Non-Linear Data StructuresIntermediate LevelStudy Time: 20 mins

Hash Table Data Structure

A Hash Table maps string/number keys to array index buckets using a mathematical Hash Function, delivering average O(1) key lookups.

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Hash Table Key Lookup & Insertion Step-by-Step

Sample Input:Hash Table Capacity: 10, Key: 'cat', Value: 42
Step 1

Compute Hash Code

Pass string key 'cat' to hash function to compute integer ASCII code total.

Hash('cat') = ('c':99 + 'a':97 + 't':116) = 312
Hash functions transform variable-length keys into fixed integer codes.
Step 2

Map Code to Bucket Array Index

Apply modulo arithmetic using array capacity: index = hashCode % capacity.

Index = 312 % 10 = Bucket 2
Modulo mapping ensures bucket index remains within array bounds [0..capacity-1].
Step 3

Store Key-Value Pair in Bucket

Write key-value entry ('cat', 42) into Bucket 2. If occupied, handle collision via linked list.

Bucket 2 -> [ ('cat': 42) ]
Average constant time lookup O(1) achieved when load factor is low.

Real-world analogy: Library Index Catalog

Hash Function
Collision
Hover over items for details

Books are assigned letter codes. Instead of scanning all shelves, you calculate the code index to walk directly to the exact shelf slot.

  • Hash Function= Converts input keys into target integer array bucket indices.
  • Collision= Occurs when two different keys map to the same bucket index.

Operations explained

Complexity analysis

Scenario Simulator: Dataset Size (N = 500)
Presets:
N = 10N = 10,000
Complexity Curve MapHash Table: O(1) Average Bucket Access
Time (t)Size N (500)N = 500O(N²)O(N log N)O(N)O(log N)O(1)Hash Function Lo
Time Steps (N=500)

1 step (Constant)

O(1) Average Bucket Access

Space Footprint

O(N) Bucket Storage

Memory growth rate as N expands.

Performance Rank
Optimal Tier

Relative efficiency rating for large N.

OperationTime ComplexitySpace Complexity
SearchO(1) AvgO(N)
InsertionO(1) AvgO(N)
DeletionO(1) AvgO(N)

Code implementations

const map = new Map();
map.set("score", 99);
const score = map.get("score");

Real-world applications

Case 01

Database Caching

Stores key-value lookup caches in Memcached/Redis.

Case 02

Duplicate Checking

Detects unique user handles instantly.

Interview questions

Topic Knowledge Test & Assessment

10 Questions Assessment

Hash Table Knowledge & Skill Test

Validate your conceptual understanding, operation mechanics, and core concepts of Hash Table.