Key Takeaways

  1. Hashing is the technique of mapping a large data set to a smaller range of indices (buckets) using a Hash Function.
  2. Collisions occur when two different keys map to the same index. They are handled via Separate Chaining (Linked List) or Open Addressing (Probing).
  3. Hash Map (Key-Value) and Hash Set (Unique Keys) are the most versatile data structures.
  4. Time Complexity: Average Case O(1) for Insert, Delete, and Search. Worst Case O(N) (collisions).
  5. Common Patterns:
    • Frequency Map: Counting occurrences (Anagrams).
    • Difference Lookup: target - current (Two Sum).
    • Prefix Sum + Hash Map: Subarray Sum Equals K.

Interactive Flashcards

Collision

When two different keys hash to the same index/bucket.

Load Factor

Ratio of elements to buckets. High load factor = more collisions. Usually resize at 0.75.

Chaining

Handling collisions by storing a linked list of items in each bucket.

LRU Cache

Least Recently Used. Uses Hash Map + Doubly Linked List for O(1) operations.

Cheat Sheet

Operation Average Case Worst Case
Insert O(1) O(N)
Delete O(1) O(N)
Search O(1) O(N)
Space O(N) O(N)

Python Reference

# Map (Dictionary)
d = {}
d['key'] = 'val'  # Put
val = d['key']    # Get
if 'key' in d:    # ContainsKey
  del d['key']  # Remove

# Set
s = set()
s.add(1)          # Add
if 1 in s:        # Contains
  s.remove(1)   # Remove

Next Steps

Now that you’ve mastered Hashing, it’s time to dive into Recursion, where functions call themselves!

Review & Cheat Sheet

[!NOTE] This module explores the core principles of Review & Cheat Sheet, deriving solutions from first principles and hardware constraints to build world-class, production-ready expertise.

1. Practice in the Vault

Looking to solidify your understanding? Head over to the Problem Vault to solve curated problems related to this module with detailed walkthroughs and optimal solutions.