poor distribution of individual character hash codes; all in the range 97 (a) – 122 (z). This approach would also highlight problems such as commutativity (since many words have the same letters, but just in a different order).

My XOR implementation for this approach would look like this:

public int GetHashCode(string word)
{
char[] chars = word.ToCharArray();
int hash = 0;
foreach (char c in chars)
{
int ascii = (int)c;
hash ^= ascii.GetHashCode();
}
return hash;
}

I sourced my dictionary from here, and de-duplicated it (and converted the words to lower case) to produce this list of words.

As expected, this algorithm (referred to as **AsciiChar_XOR** in the diagram) has a spectacularly bad value distribution, as shown in this histogram:

[Note that the width of each bucket here is 67108864, being 2^32 / 64]

Surprise, surprise – all 2898 words fall into the same histogram bucket! In fact they all fall into the far narrower range of 0-127 – the ASCII code range.

All of a sudden the simple XOR approach to hashing looks like a very poor performer indeed.

**Examining better algorithms**

Since we’ve already discussed the weaknesses of XOR, we should have a fairly good idea of where to focus our attention to create better algorithms. Firstly we should be choosing an algorithm that is non-commutative, and secondly we should be choosing an algorithm that uses the full range of integer-space, rather than limiting a hashcode to the range of its constituent members.

A bit of a search around reveals two approaches that are often discussed. The first one, given to me as a boiler-plate example by a Java developer friend, looks like this:

public override int GetHashCode()
{
int hash = 23;
hash = hash * 37 + (field1 == null ? 0 : field1.GetHashCode());
hash = hash * 37 + (field2 == null ? 0 : field2.GetHashCode());
hash = hash * 37 + (field3 == null ? 0 : field3.GetHashCode());
return hash;
}

[I shall refer to this type of algorithm as **JavaStyleAddition** as it seems to be a very common implementation in the Java world]

The second common pattern looks something like this:

public override int GetHashCode()
{
int hash = 23;
hash = (hash << 5) + (field1 == null ? 0 : field1.GetHashCode());
hash = (hash << 5) + (field2 == null ? 0 : field2.GetHashCode());
hash = (hash << 5) + (field3 == null ? 0 : field3.GetHashCode());
return hash;
}

[I shall refer to this type of algorithm as **ShiftAdd**]

Both of these have some similar characteristics. By taking the cumulative hash so far, applying an operation (multiply for JavaStyleAddition and left-shift-5 for ShiftAdd) and then adding the new field’s hash code, they both avoid the commutativity issue since

(x * n) + y is not *generally* equal to ( y * n ) + x.

They also both increase the range of the cumulative hash code above that of the constituent hash codes due to the effect of multiplying the cumulative hash code each time (remember that a single left shift is a multiplication by two).

You will also notice that both approaches use a prime number (23 in the examples shown) as the starting value for the hash, and JavaStyleAddition uses another prime (37) as the multiplier. My guess is that this, statistically, makes collisions less likely as you multiply up your hash code because if one side of the multiplication has no factors (other than 1 and itself), then you are lowering the statistical average number of factors of the result. Of course, I may be wrong about that :-s

A variant of ShiftAdd that I have seeing during my Google journeys is one in which the hash codes are shifted and then XOR’d (rather than added). I shall refer to this as **ShiftXOR**.

This certainly looks better than the histogram for AsciiChar_XOR 😀

However, all three algorithms still cluster around the centre of the number range, and all exhibit other major spikes in distribution.

What I could have done at this point is break into a major mathematical and statistical analysis of these three hashing algorithms, but I decided against it for two key reasons. Firstly – I would have got bored, and secondly – I wouldn’t have had the first idea where to start!

Nope – I felt it better to fall back on my hacker instincts and munge various forms of the above algorithms to see if I could produce a better algorithm for my particular use case.

I quickly came up with two further algorithms, both combining the left-shift approach with the the prime-add approach of the above algorithms. The difference between them being only that one adds the hashcodes each time, while the other XORs them:

public override int GetHashCode()
{
int hash = 23;
hash = ((hash << 5) * 37 ) + (field1 == null ? 0 : field1.GetHashCode());
hash = ((hash << 5) * 37 ) + (field2 == null ? 0 : field2.GetHashCode());
hash = ((hash << 5) * 37 ) + (field3 == null ? 0 : field3.GetHashCode());
return hash;
}

[**ShiftPrimeAdd]**

and

public override int GetHashCode()
{
int hash = 23;
hash = ((hash << 5) * 37) ^ (field1 == null ? 0 : field1.GetHashCode());
hash = ((hash << 5) * 37) ^ (field2 == null ? 0 : field2.GetHashCode());
hash = ((hash << 5) * 37) ^ (field3 == null ? 0 : field3.GetHashCode());
return hash;
}

[**ShiftPrimeXOR]**

For good measure, I threw these into the mix alongside the standard BCL implementation of System.String.GetHashCode() [labelled as **StringGetHashCode** in the diagram] to see how they would fare:

[Note that the y-axis for this histogram is half that of the previous diagram]

Now – that is MUCH more like it. We have a well balanced hash code distribution across the entire range of integer space. There are a few minor spikes, but all three seem to compare favourably with each other. The approach of including the prime and ‘multiplying’ up each time really does seem to do the trick.

Which would I chose out of ShiftPrimeXOR and ShiftPrimeAdd? Not sure – I’d have to benchmark them first and see which was fastest!

**Conclusion**

In summary, just XORing fields together may well produce an awful hash code distribution, unless the constituent field hash codes are themselves well balanced.

However, during the course of writing this article, I have realised that there are other relatively simple implementations that provide a good hash code distribution (for this example at least). More than that, I have reinforced my belief that these things are best checked out if you have any doubts. It doesn’t take long to put together a test harness and profile your algorithms with a sample of your data.

One thing I have omitted from my investigation is any discussion on the speed of the algorithms. It would be worth benchmarking each one because if a class is being used in a hashtable, its .GetHashCode() method is called for each .Add, .Remove and .Contains call. However, the following thought does occur to me; with these sort of repetitve mathematical operations, the relative speed of XOR vs. shift vs. multiply (etc) may *well* actually depend on your CPU architecture.

On reflection, there is a lot more to hashing than the small amount I know and I’m sure many mathematical research papers have been written on the subject.

In the future, my default choice will probably lean towards ShiftPrimeXOR or ShiftPrimeAdd as a starting point. It would be a waste of time to spend days up front trying to work out the perfect hashing algorithm. My approach would be to choose one, use it, and keep an eye on its performance. If it proves to problematic then consider optimising it, otherwise leave it alone.

Right – enough already about hashing algorithms!

A Visual Studio 2008 project containg the console application I used to generate these results can be found here.