One of the most powerful and widely used data structures in modern programming is the HashMap (also known as a HashTable or Dictionary). Whether you’re building a REST API, solving a coding interview, or managing key-value data in a project, understanding how HashMaps work internally will level up your skills significantly.

🔍 What is a HashMap?

A HashMap is a data structure that stores data in key-value pairs. You provide a unique key, and the HashMap gives you back the corresponding value.

In JavaScript, this is similar to using an object or Map:

const map = new Map();
map.set("name", "Nadim");
console.log(map.get("name")); // "Nadim"

⚙️ How It Works Internally

Let’s break down what happens under the hood when you use a HashMap:

1. Hash Function

When a key is inserted, a hash function converts it into a number (called the hash code). This number determines where the key-value pair is stored in memory.

// Pseudo code
index = hashFunction(key) % arraySize

2. Index Mapping

The hash code is mapped to an index in an array (buckets). Each index (bucket) can contain one or more entries.

3. Handling Collisions

If two keys hash to the same index, a collision occurs. Common solutions:

  • Chaining: Use a linked list or array at that index to store multiple values.
  • Open Addressing: Find another empty slot based on a probing sequence (e.g., linear probing).

4. Load Factor and Rehashing

When the number of entries exceeds a certain threshold (based on load factor), the HashMap resizes and rehashes all keys to a larger array. This improves performance by reducing collisions.

🧪 Example Walkthrough

Let’s simulate inserting "apple" and "grape":

  • "apple" → hash("apple") = 93484 → index = 93484 % 10 = 4
  • "grape" → hash("grape") = 45524 → index = 45524 % 10 = 4 (collision!)

In this case, both keys are stored in bucket 4, using chaining.

🕐 Time Complexity

Operation Average Case Worst Case
Insert O(1) O(n) (if too many collisions)
Search O(1) O(n)
Delete O(1) O(n)

HashMaps are generally very fast, but performance depends on the quality of the hash function and collision handling.

🧠 Key Takeaways

  • HashMaps allow constant-time lookup for most use cases.
  • Understanding collisions and rehashing helps you write better code.
  • Always choose meaningful keys and avoid too many collisions by using unique identifiers.

👩‍💻 When to Use a HashMap

  • Counting elements: Frequency maps in DSA.
  • Caching: LRU Cache implementations.
  • Indexing data: Quick lookups in APIs.
  • Data deduplication: Tracking unique elements in a stream.

✨ Interview Practice Tip

If you're asked to solve problems like:

  • “Two Sum”
  • “Longest Substring Without Repeating Characters”
  • “Group Anagrams”

...you need to master HashMaps! Practice using them with arrays, strings, and nested structures.

Support My Work ❤️

If you enjoy my content and find it valuable, consider supporting me by buying me a coffee. Your support helps me continue creating and sharing useful resources. Thank you!

Connect with Me 🌍

Let’s stay connected! You can follow me or reach out on these platforms:

🔹 YouTube – Tutorials, insights & tech content

🔹 LinkedIn – Professional updates & networking

🔹 GitHub – My open-source projects & contributions

🔹 Instagram – Behind-the-scenes & personal updates

🔹 X (formerly Twitter) – Quick thoughts & tech discussions

I’d love to hear from you—whether it’s feedback, collaboration ideas, or just a friendly hello!

Disclaimer

This content has been generated with the assistance of AI. While I strive for accuracy and quality, please verify critical information independently.