Why hashmap lookup is O(1) i.e. constant time?

Question

If we look from Java perspective then we can say that hashmap lookup takes constant time. But what about internal implementation? It still would have to search through particular bucket (for which key's hashcode matched) for different matching keys.Then why do we say that hashmap lookup takes constant time? Please explain.

Answer 1

Under the appropriate assumptions on the hash function being used, we can say that hash table lookups take expected O(1) time (assuming you're using a standard hashing scheme like linear probing or chained hashing). This means that on average, the amount of work that a hash table does to perform a lookup is at most some constant.

Intuitively, if you have a "good" hash function, you would expect that elements would be distributed more or less evenly throughout the hash table, meaning that the number of elements in each bucket would be close to the number of elements divided by the number of buckets. If the hash table implementation keeps this number low (say, by adding more buckets every time the ratio of elements to buckets exceeds some constant), then the expected amount of work that gets done ends up being some baseline amount of work to choose which bucket should be scanned, then doing "not too much" work looking at the elements there, because on expectation there will only be a constant number of elements in that bucket.

This doesn't mean that hash tables have guaranteed O(1) behavior. In fact, in the worst case, the hashing scheme will degenerate and all elements will end up in one bucket, making lookups take time Θ(n) in the worst case. This is why it's important to design good hash functions.

For more information, you might want to read an algorithms textbook to see the formal derivation of why hash tables support lookups so efficiently. This is usually included as part of a typical university course on algorithms and data structures, and there are many good resources online.

Fun fact: there are certain types of hash tables (cuckoo hash tables, dynamic perfect hash tables) where the worst case lookup time for an element is O(1). These hash tables work by guaranteeing that each element can only be in one of a few fixed positions, with insertions sometimes scrambling around elements to try to make everything fit.

Hope this helps!