Hash Tables
Learn about the engine behind dictionaries and sets: Hash Tables, including hash functions and collision resolution strategies.
Learn about the engine behind dictionaries and sets: Hash Tables, including hash functions and collision resolution strategies. This hands-on tutorial focuses on practical implementation of hash tables concepts.
Hash Tables
A Hash Table (or Hash Map) is a data structure that maps keys to values for highly efficient lookup. In most cases, it provides O(1) average time complexity for insertion, deletion, and search.
1. How it Works: The Hash Function
The core of a hash table is the hash function. It takes a key (like a string or object) and converts it into an integer index within the array's bounds.
2. Collision Handling
Since multiple keys can hash to the same index (a collision), we need a way to store them.
A. Chaining (Linked Lists)
Each bucket in the array points to a linked list of all elements that hash to that index.
B. Open Addressing (Linear Probing)
If a collision occurs, we look for the next available slot in the array.
3. Implementation in Python
Python's dict and set types are built using highly optimized hash tables.
Comparison: Handling Strategies
| Feature | Chaining | Open Addressing |
|---|---|---|
| Space Usage | Extra space for pointers | All data in the array |
| Clustering | No clustering | Prone to primary clustering |
| Load Factor | Can be > 1 | Must be < 1 |
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Quiz
Quiz
Question 1 of 2What is the average time complexity for searching in a well-distributed hash table?