1. Overview
Hashing is a fundamental concept of computer science.
In Java, efficient hashing algorithms stand behind some of the most popular collections we have available – such as the HashMap (for an in-depth look at HashMap, feel free to check this article) and the HashSet.
In this article, we’ll focus on how hashCode() works, how it plays into collections and how to implement it correctly.
2. Usage of hashCode() in Data Structures
The simplest operations on collections can be inefficient in certain situations.
For example, this triggers a linear search which is highly ineffective for lists of huge sizes:
List<String> words = Arrays.asList("Welcome", "to", "Baeldung");
if (words.contains("Baeldung")) {
System.out.println("Baeldung is in the list");
}
Java provides a number of data structures for dealing with this issue specifically – for example, several Map interface implementations are hash tables.
When using a hash table, these collections calculate the hash value for a given key using the hashCode() method and use this value internally to store the data – so that access operations are much more efficient.
3. Understanding How hashCode() Works
Simply put, hashCode() returns an integer value, generated by a hashing algorithm.
Objects that are equal (according to their equals()) must return the same hash code. It’s not required for different objects to return different hash codes.
The general contract of hashCode() states:
- Whenever it is invoked on the same object more than once during an execution of a Java application, hashCode() must consistently return the same value, provided no information used in equals comparisons on the object is modified. This value needs not remain consistent from one execution of an application to another execution of the same application
- If two objects are equal according to the equals(Object) method, then calling the hashCode() method on each of the two objects must produce the same value
- It is not required that if two objects are unequal according to the equals(java.lang.Object) method, then calling the hashCode method on each of the two objects must produce distinct integer results. However, developers should be aware that producing distinct integer results for unequal objects improves the performance of hash tables
“As much as is reasonably practical, the hashCode() method defined by class Object does return distinct integers for distinct objects. (This is typically implemented by converting the internal address of the object into an integer, but this implementation technique is not required by the JavaTM programming language.)”
4. A Naive hashCode() Implementation
It’s actually quite straightforward to have a naive hashCode() implementation that fully adheres to the above contract.
To demonstrate this, we’re going to define a sample User class that overrides the method’s default implementation:
public class User {
private long id;
private String name;
private String email;
// standard getters/setters/constructors
@Override
public int hashCode() {
return 1;
}
@Override
public boolean equals(Object o) {
if (this == o) return true;
if (o == null) return false;
if (this.getClass() != o.getClass()) return false;
User user = (User) o;
return id != user.id
&& (!name.equals(user.name)
&& !email.equals(user.email));
}
// getters and setters here
}
The User class provides custom implementations for both equals() and hashCode() that fully adhere to the respective contracts. Even more, there’s nothing illegitimate with having hashCode() returning any fixed value.
However, this implementation degrades the functionality of hash tables to basically zero, as every object would be stored in the same, single bucket.
In this context, a hash table lookup is performed linearly and does not give us any real advantage – more on this in section 7.
5. Improving the hashCode() Implementation
Let’s improve a little bit the current hashCode() implementation by including all fields of the User class so that it can produce different results for unequal objects:
@Override
public int hashCode() {
return (int) id * name.hashCode() * email.hashCode();
}
This basic hashing algorithm is definitively much better than the previous one, as it computes the object’s hash code by just multiplying the hash codes of the name and email fields and the id.
In general terms, we can say that this is a reasonable hashCode() implementation, as long as we keep the equals() implementation consistent with it.
6. Standard hashCode() Implementations
The better the hashing algorithm that we use to compute hash codes, the better will the performance of hash tables be.
Let’s have a look at a “standard” implementation that uses two primes numbers to add even more uniqueness to computed hash codes:
@Override
public int hashCode() {
int hash = 7;
hash = 31 * hash + (int) id;
hash = 31 * hash + (name == null ? 0 : name.hashCode());
hash = 31 * hash + (email == null ? 0 : email.hashCode());
return hash;
}
While it’s essential to understand the roles that hashCode() and equals() methods play, we don’t have to implement them from scratch every time, as most IDEs can generate custom hashCode() and equals() implementations and since Java 7, we got an Objects.hash() utility method for comfortable hashing:
Objects.hash(name, email)
IntelliJ IDEA generates the following implementation:
@Override
public int hashCode() {
int result = (int) (id ^ (id >>> 32));
result = 31 * result + name.hashCode();
result = 31 * result + email.hashCode();
return result;
}
And Eclipse produces this one:
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + ((email == null) ? 0 : email.hashCode());
result = prime * result + (int) (id ^ (id >>> 32));
result = prime * result + ((name == null) ? 0 : name.hashCode());
return result;
}
In addition to the above IDE-based hashCode() implementations, it’s also possible to automatically generate an efficient implementation, for example using Lombok. In this case, the lombok-maven dependency must be added to pom.xml:
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok-maven</artifactId>
<version>1.16.18.0</version>
<type>pom</type>
</dependency>
It’s now enough to annotate the User class with @EqualsAndHashCode:
@EqualsAndHashCode
public class User {
// fields and methods here
}
Similarly, if we want Apache Commons Lang’s HashCodeBuilder class to generate a hashCode() implementation for us, the commons-lang Maven dependency must be included in the pom file:
<dependency>
<groupId>commons-lang</groupId>
<artifactId>commons-lang</artifactId>
<version>2.6</version>
</dependency>
And hashCode() can be implemented like this:
public class User {
public int hashCode() {
return new HashCodeBuilder(17, 37).
append(id).
append(name).
append(email).
toHashCode();
}
}
In general, there’s no universal recipe to stick to when it comes to implementing hashCode(). We highly recommend reading Joshua Bloch’s Effective Java, which provides a list of thorough guidelines for implementing efficient hashing algorithms.
What can be noticed here is that all those implementations utilize number 31 in some form – this is because 31 has a nice property – its multiplication can be replaced by a bitwise shift which is faster than the standard multiplication:
31 * i == (i << 5) - i
7. Handling Hash Collisions
The intrinsic behavior of hash tables raises up a relevant aspect of these data structures: even with an efficient hashing algorithm, two or more objects might have the same hash code, even if they’re unequal. So, their hash codes would point to the same bucket, even though they would have different hash table keys.
This situation is commonly known as a hash collision, and various methodologies exist for handling it, with each one having their pros and cons. Java’s HashMap uses the separate chaining method for handling collisions:
“When two or more objects point to the same bucket, they’re simply stored in a linked list. In such a case, the hash table is an array of linked lists, and each object with the same hash is appended to the linked list at the bucket index in the array.
In the worst case, several buckets would have a linked list bound to it, and the retrieval of an object in the list would be performed linearly.”
Hash collision methodologies show in a nutshell why it’s so important to implement hashCode() efficiently.
Java 8 brought an interesting enhancement to HashMap implementation – if a bucket size goes beyond the certain threshold, the linked list gets replaced with a tree map. This allows achieving O(logn) look up instead of pessimistic O(n).
8. Creating a Trivial Application
To test the functionality of a standard hashCode() implementation, let’s create a simple Java application that adds some User objects to a HashMap and uses SLF4J for logging a message to the console each time the method is called.
Here’s the sample application’s entry point:
public class Application {
public static void main(String[] args) {
Map<User, User> users = new HashMap<>();
User user1 = new User(1L, "John", "john@domain.com");
User user2 = new User(2L, "Jennifer", "jennifer@domain.com");
User user3 = new User(3L, "Mary", "mary@domain.com");
users.put(user1, user1);
users.put(user2, user2);
users.put(user3, user3);
if (users.containsKey(user1)) {
System.out.print("User found in the collection");
}
}
}
And this is the hashCode() implementation:
public class User {
// ...
public int hashCode() {
int hash = 7;
hash = 31 * hash + (int) id;
hash = 31 * hash + (name == null ? 0 : name.hashCode());
hash = 31 * hash + (email == null ? 0 : email.hashCode());
logger.info("hashCode() called - Computed hash: " + hash);
return hash;
}
}
The only detail worth stressing here is that each time an object is stored in the hash map and checked with the containsKey() method, hashCode() is invoked and the computed hash code is printed out to the console:
[main] INFO com.baeldung.entities.User - hashCode() called - Computed hash: 1255477819 [main] INFO com.baeldung.entities.User - hashCode() called - Computed hash: -282948472 [main] INFO com.baeldung.entities.User - hashCode() called - Computed hash: -1540702691 [main] INFO com.baeldung.entities.User - hashCode() called - Computed hash: 1255477819 User found in the collection
9. Conclusion
It’s clear that producing efficient hashCode() implementations often requires a mixture of a few mathematical concepts, (i.e. prime and arbitrary numbers), logical and basic mathematical operations.
Regardless, it’s entirely possible to implement hashCode() effectively without resorting to these techniques at all, as long as we make sure the hashing algorithm produce different hash codes for unequal objects and is consistent with the implementation of equals().
As always, all the code examples shown in this article are available over on GitHub.