C Program To Implement Dictionary Using Hashing Algorithms

However, there are good reasons to avoid using hash tables. First, they are expected to perform well, but they often don't. Unless you know exactly how many values to add or how many queries you will make, they are inherently inefficient; if you insert or remove data from a hash table, you may have to rehash the entire thing. Second, they are not designed to be accessed in-place. Dictionary traversal is O(n) while insertion is O(1), so if you need to iterate a dictionary, you have to make a copy. Both of these inefficiencies may be unacceptable in a production environment. Third, Python offers an alternative data structure, called the deque, which has O(1) lookup and insertion, and O(n) removal, meaning it is much faster than dictionary traversal. Fourth, Python includes as a built-in object the set, a set data structure that is inherently unordered and does not provide a way to remove values, so that in practice a hash table is superior. Finally, it is easy to create implementations of hash tables that waste a lot of CPU time with poor interface choices, thereby making hash table performance less predictable than it should be.

The code below contains a series of benchmarks. It creates a hash table with capacity set to 100,000 using the PyUnicode_Join and PyUnicode_Compare methods, along with a dictionary. In the deque case, the hash table is empty.

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One of the drawbacks to this approach was that we needed to recompile every time we made even a small change to the code. This slowed everything else we were doing down and because we couldn't always have someone waiting for the application to finish building. 827ec27edc