In this topic, we explore hashing, a technique very widely used in interview questions. Hashing is designed to solve the problem of needing to efficiently find or store an item in a collection.
For example, if we have a list of 10,000 words of English and we want to check if a given word is in the list, it would be inefficient to successively compare the word with all 10,000 items until we find a match. Hashing is a technique to make things more efficient by effectively narrowing down the search at the outset.
Hashing means using some function or algorithm to map object data to some representative integer value. This so-called hash code (or simply hash) can then be used as a way to narrow down our search when looking for the item in the map.
Purely as an example to help us grasp the concept, let us suppose that we want to map a list of string keys to string values (for example, map a list of countries to their capital cities). So let’s say we want to store the data in Table 1 in the map.
Key | Value ----------------|------------- Cuba | Havana England | London France | Paris Spain | Madrid Switzerland | Berne
And let us suppose that our hash function is to simply take the length of the string.
For simplicity, we will have two arrays: one for our keys and one for the values. So to put an item in the hash table, we compute its hash code (in this case, simply count the number of characters), then put the key and value in the arrays at the corresponding index.
For example, Cuba has a hash code (length) of 4. So we store Cuba in the 4th position in the keys array, and Havana in the 4th index of the values array etc. And we end up with the following:
Position | Keys array | Values array (hash = key length) | | ---------------------|------------------|--------------- 1 | | 2 | | 3 | | 4 | Cuba | Havana 5 | Spain | Madrid 6 | France | Paris 7 | England | London 8 | | 9 | | 10 | | 11 | Switzerland | Berne
Now, in this specific example things work quite well. Our array needs to be big enough to accommodate the longest string, but in this case that’s only 11 slots. And we do waste a bit of space because, for example, there’s no 1-letter keys in our data, nor keys between 8 and 10 letters. But in this case, the waste isn’t so bad either. And taking the length of a string is nice and fast, so so is the process of finding the value associated with a given key (certainly faster than doing up to five string comparisons)1.
We can also easily see that this method would not work for storing arbitrary strings. If one of our string keys was a thousand characters in length but the rest were small, we would waste the majority of the space in the arrays. More seriously, this model can’t deal with collisions: that is, what to do when there is more than one key with the same hash code (in this case, more than one string of a given length). For example, if our keys were random words of English, taking the string length would be fairly useless. Granted, the word “psuedoantidisestablishmentarianistically” would probably get its own place in the array. But on the other hand, we’d be left with thousands of, say, 6-letter words all competing for the same slot in the array.
So next up, we look at how hashing is traditionally implemented.