swisstable | Access Abseil Swiss Tables from C | Hashing library

Currently, our implementation simply performs naive linear search. This provides excellent performance on small nodes of elements which are cheap to compare. However in the future we would like to further explore choosing the optimal search strategy based on the choice of B, and possibly other factors. Using linear search, searching for a random element is expected to take O(B * log(n)) comparisons, which is generally worse than a BST. In practice, however, performance is excellent.

Source: BTreeMap referenced from here.

From this reference, I'd assume that HashSet is more or less equal to HashMap:

The default hashing algorithm is currently SipHash 1-3, though this is subject to change at any point in the future. While its performance is very competitive for medium sized keys, other hashing algorithms will outperform it for small keys such as integers as well as large keys such as long strings, though those algorithms will typically not protect against attacks such as HashDoS.

Source: HashMap

Since this doesn't state anything specific, I'd assume that O(1) should apply most of the time. This thread has (although for Java) some very good answers.

In very simple words: the complexity of an algorithm is defined by looking at the source code. For a two-dimensional array, the runtime (without doing anything in the inner loop) would be n² because you'd have two loops running n-times each: