Why is looking for an item in a hash map faster than looking for an item in an array?

Question

You might have come across someplace where it is mentioned that it is faster to find elements in hashmap/dictionary/table than list/array. My question is WHY?

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Answer 1

Let’s reason by analogy. Suppose you want to find a specific shirt to put on in the morning. I assume that, in doing so, you don’t have to look at literally every item of clothing you have. Rather, you probably do something like checking a specific drawer in your dresser or a specific section of your closet and only look there. After all, you’re not (I hope) going to find your shirt in your sock drawer.

Hash tables are faster to search than lists because they employ a similar strategy - they organize data according to the principle that every item has a place it “should” be, then search for the item by just looking in that place. Contrast this with a list, where items are organized based on the order in which they were added and where there isn’t a a particular pattern as to why each item is where it is.

More specifically: one common way to implement a hash table is with a strategy called chained hashing. The idea goes something like this: we maintain an array of buckets. We then come up with a rule that assigns each object a bucket number. When we add something to the table, we determine which bucket number it should go to, then jump to that bucket and then put the item there. To search for an item, we determine the bucket number, then jump there and only look at the items in that bucket. Assuming that the strategy we use to distribute items ends up distributing the items more or less evenly across the buckets, this means that we won’t have to look at most of the items in the hash table when doing a search, which is why the hash table tends to be much faster to search than a list.

For more details on this, check out these lecture slides on hash tables, which fills in more of the details about how this is done.

Hope this helps!