Hashing and Hash Tables


Hashing and Hash Tables in data structure
Hashing and Hash Tables in data structure

Hashing and Hash Tables in data structure

1. Introduction to Hashing

  • Definition of hashing.
  • The need for hashing in computer science.
  • Real-world analogies of hashing (e.g., dictionaries, phonebooks).

2. Key Concepts in Hashing

  • Hash Function:
    • Definition and purpose.
    • Properties of a good hash function (deterministic, efficient, uniform distribution, etc.).
  • Hash Table:
    • Data structure overview.
    • How hash tables store and retrieve data efficiently.
  • Keys and Values:
    • Explanation of key-value pairs.
    • Examples in everyday applications (e.g., username-password mappings).

3. How Hash Tables Work

  • Inserting a key-value pair.
  • Retrieving a value using the key.
  • Deleting a key-value pair.

4. Collisions in Hashing

  • Definition of collision.
  • Causes of collisions.
  • Collision resolution techniques:
    • Chaining (Separate Chaining).
    • Open Addressing:
      • Linear Probing.
      • Quadratic Probing.
      • Double Hashing.

5. Hash Table Operations

  • Time complexities for:
    • Insertion.
    • Search.
    • Deletion.
  • Best, worst, and average-case scenarios.

6. Applications of Hashing and Hash Tables

  • Caching mechanisms.
  • HashMaps in programming languages (e.g., Python dictionaries, Java HashMap).
  • Cryptography and data security.
  • Database indexing.

7. Limitations of Hash Tables

  • Hash collisions.
  • Inefficiency with poor hash functions.
  • Memory overhead compared to other data structures.

8. Designing a Hash Function

  • Guidelines for creating a good hash function.
  • Examples of common hash functions (e.g., division method, multiplication method).

9. Advanced Hashing Techniques

  • Perfect hashing.
  • Dynamic resizing of hash tables (rehashing).
  • Bloom filters (brief introduction).

10. Practical Implementation

  • Code examples:
    • Hash table implementation in Python, Java, or C++.
    • Demonstration of collision handling.
  • Sample problem and solution using a hash table.

11. Comparison with Other Data Structures

  • Hash tables vs. arrays.
  • Hash tables vs. trees (e.g., Binary Search Tree, AVL Tree).

12. Conclusion and Key Takeaways

  • Recap of hashing concepts and benefits.
  • When and why to use hash tables.


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