What is a HashMap in Java?

Introduction#

In the realm of Java programming, data structures play a vital role in efficiently organizing and manipulating data. One such indispensable data structure is the Java HashMap. With its powerful capabilities and versatile nature, HashMaps have become a cornerstone of Java development. In this blog, we will explore what the Java HashMap is, including coding examples, their benefits, and potential disadvantages.

Java HashMap: Behind the scenes#

A Java HashMap stores key-value pairs where each key is different. This makes it easier and faster to find the value.

It achieves efficient storage and retrieval of key-value pairs by combining hash tables and linked lists in its back-end implementation. Let's dive into the details.

Buckets and Hashing

The HashMap internally uses an array of buckets to store the elements.

Each bucket is capable of holding multiple elements because of possible hash collisions. Note that collisions can happen when multiple keys have the same code.
The value for the key (i.e., the hash code) is calculated using the hashCode() method whenever a key-value pair is added to the HashMap. The value of the key is used to determine the specific bucket where the element will be added in the list.
Java’s hashCode() function spreads the hash codes uniformly across the array to minimize collisions and improve performance.

Linked list nodes

Each bucket in the HashMap is basically a linked list of nodes.

Whenever two items have the same code and need to go in the same bucket (otherwise known as a collision), we just add the new items to the front of that list.
In the linked list, each node holds both the key-value information and a reference to the next node that comes after it in the list.

Load factor and rehashing

The HashMap maintains a load factor that represents the ratio of filled buckets to the total number of buckets.
When the load factor exceeds a certain threshold (the default is 0.75), the HashMap automatically increases the size of the underlying bucket array and hashes all the elements again. This process is called rehashing.
Rehashing involves creating a new array with a larger capacity, recalculating the hash codes for all the keys, and redistributing the elements across the new array. This helps maintain a balanced distribution of elements and keeps the average bucket size small, improving performance.

Capacity, load factor tuning and rehashing strategy#

By default, a HashMap starts with capacity 16 and load factor 0.75.
But smart tuning can significantly improve performance for your use case.

Key tuning strategies#

Initial capacity:
If you know the expected number of entries, set
initialCapacity = (int)(expected / loadFactor) + 1
to avoid costly resizes.
Lowering load factor:
Reducing the load factor (e.g., to 0.5) lowers collisions
but increases memory overhead — a trade-off of memory for speed.
Custom thresholds:
Resizing is expensive since it requires rehashing all entries.
For real-time systems, avoid frequent rehashes by oversizing initially.
Resize doubling:
HashMap doubles its bucket array on resize.
That means load factor thresholds are tied to capacity doubling.
Predict worst-case:
In high-throughput maps, prevent peak rehash times by pre-sizing
or keeping spare headroom.

These tuning strategies help prevent performance hiccups
for large or long-lived HashMaps.

Java 8+ enhancements: Treeification for performance#

In Java 8 and later, HashMap introduces an important enhancement: tree bins.
When a single bucket’s linked list grows beyond a threshold (default 8),
and the table size is sufficiently large (minimum capacity 64),
the bucket is converted from a linked list into a red-black tree.

This ensures that in the worst case, operations degrade to O(log n) instead of O(n).
When collisions are many, treeification prevents pathological performance degradation.

Key points#

The conversion threshold is controlled by TREEIFY_THRESHOLD (default 8)
and MIN_TREEIFY_CAPACITY (default 64).
If table capacity is small, a bucket won’t treeify — even with many entries —
until rehashing increases the table size.
On removal or resizing, a tree can revert to a linked list
if elements fall below UNTREEIFY_THRESHOLD (default 6).
Tree nodes use a subclass TreeNode<K,V>, which preserves ordering
(by key’s Comparable or hash tie) and integrates with existing chaining logic.

This change lets modern HashMap handle collision floods more reliably
under adversarial keys.

Designing hashCode() and equals(): the key to performance#

The correctness and performance of any HashMap usage heavily depend on
properly implementing hashCode() and equals() for your key objects.

Best practices#

Consistent contract:
If two objects are equal per equals(), they must return the same hashCode().
Violations break lookup correctness.
Good hash spread:
Combine fields in a way that spreads bits; avoid poor distributions
(e.g., values that are always multiples of 16).
Immutable keys:
Keys should remain immutable while used in a map.
Changing fields used in hashCode() after insertion will make the key “lost.”
Use Objects.hash(...) or manual combine:
Combine multiple fields via bit shifts, XOR, or multipliers.
For performance-critical code, avoid creating arrays in hashCode().
Avoid collisions:
Low-collision hash codes reduce the burden on treeification
and keep average complexity tight.
Overrides that call super:
Avoid including super.hashCode() unless necessary,
as it can spoil a clean hash distribution.

If a badly written hashCode() causes many keys to land in the same bucket,
performance will degrade — even with the treeification fallback in place.

Implementing different HashMap functions#

Let’s take a look at some code examples to better understand how the Java HashMap works.

Java

import java.util.HashMap;
public class main {
    public static void main(String[] args) {
        // Creating a HashMap
        HashMap<String, Integer> studentScores = new HashMap<>();
        
        // Adding key-value pairs
        studentScores.put("John", 95);
        studentScores.put("Emily", 87);
        studentScores.put("Michael", 92);
        System.out.println(studentScores);
        //updating John's score. 
        //Note that the key "John" already exists. 
        //The score is simply updated
        studentScores.put("John", 85);
        System.out.println(studentScores);
    }
}

Java

import java.util.HashMap;
public class main {
    public static void main(String[] args) {
        // Creating a HashMap
        HashMap<String, Integer> studentScores = new HashMap<>();
        // Adding key-value pairs
        studentScores.put("John", 95);
        studentScores.put("Emily", 87);
        studentScores.put("Michael", 92);
        // Retrieving a value using a key
        int johnScore = studentScores.get("John");
        System.out.println("John's score: " + johnScore);
    }
}

Java

import java.util.HashMap;
public class main {
    public static void main(String[] args) {
        // Creating a HashMap
        HashMap<String, Integer> studentScores = new HashMap<>();
        // Adding key-value pairs
        studentScores.put("John", 95);
        studentScores.put("Emily", 87);
        studentScores.put("Michael", 92);
        // Checking if a key exists
        boolean containsEmily = studentScores.containsKey("Emily");
        System.out.println("Contains Emily? " + containsEmily);
    }
}

Java

import java.util.HashMap;
public class main {
    public static void main(String[] args) {
        // Creating a HashMap
        HashMap<String, Integer> studentScores = new HashMap<>();
        // Adding key-value pairs
        studentScores.put("John", 95);
        studentScores.put("Emily", 87);
        studentScores.put("Michael", 92);
        // Checking if a value exists
        boolean containsScore92 = studentScores.containsValue(92);
        System.out.println("Contains score 92? " + containsScore92);
    }
}

Java

import java.util.HashMap;
public class main {
    public static void main(String[] args) {
        // Creating a HashMap
        HashMap<String, Integer> studentScores = new HashMap<>();
        // Adding key-value pairs
        studentScores.put("John", 95);
        studentScores.put("Emily", 87);
        studentScores.put("Michael", 92);
        // Removing a key-value pair
        Integer removedScore = studentScores.remove("Michael");
        System.out.println("Removed score: " + removedScore);
    }
}

Java

import java.util.HashMap;
import java.util.Set;
public class main {
    public static void main(String[] args) {
        // Creating a HashMap
        HashMap<String, Integer> studentScores = new HashMap<>();
        // Adding key-value pairs
        studentScores.put("John", 95);
        studentScores.put("Emily", 87);
        studentScores.put("Michael", 92);
        // Retrieving all the keys
        Set<String> keys = studentScores.keySet();
        System.out.println("Keys in the HashMap: " + keys);
    }
}

Java

import java.util.HashMap;
import java.util.Collection;
public class main {
    public static void main(String[] args) {
        // Creating a HashMap
        HashMap<String, Integer> studentScores = new HashMap<>();
        // Adding key-value pairs
        studentScores.put("John", 95);
        studentScores.put("Emily", 87);
        studentScores.put("Michael", 92);
        // Retrieving all the values
        Collection<Integer> values = studentScores.values();
        System.out.println("Values in the HashMap: " + values);
    }
}

Java

import java.util.HashMap;
import java.util.Set;
public class main {
    public static void main(String[] args) {
        // Creating a HashMap
        HashMap<String, Integer> studentScores = new HashMap<>();
        // Adding key-value pairs
        studentScores.put("John", 95);
        studentScores.put("Emily", 87);
        studentScores.put("Michael", 92);
        // Retreiving all enties
        Set<HashMap.Entry<String, Integer>> entries = studentScores.entrySet();
        System.out.println("Entries in the HashMap: " + entries);
    }
}

Java

import java.util.HashMap;
import java.util.Set;
public class main {
    public static void main(String[] args) {
        // Creating a HashMap
        HashMap<String, Integer> studentScores = new HashMap<>();
        // Adding key-value pairs
        studentScores.put("John", 95);
        studentScores.put("Emily", 87);
        studentScores.put("Michael", 92);
        // Clearing the HashMap
        studentScores.clear();
        System.out.println("HashMap cleared. Size: " + studentScores.size());
    }
}

Java

import java.util.HashMap;
import java.util.Iterator;
public class HashMapIterationExample {
    public static void main(String[] args) {
        // Create a HashMap
        HashMap<String, Integer> studentScores = new HashMap<>();
        // Add elements to the HashMap
        studentScores.put("John", 95);
        studentScores.put("Emily", 87);
        studentScores.put("Michael", 92);
        // Iterating using EntrySet()
        System.out.println("Iterating using EntrySet():");
        for (HashMap.Entry<String, Integer> entry : studentScores.entrySet()) {
            String key = entry.getKey();
            Integer value = entry.getValue();
            System.out.println("Key: " + key + ", Value: " + value);
        }
        // Iterating using KeySet()
        System.out.println("\nIterating using KeySet():");
        for (String key : studentScores.keySet()) {
            Integer value = studentScores.get(key);
            System.out.println("Key: " + key + ", Value: " + value);
        }
        // Iterating using Iterator
        System.out.println("\nIterating using Iterator:");
        Iterator<HashMap.Entry<String, Integer>> iterator = studentScores.entrySet().iterator();
        while (iterator.hasNext()) {
            HashMap.Entry<String, Integer> entry = iterator.next();
            String key = entry.getKey();
            Integer value = entry.getValue();
            System.out.println("Key: " + key + ", Value: " + value);
        }
    }
}

In the example above, we create a class called HashMapIterationExample containing a main method. The necessary classes, HashMap and Iterator from the java.util package, are imported.

Inside the main method, we create a HashMap<String, Integer> called studentScores and add some key-value pairs to it.

The different ways to iterate over the HashMaps are as follows:

Lines 15–20: In this approach, the entrySet() method returns a set of key-value pairs (Map.Entry) in the HashMap. The for-each loop then iterates over each entry, allowing access to the key via getKey(), and the value via getValue().
Lines 23–27: The keySet() method returns a set of keys present in the HashMap. We can then use each key to retrieve the corresponding value using the get(key) method.
Lines 30–37: This approach involves obtaining an iterator using entrySet().iterator() and then iterating over the HashMap using a while loop. Similar to the first method, the key and the value can be accessed using getKey() and getValue(), respectively.

Data structures equivalent to Java HashMap in different programming languages#

What is the Java HashMap? The Java HashMap is a popular and powerful data structure in the Java programming language. However, when working with other programming languages, we have some equivalent data structures to choose from.

In Python, the data structure equivalent to a Java HashMap is the dictionary, which allows us to store key-value pairs where keys are unique and values can be of any type.

In C++, the unordered_map from the Standard Template Library (STL) provides a functionality similar to that of a HashMap. It offers fast insertion, retrieval, and deletion operations.

In JavaScript, objects can be used as data structures similar to HashMap. Keys can be strings or symbols, and values can be of any type.

Below is an example of the codes in C++, Python, and JavaScript.

#include <iostream>
#include <unordered_map>
int main() {
    // Creating an unordered_map
    std::unordered_map<std::string, int> student_scores = {
        {"John", 95},
        {"Emily", 87},
        {"Michael", 92}
    };
    // Accessing a value using a key
    int john_score = student_scores["John"];
    std::cout << "John's score: " << john_score << std::endl;
    // Updating a value
    student_scores["Emily"] = 90;
    // Removing a key-value pair
    student_scores.erase("Michael");
    return 0;
}

HashMap-equivalent code in C++

Fast retrieval	It offers constant-time performance for basic operations such as insertion, retrieval, and deletion, making them highly efficient for handling large datasets.
Flexible key-value pairing	It allows the association of any object as a key and any object as a value, enabling developers to create custom data structures for specific use cases.
No duplicate keys	It enforces uniqueness of keys, ensuring that no duplicate keys exist within the collection.
Dynamic sizing	It automatically adjusts their size to accommodate more elements, eliminating the need for manual resizing.

No preserved order	It does not guarantee any particular order of elements. If we require ordered key-value pairs, we need to consider using a `LinkedHashMap` instead.
Null key limitation	It allows a single null key but can't have duplicate null keys.
Hash collision performance	In rare cases, different keys may hash to the same index, resulting in a collision. This situation can degrade performance, especially when dealing with a large number of collisions. However, Java's `HashMap` implementation uses techniques like separate chaining and balanced trees to handle collisions efficiently.
Thread safety	It is not inherently thread-safe. If the application involves multiple threads accessing or modifying the map concurrently, we need to use `ConcurrentHashMap`, which provides thread-safe operations without the need for external synchronization.

Thread safety and concurrent maps#

A major limitation of HashMap is its lack of built-in thread safety.
Concurrent writes or mixed reads and writes can lead to data corruption
or even infinite loops in internal chains.
If you need thread-safe maps, use one of these approaches:

Thread-safe alternatives#

ConcurrentHashMap:
Supports concurrent reads and writes using lock striping and non-blocking reads.
Does not allow null keys or values.
Collections.synchronizedMap(…):
Wraps a map with synchronization — simple but uses coarse-grained locking (global lock).
Concurrent alternatives:
Depending on the use case, ConcurrentSkipListMap or CopyOnWriteArrayList (for small maps) may suffice.

Example usage#

If you ever use a vanilla HashMap in a multi-threaded context,
wrap it or switch to a concurrent implementation to ensure safety.

Map variants and when to use them#

HashMap is a great default, but the Java ecosystem offers variants
with extra semantics or ordering guarantees.

Common map variants#

LinkedHashMap:
Maintains insertion order (or access order) while retaining hash performance.
Use for LRU caches.
TreeMap:
Implements NavigableMap, sorted by key (natural or comparator).
Operations are O(log n) rather than constant time.
WeakHashMap:
Keys are referenced weakly, and entries can be garbage-collected
when keys are no longer strongly reachable.
IdentityHashMap:
Compares keys by reference (==) rather than equals().
Useful for certain identity-based caching cases.
EnumMap:
Specialized map for enum keys — offers high performance and compact storage.
ConcurrentSkipListMap:
A thread-safe, sorted map (like a concurrent version of TreeMap).

Choosing the right map#

Select the right map variant based on your needs —
ordered iteration, memory lifecycle, identity-based keys, or concurrency.

Conclusion and next steps#

The Java HashMap offers a powerful and efficient way to store, retrieve, and manipulate data using key-value pairs. With its flexibility and high performance, it has become an indispensable tool for Java developers. By understanding the benefits and potential limitations of the HashMap, its strengths can be leveraged to create robust and efficient applications.

We hope this experience was fun and helped you learn about HashMap in Java. If you are looking to explore other data types and how they are used in different programming languages, we recommend checking out the following Educative courses:

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Operation	Time Complexity
`put(key, value)`	O(1) average–O(n) worst case
`get(key)`	O(1) average–O(n) worst case
`containsKey(key)`	O(1) average–O(n) worst case
`containsValue(value)`	O(n)
`remove(key)`	O(1) average–O(n) worst case
`size()`	O(1)
`isEmpty()`	O(1)
`keySet()`	O(n)
`values()`	O(n)
`entrySet()`	O(n)
`clear()`	O(1)

What is a HashMap in Java?

Introduction#

Java HashMap: Behind the scenes#

Capacity, load factor tuning and rehashing strategy#

Key tuning strategies#

Java 8+ enhancements: Treeification for performance#

Key points#

Designing hashCode() and equals(): the key to performance#

Best practices#

Implementing different HashMap functions#

Iterating over a Java HashMap#

Data structures equivalent to Java HashMap in different programming languages#

Advantages of HashMap#

Disadvantages of HashMap#

Thread safety and concurrent maps#

Thread-safe alternatives#

Example usage#

Map variants and when to use them#

Common map variants#

Choosing the right map#

Conclusion and next steps#