To optimize memory in a hash table, use open addressing instead of linked lists to avoid extra pointers, choose a good initial size and resizing strategy, and store keys and values efficiently with compact data types. Also, use a good hash function to reduce collisions and save space. For more info, see https://en.wikipedia.org/wiki/Hash_table#Memory_efficiency Example of open addressing with linear probing: python class HashTable: def __init__(self, size=8): self.size = size self.keys = [None] * size self.values = [None] * size def hash(self, key): return hash(key) % self.size def insert(self, key, value): idx = self.hash(key) while self.keys[idx] is not None and self.keys[idx] != key: idx = (idx + 1) % self.size self.keys[idx] = key self.values[idx] = value This approach saves memory by avoiding extra pointers.

If you want to optimize memory usage in a hash table, you start by adjusting how the table grows and stores entries. Instead of aggressively resizing on every load factor trigger, you can use a balanced load factor (like 0.7) so you don’t waste space on half-empty arrays. You can also store keys and values more efficiently by using references or interning strings if keys repeat often. Another strategy is open addressing (like linear probing or quadratic probing) instead of separate chaining, since it avoids storing extra linked list or tree nodes. If you do use chaining, you can replace pointers with arrays or compact structures to cut down overhead. For cases where the key space is predictable, you can use perfect hashing or minimal hashing to shrink memory usage further. Here’s a simple example of memory-aware open addressing in Python-like pseudocode:

class HashTable:
    def __init__(self, size=8):
        self.size = size
        self.keys = [None] * size
        self.values = [None] * size
    
    def hash(self, key):
        return hash(key) % self.size
    
    def insert(self, key, value):
        idx = self.hash(key)
        while self.keys[idx] is not None and self.keys[idx] != key:
            idx = (idx + 1) % self.size
        self.keys[idx] = key
        self.values[idx] = value

This approach avoids extra node allocations, keeping memory tighter.