HashMap is a Data Structure every SWE should know.

They provide an efficient way to store and retrieve data by mapping keys to values.

Here are 6 things you need to know about Hashmaps:

1. Hashing

Hashing is the process of converting a key into a hash code.

The hash code is an integer representing the key;

We use the key to compute the index in the hashmap's underlying array.

A good hashing function should distribute the keys uniformly to reduce collisions.

2. Collisions

Collisions occur when two different keys produce the same hash code.

Handling collisions is a critical aspect of hashmap implementation.

Resolution techniques:

- Chaining

- Open Addressing

3. Time Complexity

Hashmaps offer efficient:

- Lookup

- Insertion

- Deletion operations

On average, these operations have a time complexity of O(1).

But, in the worst case, when collisions are frequent, the time complexity can degrade to O(n), where n is the # of elements.

4. Load Factor

The load factor of a hashmap is the ratio of the number of elements stored to the array's size.

A high load factor increases the likelihood of collisions and degrades performance.

You can calculate the load factor using this formula.

n = number of elements stored

m = array's size

\(a = n/m\)

Resizing the hashmap and rehashing the elements can reduce this issue.

5. Iterating through Elements

Hashmaps do not maintain a specific order for their elements.

When iterating over a hashmap, it does not guarantee the order in retrieving elements.

If you need order, structures like linked lists or balanced trees can help.

6. Hash Function Selection

Choosing an appropriate hash function is important for a good distribution of keys.

The hash function should be fast and produce distributed hash codes to cut collisions.

Common applications of using hashmaps:

- Caching

- Indexing and Searching

- Databases

- Frequency Counting

- Graph Algorithms

- Distributed Systems

In simple terms, they map keys to values.

A hashmap is a collection of key-value pairs where each key is unique.

Have you used hashmaps?