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🗺️ Hash Map Pattern

Key-Value Storage with O(1) Access

👶Explain Like I'm 5

Imagine a magical dictionary that works super fast...

You tell it a KEY (like “apple”), and it instantly shows you the VALUE (like “red”). You don't flip through pages - it just knows where everything is!

Why is this useful? Hash maps let us find things instantly, no matter how much data we have. It's like magic!

Real-world use cases:

  • Phone contacts (name → phone number)
  • Dictionary (word → definition)
  • Caching frequently used data
  • Counting frequency of elements
  • Finding duplicates or unique items
  • Two Sum problem and variants

Interactive Hash Map Visualization

🎯 Hash Map: Key-Value pairs for O(1) lookups!

Hash Map is empty 📭

Size
0
Keys Count
0
Is Empty?
Yes

SET (Add/Update Key-Value)

Search Operations

Hash Map Operations

SET(key, value)

Add or update a key-value pair. Average O(1) time complexity.

🔍GET(key)

Retrieve value for a key. Average O(1) time complexity.

HAS(key)

Check if key exists. Average O(1) time complexity.

🗑️DELETE(key)

Remove a key-value pair. Average O(1) time complexity.

🔮How Does It Work? (Hash Function Magic)

1. Hash Function converts KEY → INDEX

Example: “apple” → hash → index 3

2. Store VALUE at that INDEX

Store “red” at position 3

3. Lookup is instant!

“apple” → hash → index 3 → get value “red”

🎯When to Use Hash Map?

Keywords: “count”, “frequency”, “lookup”, “find pair”, “cache”
Need to: Store and retrieve data quickly using keys
Problems: Two Sum, Group Anagrams, Frequency Counter, LRU Cache
💡
Pro Tip: If you see “find in O(1)” or “count occurrences”, think Hash Map!
Common Pattern: Trade space (O(n)) for time (O(1)) - store data to access instantly