Hashmap Internal Working & Implementation With Best Examples

When diving deep into data structures, you’ll often encounter HashMaps as one of the most frequently used and fundamental structures. From basic programming tasks to complex software systems, HashMaps are ubiquitous in modern programming languages. But how does a HashMap work internally? What’s the HashMap internal implementation? How does it manage data and handle operations like searching, inserting, and deleting in constant time? In this article, we’ll explore HashMap internal working, understand the HashMap internal implementation, and break down the mechanics of one of the most powerful data structures in computer science.

What is a HashMap?

The key to understanding HashMap internal working is first understanding what a HashMap is. A HashMap stores data as key-value pairs and enables quick access and modification. The key serves as the unique identifier for a piece of data, while the value holds the actual data. This structure functions as an associative array or dictionary, where keys map to specific values.

For example, consider a phone book where each name acts as a key and the phone number is the associated value:

HashMap<String, String> phoneBook = new HashMap<>();
phoneBook.put("Alice", "555-1234");
phoneBook.put("Bob", "555-5678");

Here, “Alice” and “Bob” serve as keys, and “555-1234” and “555-5678” are the values that correspond to them.

To understand the HashMap internal working, let’s understand first that in a typical HashMap implementation, operations such as searching, inserting, or deleting elements occur in constant time on average. This makes it one of the most efficient data structures if implemented correctly.

To understand how this efficiency works, let’s take a closer look at the HashMap internal working in Java.

Also check: Backtracking Algorithm Explained With Examples

The Core Concept Behind HashMaps

At the heart of a HashMap internal working lies the hash function. This function takes a key and generates an integer value that represents the “location” where the key-value pair will be stored in memory. The goal of the hash function is to distribute these hash values evenly across the available locations, thereby reducing the chances of collisions.

For example, if you apply a hash function to the key “Alice“, it might produce a hash code such as 123456. This hash value determines the index where the key-value pair will reside.

The Structure of a HashMap

HashMap internal working/ implementation

A HashMap typically uses an array (also known as a “bucket array”) to store key-value pairs. Each index in this array corresponds to a potential storage location for a key-value pair. These locations are determined by the hash function applied to the key. To understand the HashMap internal working, let’s break down its key components:

Buckets (or Slots): These are individual slots in the internal array that hold the key-value pairs. Each bucket may store a single element or, in cases of collision, a collection of elements.
Hash Function: This function takes a key and returns a hash value (typically an integer), which the HashMap uses to calculate the index or position within the internal array (bucket array) to store the corresponding key-value pair. The hash value helps distribute the keys evenly across the available buckets, aiming to minimize the likelihood of collisions.
Collisions: Collisions occur when two different keys produce the same hash value. As the hash value determines the index in the array, two keys with identical hash values attempt to occupy the same bucket. A well-designed HashMap implementation handles collisions effectively.
Load Factor: The load factor represents the ratio of the number of elements to the capacity of the HashMap. If this ratio becomes too high, the HashMap resizes itself (rehashing) to maintain efficient operations.
Resizing (Rehashing): When the load factor exceeds a predefined threshold, the HashMap increases the size of the underlying array, rehashes all existing elements, and redistributes them into the new array. This process is called rehashing.

HashMap Internal Working & Implementation: Step-by-Step

Now that we understand the basics of HashMap internal working, let’s dive into the steps involved in the common operations: insertion, searching, and deletion.

1. Insertion – HashMap Internal Working & Implementation in Java

When you insert a new key-value pair into a HashMap, the following steps take place:

Example: Inserting a Key-Value Pair

HashMap<String, Integer> wordCount = new HashMap<>();
wordCount.put("apple", 3);
wordCount.put("banana", 5);
wordCount.put("grape", 2);

Hashing the Key: The key “apple” goes through the hash function, which calculates a hash code (let’s assume it’s 42).
Index Calculation: The hash code is used to calculate the index within the internal array where the key-value pair will be stored. If the array size is 10, the index for “apple” would be 42 % 10 = 2.
Collision Handling: If the bucket at the calculated index is empty, the HashMap directly stores the key-value pair there. If there’s already an entry at that index, a collision occurs. The HashMap then uses methods like chaining (linked list) or open addressing to handle this collision.
- Chaining: The HashMap stores collided key-value pairs in a linked list (or other data structures) within the same bucket. When a collision happens, the HashMap appends the new pair to the list at that bucket.
- Open Addressing: With open addressing, the HashMap searches for the next available slot in the array and places the key-value pair there.
Resize (if necessary): If the load factor exceeds a threshold (e.g., 0.75), the HashMap increases the array size. Then, it rehashes all the existing key-value pairs and redistributes them across the new array, ensuring efficient performance.

2. Searching – HashMap Internal Working & Implementation in Java

To search for a value in a HashMap, you perform the following steps:

Example: Searching for a Value

String key = "apple";
Integer count = wordCount.get(key);
System.out.println("Count of apple: " + count);

Hashing the Key: The key “apple” undergoes the same hash function as during insertion.
Index Calculation: The hash code helps calculate the index where the key-value pair should reside. The index for “apple” is 2.
Collision Handling: If multiple key-value pairs exist at the same index (due to collisions), the HashMap searches through the list or probes the array (in case of open addressing) to find the correct key-value pair.
Return Value: If the key is found, the HashMap returns the corresponding value (in this case, 3). If the key doesn’t exist, the HashMap returns null.

3. Deletion – HashMap Internal Working & Implementation in Java

When you delete a key-value pair from a HashMap, you follow these steps:

Example: Deleting a Key-Value Pair

wordCount.remove("banana");

Hashing the Key: The key “banana” is hashed using the hash function.
Index Calculation: The index is determined based on the hash code. Let’s assume the hash code for “banana” points to index 5.
Collision Handling: If collisions exist, the HashMap searches through the list at that index (or probes in case of open addressing) to locate the key-value pair to delete.
Deleting the Pair: Once the correct pair is found, the HashMap removes it from the bucket. If chaining is used, the corresponding linked list is updated to remove the entry. In the case of open addressing, the HashMap re-probes the array to fill the gap left by the removed pair, ensuring that the structure remains intact and accessible.

Also check: Sliding Window Algorithm – Fixed and Variable-Length Solutions

HashMap Internal Working & Implementation: Detailed Explanation

Hash Function

Understanding how a hash function works is key to understanding the HashMap internal working.

The hash function plays a critical role in the HashMap internal working. It ensures that keys are distributed evenly across available array indices. A good hash function minimizes collisions and provides a uniform distribution of keys.

In Java, for example, the String.hashCode() method calculates the hash code by summing up the values of the string’s characters, multiplying each by a prime number.

Most programming languages implement hash functions for different data types to ensure an even distribution of hash values.

For example:

Java’s Object.hashCode() method.
Python’s hash() function.
C++’s std::hash.

Collision Handling Techniques

Collisions are inevitable due to the finite size of the underlying array. However, HashMap implementations use methods like chaining and open addressing to resolve collisions:

Chaining: As we discussed, chaining stores collided key-value pairs in a linked list (or another structure) at the same bucket. This allows multiple pairs to coexist in the same bucket.
Open Addressing: With open addressing, the HashMap looks for the next available slot in the array to store the collided pair. If the slot is occupied, the HashMap probes the next available slot, either using linear probing or quadratic probing.

Resizing and Rehashing

Resizing occurs when the HashMap reaches its capacity. If the load factor exceeds a predefined threshold, the HashMap creates a new, larger array, rehashes all existing elements, and redistributes them across the new array. This rehashing ensures that the HashMap maintains optimal performance as the data grows.

Load Factor

The load factor determines when the HashMap should resize. The default load factor for a HashMap is set to 0.75. This means that the HashMap will resize and rehash its elements when it reaches 75% of its capacity, ensuring that the operations remain efficient. This helps the HashMap maintain efficient operations like search, insert, and delete in O(1) time complexity.

Simple Questions to Help You Understand HashMap Internal Working & Implementation

By now, you should have a clear understanding of the HashMap internal working and implementation. Let’s take a look at a few simple questions to assess how well you can understand the internal working of HashMap. These examples are in Java.

Q1: What will the output be for the code below?

HashMap<Integer, String> map = new HashMap<>();
map.put(1, "ABC");
map.put(2, "PQR");
map.put(1, "MNO");
map.put(2, "DEF");
map.put(2, "KLO");
System.out.println("Size = " + map.size());

Q2: What will the output be for the code below?

HashMap<Integer, String> map = new HashMap<>();
System.out.println(map.get(1));

Q3: What will the output be for the code below?

HashMap<Integer, String> map = new HashMap<>();
map.put(1, "ABC");
map.put(2, "PQR");
HashMap<Integer, String> map1 = new HashMap<>(map);
map.remove(1);
System.out.println(map1.size());

Q4: What will the output be for the code below?

HashMap<Integer, String> map = new HashMap<>();
map.put(1, "ABC");
map.put(2, "PQR");
HashMap<Integer, String> map1 = map;
map.remove(1);
System.out.println(map1.size());

Answers

Q1: 2
Q2: null
Q3: 2
Q4: 1

Examples For Understanding HashMap Internal Working & Implementation

Two Sum (PayTM, Meesho, Adobe, Oracle, Zomato)
Letter Combinations of a Phone Number (Ola, Cisco, Siemens, Atlassian)
Valid Sudoku (Uber, Cognizant, Samsung)
Group Anagrams (Intuit, Salesforce, Practo, Infosys)
Word Break (IBM, Apple, Morgan Stanley, Cisco)
Sudoku Solver (Oracle, JP Morgan, Google)

Conclusion

HashMaps are a sophisticated and efficient way of storing key-value pairs. By utilizing a combination of a hash function, collision handling techniques, and resizing, the HashMap data structure ensures fast and efficient data access.

Understanding the HashMap internal working helps you optimize your code, avoid performance pitfalls, and use this data structure effectively. Whether you’re working with large datasets or need an efficient way to store and retrieve data, HashMaps are a versatile tool in any programmer’s toolkit.

By grasping the principles of hashing, collision handling, and resizing, you can make better decisions about when and how to use a HashMap, improving the performance and efficiency of your software systems.

That’s all on the HashMap internal working and its implementation.

To keep yourself up to date with our latest content, please subscribe to our newsletter by dropping your email address here: Signup for Our Newsletter.

Please follow us on Medium.