Diving into the world of hash tables
If you care about performance, it's important to note how the underlying representation of a dictionary data structure.<p>For instance, the unordered_hash_map in C++ is based around a linked list of key/value pair buckets. This means iterating through the keys or values is very slow (lots of cache misses!), but insertion is fast. Retrieving a key is O(logn) but a very slow O(logn), because of the cache misses.<p>Other implementations is to keep a sorted vector of keys and a respective vector of values. There's loki::assocvector, booost::flat_unordered_map that do this for instance. Now insertion is slow, but iteration and retrieval are very fast (a fast O(logn) by binary search with few cache misses). It's also memory efficient, since no pointers between elements.<p>If you have one big dictionary you would use throughout an application, and know the data up front, a good strategy is to reserve the necessary memory in an array, fill it with keys/values, sort it once over the keys and coindex the value array. Now you have a memory efficient and extremely fast dictionary data structure.<p>One other strategy any intermediate coder can implement is to have two unsorted coindexed arrays. You don't even need a hash function for this. Now iterating and insertion through the table is extremely fast, and it is memory efficient, but finding a key is just a fast O(n). So this is good for smaller tables. In C++ you could implement it as a std::pair<vector<key>, vector<value>>. If you need a quick small map in a function this is often the fastest data structure you can implement without too many headaches.