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### Re: A short meditation about hash search performance

by Abigail-II (Bishop)
 on Nov 16, 2003 at 23:03 UTC ( #307515=note: print w/ replies, xml ) Need Help??
You obviously don't understand what O(1) means.
Let's see. The definition of big O is:
```   f(n) = O (g (n)) iff there are a M > 0 and a c > 0 such that
for all m > M, 0 <= f(m) <= c * g (m).   [1] [
+2] [3]
I don't have any problem understanding with it. In layman terms, it means that a function f of n is in the order of g of n, if, and only if, there's a constant, such that if n gets large enough, the value of f is at most the value of g times said constant.
Search from beginning to end, going thru each element one by one, until hit what you are searching for. In the worst case (the element is at the end of the array), you have to hit 1 billion elements, but according to you, that's O(1). I say it is O(n). We never put a restriction saying that an array can at most contain 1 billion elements (so the size of an array in general is not a constant, although it is a constant for a given array at one given observation point.)
Hello? We never put a restriction on the size? Come again. What do you call:
And still O(1) is not reachable, unless each element resolve a unique key ;-)
That's a restriction of 1. You started out by putting restrictions on it, claiming that only if there's a restriction of a size of 1, the search algorithm is O (1). I on the other hand pointed out that as long as there is a restriction on the limit of the chain, it doesn't matter what the restriction is, 1, 14 (for 5.8.2), or a billion. If there's a restriction on the size, even with a linear search it's O (1). Here's a proof:
Suppose the chain is limited to length K, where K is a constant, independent of the amount of keys in the hash. Searching for a key is a two step process: first we need to find the bucket the key hashes to, then we need to find the key in the associated chain. Finding the right bucket takes constant time. Traversing the chain takes at most K * e time, for some constant e. So, searching for the element takes at most:
```                               e * K + O (1),  e >= 0
{definition of O()}  <=   e * K + d * 1,  e >= 0, d >= 0
{arithmetic}         ==  (e * K + d) * 1, e >= 0, d >= 0
{c == e * K + d}     ==   c * 1
{c > 0}              ==   O (1).
q.e.d.
```
I won't deny the performance will be rather lousy, but it's still O (1). Which proves that big-Oh doesn't say everything.
[1]
Cormen, Leiserson, and Rivest: Introduction to Algorithms. MIT Press, 1990. pp 26.
[2]
Knuth: The Art of Computer Programming, Volume 1: Fundamental Algorithms. Third Edition. Addison-Wesley, 1997. pp 107.
[3]
Sedgewick, and Flajolet: Analysis of Algorithms. Addison-Wesley, 1996. pp 4.

Abigail

Comment on Re: A short meditation about hash search performance
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Re: Re: A short meditation about hash search performance
by demerphq (Chancellor) on Nov 17, 2003 at 08:58 UTC

Abigail, I have two minor questions for you. First off you speak of finding the correct bucket as occurring in constant time. Given that the time to calculate the bucket value is dependent on the length of the key I dont quite see how this is correct. Or does this factor disappear because it averages to a constant time in normal use? I have a similar concern about the doubling of the buckets during insertion. My by now hazy recollection of big O() says that this behaviour is signifigant and should be included in the O() of hash insertion. Is this wrong? If its not wrong how would it be calculated? I havent the foggiest how you would calculate the effect of a factor that comes into play so rarely. Or is it again that it averages to 0 and so can be left out of the equation?

---
demerphq

First they ignore you, then they laugh at you, then they fight you, then you win.
-- Gandhi

Yes, you are right that length of a key plays a role in calculating the hash value. And it plays a role in comparing two keys as well. You can take this time into account, and say insertion/searching takes O (k), where k is the length of input. There's no relationship between k and n though, and usually we aren't interested in this factor. We just define that calculating a hash value can be done in constant time, and so can comparing two keys.

As for the doubling, this factors out (assuming it isn't possible to construct a set of keys such that even after repeated doubling, they keep hashing to the same values). The sketch of the proof is as follows: suppose we rebuild the hash after N inserts; that is, the hash was rebuild when it contains N keys. Building a hash out of N keys will take O (N) time - this is O (1) per key. Now you also have to show that the next rebuild isn't taking place after inserting another c * N keys, for some constant c > 1. This means that if you rebuild a hash with N keys, for at least N / (1 + c) keys, this is the first time they are involved, for at least N / (1 + c)^2 keys, this is the second time they are involved in a rebuild, etc. If you do the math, you will see that there are some keys that have been charged O (log N) on rebuilds, but because there are so many more who have been charged less, it works out to O (1) amortized time. So, yes, a single insert can take O (1) time, but, starting from an empty hash, N inserts take O (N) time.

Rebuilding after a bunch of inserts is actually a well-known technique for datastructures. Often a datastructure is only partially rebuild (giving the technique its name: "partially rebuilding"). Rebuilding the entire datastructure is just an extreme variant.

Abigail

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