Advanced Look at How Hashmaps Work Internally in Java

7 min readJan 15, 2024

Introduction

Hashmaps in Java are one of the fundamental data structures used in programming. They are a part of the Java Collections Framework and provide a means to store data in key-value pairs. This article goes into the internal workings of hashmaps, offering a deeper understanding of their functionality and performance implications.

Understanding the Basics of Hashmaps

What is a Hashmap?

A hashmap is a data structure that associates keys with values. It is designed to optimize the retrieval of values based on key information. In Java, HashMap is a part of the java.util package and implements the Map interface.

Example of a Simple Hashmap in Java

To illustrate the basic usage of a hashmap, let’s consider a simple example. This code snippet demonstrates how to create a hashmap, add key-value pairs, and retrieve a value:

import java.util.HashMap;

public class SimpleHashMapExample {
    public static void main(String[] args) {
        // Creating a hashmap
        HashMap<String, Integer> ageOfFriends = new HashMap<>();

        // Adding key-value pairs to the hashmap
        ageOfFriends.put("Alice", 30);
        ageOfFriends.put("Bob", 25);
        ageOfFriends.put("Charlie", 28);

        // Retrieving the age of 'Bob'
        int age = ageOfFriends.get("Bob");
        System.out.println("Bob's age: " + age);
    }
}

In this example, we create a hashmap named ageOfFriends where the keys are of type String (representing names), and the values are of type Integer (representing ages). We add some entries with put and then retrieve an entry using get.

Key Characteristics

Data Organization: Hashmaps store data in an array format where each data element, referred to as a ‘bucket’, holds the key-value pairs.
Hash Function: This function is critical for determining the index in the array where a particular key-value pair will be stored. The hash function takes a key and returns an integer, which is then used to calculate the index.
Collision Handling: Collisions occur when different keys produce the same index. Java hashmaps handle this using methods like chaining, where a single bucket stores multiple entries in a linked list or a tree structure.
Capacity and Load Factor: The capacity is the number of buckets in the hashmap, and the load factor is a measure that indicates when to increase the capacity automatically. The default load factor is 0.75, which offers a good trade-off between time and space costs.

How Hashmaps Work

Insertion (put Method): When a key-value pair is added, the hashmap uses the hash function to compute the index for the key and places the value at that index.

Key Hashing: The hashCode() method of the key object is invoked to compute the hash code, which is then transformed into the index.

Collision Resolution: If the computed index

already contains other entries (collision), the hashmap places the new entry in a linked list or tree at that index, depending on the number of items in the bucket.

Retrieval (get Method): To retrieve a value, the hashmap computes the index using the same hash function and then searches through the entries at that index (if there are multiple due to collisions) to find the matching key.
Equality Check: For both insertion and retrieval, the equality of keys is checked using the equals() method. This ensures that even if two keys have the same hash code, they can still be distinguished.

Understanding `hashCode()` and `equals()`

The hashCode() Method: This method, defined in the Object class, returns an integer representation of the object memory address by default. However, it’s often overridden in custom classes to produce distinct hash codes for different instances.
The equals() Method: Also defined in Object, this method checks if two objects are the same. Like hashCode(), it’s frequently overridden to provide logical equality checks rather than the default reference equality check.

Resizing and Performance

Resizing: As entries are added to a hashmap, its capacity may need to increase to maintain efficient access times. This resizing is an expensive operation, as it involves creating a new array and rehashing all existing keys.
Performance Implications: The performance of a hashmap is significantly influenced by the hash function’s quality, the load factor, and the collision resolution method. A poor hash function can lead to many collisions, degrading the performance from the ideal O(1) time complexity.

Internal Workings of a Java Hashmap

Hash Function and Index Computation

At the core of a Java hashmap’s efficiency is its hash function. The hash function takes a key and returns an integer, which the hashmap then uses to determine where to store the key-value pair.

Hash Code Computation: Java uses the hashCode() method of the key object to compute an initial hash value. However, this value is further processed to reduce collisions.
Index Calculation: The computed hash code is then used to find the index in the array of buckets. This is typically done using the formula index = hashCode(key) & (n-1), where n is the number of buckets. The bitwise AND operation with n-1 ensures that the index is within the array bounds.

Handling Collisions

Since different keys can produce the same hash code, collisions are an inevitable part of using hashmaps.

Chaining: Initially, Java hashmaps resolve collisions through chaining, where each bucket contains a linked list of entries that hashed to the same index.
Treeification: In Java 8 and later, if a bucket becomes too crowded (typically when the number of items in a bucket exceeds a certain threshold), the linked list is converted into a balanced tree. This significantly improves the worst-case performance of lookups.

Dynamic Resizing

To maintain its efficiency, a hashmap must balance between the number of buckets (capacity) and the number of key-value pairs (size).

Load Factor and Capacity: The load factor, a measure of how full the hashmap is allowed to get before its capacity is automatically increased, is crucial in this balancing act. When the number of entries in the hashmap exceeds the product of the load factor and current capacity, the hashmap is resized.
Resizing Process: Resizing involves creating a new array of buckets and then rehashing all the existing keys to find out their new locations in the array. This process can be computationally expensive, so it’s important to set the initial capacity and load factor with care.

Optimizing HashMap Performance

Initial Capacity and Load Factor: Choosing the right initial capacity and load factor can significantly impact performance, especially for hashmaps that store a large number of key-value pairs.
Custom Hash Functions: For custom object keys, implementing a strong hashCode() method that evenly distributes keys can enhance performance by reducing collisions.

Performance Considerations

Time Complexity

The efficiency of hashmaps in operations such as put, get, and remove is a key advantage of using them.

Average Case Complexity: In the average case, these operations have a time complexity of O(1), meaning they can be performed in constant time. This is because the hash function usually distributes keys uniformly across the buckets, leading to a quick computation of the index and retrieval or insertion of the value.
Worst Case Complexity: The worst-case scenario occurs when multiple keys are hashed to the same index, leading to a large number of collisions. Before Java 8, this would degrade the performance to O(n) for operations, as it would involve traversing a linked list. However, with the introduction of tree structures for buckets with many collisions, this has been improved to O(log n).

Memory Usage

The memory efficiency of a hashmap is determined by its capacity (the number of buckets) and its size (the number of key-value pairs stored).

Balancing Capacity and Size: A larger capacity reduces the likelihood of collisions and thus improves performance. However, it also increases the memory footprint. Conversely, a smaller capacity saves memory but can lead to more collisions, reducing performance.
Dynamic Resizing: Dynamic resizing impacts memory usage. When a hashmap is resized, it temporarily requires space for both the old and new arrays of buckets, which can lead to a spike in memory usage.

Best Practices for Optimizing Performance

Setting Initial Capacity and Load Factor: If the approximate number of entries is known in advance, setting the initial capacity to accommodate this number can prevent or minimize the need for resizing.
Custom hashCode() Implementation: For custom object keys, implementing a well-thought-out hashCode() method can prevent clustering of keys and improve performance.
Using Immutable Keys: Using immutable objects as keys can prevent issues related to mutable keys, where changes to the key object could affect its hashcode and equality, leading to unexpected behavior.

Impact of Java Versions

The performance of hashmaps has been improved in recent versions of Java, particularly since Java 8. The introduction of tree bins for handling collisions in densely populated buckets has been a significant enhancement, reducing the time complexity in the worst-case scenario.

Conclusion

This exploration of Java hashmaps has revealed the intricacies behind one of the most versatile structures in the Java Collections Framework. We’ve seen how hashmaps efficiently manage key-value pairs through sophisticated hashing, collision handling, and dynamic resizing. The insights gained here are crucial for any Java developer looking to optimize data management and performance in their applications. As we continue to navigate the evolving landscape of Java programming, understanding these fundamental concepts will be instrumental in crafting efficient and effective solutions.