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CSa video on hashing and hashmaps! sorry been busy with exams and uni! hope all you guys are well💙
Hash maps feel like magic because they promise fast lookups—often described as O(1) time. But that speed depends entirely on how well the hash function distributes data. A hash function takes a key and converts it into an index in memory, ideally spreading values evenly so each lookup goes straight to the right spot. When that happens, everything feels instant and efficient.
The problem is that collisions are unavoidable. Different keys can produce the same index, which means the hash map has to handle multiple values in one place. This is where strategies like chaining (storing multiple items in a list at the same index) or open addressing (finding another spot using methods like linear or quadratic probing) come in. These methods keep things working—but they introduce extra steps.
That’s why hash maps aren’t truly constant time in all cases. Their performance depends on the quality of the hash function and how collisions are handled. In the best case, lookups are fast and smooth—but in worse cases, they can degrade toward O(n). So O(1) isn’t a guarantee—it’s an expectation based on good design.
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