Hash functions map sequences of bytes to fixed length sequences. The value returned by a hash function is often called a hash, message digest, hash value, or checksum.
Underlying a hash table is a large array of length $N$, where most of the slots are empty. To resolve collisions (which happen when we go to enter a value at a particular position, and there is already a value there), we can either implement chaining (where we turn each slot into a linked list) or open addressing where we place the value in the next empty slot. If we use chaining, we’ll have to tack on a 32 or 64 bit pointer to our space bill for each element in our linked list. And if we use open addressing and do not keep a spacious amount of space (storing a lot of empty slots) our clever hash table will devolve to a structure with $O(n)$ traversal. Tradeoffs to consider!
If you’re looking to hash a string, you’ll need to convert your string to binary.
# Convert string to binary binary_string = b'abcd' print(binary_string) >>> b'abcd'
# Generate hexadecimal from binary string import binascii binascii.hexlify(binary_string) >>> b'61626364'
import hashlib m = hashlib.md5() m.update(binary_string) m.hexdigest() >>> 'e2fc714c4727ee9395f324cd2e7f331f'
Before you get to thinking that these Python built-ins are all there is to know in the world of hash functions, let me dazzle before your eyes Google’s sparsehash, which offers one of the most space efficient hashmap implementations known to humankind, requiring a mere 2 bits of overhead per entry. It does, however, clock up to 2 to 3 times slower than the implementations explored above, but if space is your concern, I suggest you check it out.