From what I know hash sets generally have complexity of $O(1)$ (unless the hash function is bad, but let's just ignore that for this question). However, sets need to either read the full data so as to make a hash function that's guaranteed to return a unique result for every input or they need to make a comparison to make sure the data is the same otherwise. Doesn't that mean that the complexity for strings should be $O(m)$, where $m$ is the length of the string, at least if we accept that these strings can get arbitrarily large? Is there something I do not understand?

The reason I'm asking is because I solved a problem with a trie and the provided solution was solved with a set (that was the only difference), but it assumed that arbitrarily large strings can be found to exist or to not exist in a set in $O(1)$ time. The problem is to find which words in a set of words can be formed using other words in the set (words can be used multiple times) — for example in "a", "bc", "cd", "acd", "abcd", only "acd" can be formed using other words, though I don't think the specific problems matters much.

  • 2
    $\begingroup$ You always have to read the string, yes, at least once $\endgroup$ – Pål GD May 8 at 14:33
  • $\begingroup$ So the complexity is technically O(m), right? $\endgroup$ – asharpharp May 8 at 14:34
  • $\begingroup$ GD: Not to get a hash code, you can easily extract say 100 characters out of a million for hashing. But if the hash code is in the table, and the strings have same length, you need to compare until you find a difference, and if the string is in the table, you’ll have to read all characters. $\endgroup$ – gnasher729 May 16 at 15:10

You are right in that to check membership (or to retrieve) using a hash function, you also need to check that the retrieved element is equal to the element in question. For a string, you should assume that this takes time linear in the length.

Hence checking membership in a hash set is indeed expected $O(m)$.


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