Hash table runtime complexity insert search and delete

Question

Why do I keep seeing different runtime complexities for these functions on a hash table    On wiki  search and delete are O n   I thought the point of hash tables was to have constant lookup so what s the point if search is O n      In some course notes from a while ago  I see a wide range of complexities depending on certain details including one with all O 1   Why would any other implementation be used if I can get all O 1    If I m using standard hash tables in a language like C   or Java  what can I expect the time complexity to be

User · Answer

Perhaps you were looking at the space complexity? That is O(n). The other complexities are as expected on the hash table entry. The search complexity approaches O(1) as the number of buckets increases. If at the worst case you have only one bucket in the hash table, then the search complexity is O(n).

Edit in response to comment I don't think it is correct to say O(1) is the average case. It really is (as the wikipedia page says) O(1+n/k) where K is the hash table size. If K is large enough, then the result is effectively O(1). But suppose K is 10 and N is 100. In that case each bucket will have on average 10 entries, so the search time is definitely not O(1); it is a linear search through up to 10 entries.

User · Answer

Hash tables are O 1  average and amortized case complexity  however it suffers from O n  worst case time complexity   And I think this is where your confusion is   Hash tables suffer from O n  worst time complexity due to two reasons    If too many elements were hashed into the same key  looking inside this key may take O n  time  Once a hash table has passed its load balance - it has to rehash  create a new bigger table  and re-insert each element to the table      However  it is said to be O 1  average and amortized case because    It is very rare that many items will be hashed to the same key  if you chose a good hash function and you don t have too big load balance  The rehash operation  which is O n   can at most happen after n 2 ops  which are all assumed O 1   Thus when you sum the average time per op  you get    n O 1    O n     n    O 1    Note because of the rehashing issue - a realtime applications and applications that need low latency - should not use a hash table as their data structure   EDIT  Annother issue with hash tables  cache Another issue where you might see a performance loss in large hash tables is due to cache performance  Hash Tables suffer from bad cache performance  and thus for large collection - the access time might take longer  since you need to reload the relevant part of the table from the memory back into the cache

User · Answer

Depends on the how you implement hashing  in the worst case it can go to O n   in best case it is 0 1   generally you can achieve if your DS is not that big easily

User · Answer

Some hash tables  cuckoo hashing  have guaranteed O 1  lookup

User · Answer

Ideally  a hashtable is O 1    The problem is if two keys are not equal  however they result in the same hash   For example  imagine the strings  it was the best of times it was the worst of times  and  Green Eggs and Ham  both resulted in a hash value of 123   When the first string is inserted  it s put in bucket 123   When the second string is inserted  it would see that a value already exists for bucket 123   It would then compare the new value to the existing value  and see they are not equal   In this case  an array or linked list is created for that key   At this point  retrieving this value becomes O n  as the hashtable needs to iterate through each value in that bucket to find the desired one   For this reason  when using a hash table  it s important to use a key with a really good hash function that s both fast and doesn t often result in duplicate values for different objects   Make sense

[algorithm] Hash table runtime complexity (insert, search and delete)

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