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Concept Breakdowns

Hash Table Collision Resolution Techniques

Flashcards on hash table collision resolution — a core topic for CS algorithms courses, system design interviews, and competitive programming. A collision occurs when two keys hash to the same index. Choosing the right resolution strategy (chaining vs. open addressing) directly impacts real-world performance in databases, caches, and language runtimes.

Interactive Deck

5 Cards
1
Front

What is a hash collision?

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1
Back

A collision occurs when two distinct keys produce the same hash index. Collisions are inevitable — the birthday paradox guarantees them for large key sets.

2
Front

Separate chaining resolution

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2
Back

Separate chaining: Each bucket stores a linked list of all entries that hash to it.

  • Simple to implement
  • O(1) avg, O(n) worst-case lookup
3
Front

Linear probing resolution

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3
Back

Linear probing: On collision, check index+1, +2, +3... until an empty slot is found. Suffers from primary clustering — long runs slow insertions.

4
Locked

What is the load factor?

5
Locked

Quadratic probing vs. double hashing

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Frequently Asked Questions

What is the difference between chaining and open addressing?

Chaining stores colliding elements outside the table (in linked lists), allowing load factor > 1. Open addressing stores all elements in the table itself, requiring load factor < 1 and careful probing to avoid clustering.

Why is load factor important in hash tables?

Load factor determines how full the table is. A high load factor increases collision probability, degrading average lookup from O(1) toward O(n). Most implementations resize at α = 0.7–0.75 to maintain performance.

How many collision resolution techniques are there?

The main techniques are:

  • Separate chaining (most common in language runtimes)
  • Linear probing
  • Quadratic probing
  • Double hashing
  • Robin Hood hashing (modern variant of open addressing)

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