Hash Tables and Key-Value Lookup
Hash tables are vital for fast key-value lookup, making them a cornerstone of modern databases and caching systems. By mapping keys to unique indices using a hash function, hash tables allow you to quickly insert, search, and delete data. This direct access is what makes them so powerful for applications that demand high-speed retrieval, such as caching web sessions or indexing database records. When you store or retrieve a value, the hash function computes an index from the key, and the value is placed or found at that position in an underlying array. If two keys hash to the same index — a collision — strategies like chaining or open addressing are used to resolve conflicts, ensuring every key-value pair can be efficiently accessed. This structure underpins high-performance systems where rapid data access is crucial.
1234567891011121314151617181920212223242526272829303132333435363738class HashTable: def __init__(self, size=10): self.size = size self.table = [[] for _ in range(size)] # Using chaining for collision resolution def _hash(self, key): return hash(key) % self.size def insert(self, key, value): idx = self._hash(key) for i, (k, v) in enumerate(self.table[idx]): if k == key: self.table[idx][i] = (key, value) # Update existing key return self.table[idx].append((key, value)) # Insert new key-value pair def search(self, key): idx = self._hash(key) for k, v in self.table[idx]: if k == key: return v return None # Key not found def delete(self, key): idx = self._hash(key) for i, (k, v) in enumerate(self.table[idx]): if k == key: del self.table[idx][i] return True return False # Key not found # Example usage: ht = HashTable() ht.insert("user1", "session1") ht.insert("user2", "session2") print(ht.search("user1")) # Output: session1 ht.delete("user1") print(ht.search("user1")) # Output: None
Hash table performance is one of their main advantages. On average, insertion, search, and deletion operations take constant time — O(1) — because the hash function computes the index directly. However, in the worst-case scenario, such as when many keys collide and end up in the same bucket, operations can degrade to O(n), where n is the number of entries. This is rare in practice for well-designed hash functions and good load factors. Real-world systems must also handle resizing: as the table becomes full, it is often doubled in size and all entries are rehashed to maintain efficiency. Clustering, where many keys end up in the same region of the table, can also affect performance, so careful hash function selection and load factor management are key to maintaining speed.
Distributed systems like Redis and DynamoDB rely on hash tables to scale efficiently. They use advanced techniques like partitioning and replication to distribute key-value pairs across many nodes, providing both speed and resilience for large-scale applications.
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Hash tables are vital for fast key-value lookup, making them a cornerstone of modern databases and caching systems. By mapping keys to unique indices using a hash function, hash tables allow you to quickly insert, search, and delete data. This direct access is what makes them so powerful for applications that demand high-speed retrieval, such as caching web sessions or indexing database records. When you store or retrieve a value, the hash function computes an index from the key, and the value is placed or found at that position in an underlying array. If two keys hash to the same index — a collision — strategies like chaining or open addressing are used to resolve conflicts, ensuring every key-value pair can be efficiently accessed. This structure underpins high-performance systems where rapid data access is crucial.
1234567891011121314151617181920212223242526272829303132333435363738class HashTable: def __init__(self, size=10): self.size = size self.table = [[] for _ in range(size)] # Using chaining for collision resolution def _hash(self, key): return hash(key) % self.size def insert(self, key, value): idx = self._hash(key) for i, (k, v) in enumerate(self.table[idx]): if k == key: self.table[idx][i] = (key, value) # Update existing key return self.table[idx].append((key, value)) # Insert new key-value pair def search(self, key): idx = self._hash(key) for k, v in self.table[idx]: if k == key: return v return None # Key not found def delete(self, key): idx = self._hash(key) for i, (k, v) in enumerate(self.table[idx]): if k == key: del self.table[idx][i] return True return False # Key not found # Example usage: ht = HashTable() ht.insert("user1", "session1") ht.insert("user2", "session2") print(ht.search("user1")) # Output: session1 ht.delete("user1") print(ht.search("user1")) # Output: None
Hash table performance is one of their main advantages. On average, insertion, search, and deletion operations take constant time — O(1) — because the hash function computes the index directly. However, in the worst-case scenario, such as when many keys collide and end up in the same bucket, operations can degrade to O(n), where n is the number of entries. This is rare in practice for well-designed hash functions and good load factors. Real-world systems must also handle resizing: as the table becomes full, it is often doubled in size and all entries are rehashed to maintain efficiency. Clustering, where many keys end up in the same region of the table, can also affect performance, so careful hash function selection and load factor management are key to maintaining speed.
Distributed systems like Redis and DynamoDB rely on hash tables to scale efficiently. They use advanced techniques like partitioning and replication to distribute key-value pairs across many nodes, providing both speed and resilience for large-scale applications.
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