Hashing in Data Structures
Hashing is a technique used to map data of arbitrary size to a fixed-size value (hash value or hash code). It enables fast data access and is widely used in databases, caches, and symbol tables.
What is Hashing?
Hashing uses a hash function to convert a key into an index in a hash table where the corresponding value is stored.
Key Points:
- Key → Hash Function → Index
- Index determines storage location in the hash table
- Provides O(1) average access time
Hash Function
A hash function converts input keys into integer indices.
Example:hash(key) = key % table_size
Good hash functions:
- Minimize collisions
- Distribute keys uniformly
Collision Handling
Collisions occur when two keys map to the same index.
1. Chaining
- Each table entry points to a linked list of keys
- Easy to implement
2. Open Addressing
- Finds another empty slot
- Techniques: Linear probing, Quadratic probing, Double hashing
Types of Hashing
- Static Hashing: Table size fixed
- Dynamic Hashing: Table size changes dynamically
Advantages of Hashing
- Very fast data access (O(1) average)
- Efficient for large datasets
- Easy insertion, deletion, and search
Disadvantages of Hashing
- Collisions may occur
- Not suitable for ordered data
- Requires good hash function design
Real-World Applications
- Password storage
- Caches in web browsers
- Databases and indexing
- Symbol tables in compilers
- Network routers
Conclusion
Hashing is a powerful technique for fast data retrieval and storage. Understanding hash functions, collision resolution, and hash table design is essential for efficient data structure implementation.