The Map interface in Java is a part of the java.util package and provides a way to store key-value pairs. It does not inherit from Collection, and instead defines a specific set of methods to interact with the key-value mappings.

Internal Methods of the Map Interface:

Here is a list of all the methods from the Map interface with their corresponding use cases:

1. void clear()
   Use Case: To reset the map by removing all key-value mappings.
2. boolean containsKey(Object key)
   Use Case: To check if a specific key is present in the map.
3. boolean containsValue(Object value)
   Use Case: To check if any key in the map is associated with a given value.
4. Set<Map.Entry<K, V>> entrySet()
   Use Case: To obtain a set view of the map's key-value pairs, useful for iteration.
5. V get(Object key)
   Use Case: To retrieve the value associated with a specified key.
6. boolean isEmpty()
   Use Case: To determine if the map contains no key-value mappings (i.e., it is empty).
7. Set<K> keySet()
   Use Case: To get a set view of the keys in the map, which can be useful for iterating over the keys.
8. V put(K key, V value)
   Use Case: To add or update a key-value pair in the map.
9. void putAll(Map<? extends K, ? extends V> m)
   Use Case: To copy all mappings from another map to the current map.
10. V remove(Object key)
    Use Case: To remove a key-value pair from the map by specifying the key.
11. int size()
    Use Case: To get the number of key-value pairs in the map.
12. Collection<V> values()
    Use Case: To get a collection view of all the values in the map.

Why Map is Needed:

A Map in Java is an interface that represents a collection of key-value pairs. It is needed in scenarios where you need to:

1. Efficient Lookup by Key: You can retrieve values quickly using their corresponding keys. This makes Map suitable for situations where fast lookups, inserts, and deletions are required, such as searching through databases, caches, and directories.
2. Uniqueness of Keys: Maps allow only one value per unique key, ensuring no duplicates. This is useful in cases like employee directories, product catalogs, or configuration settings where each entry must be uniquely identified by a key.
3. Flexible Data Structures: Different Map implementations (like HashMap, TreeMap, LinkedHashMap, etc.) provide various features like ordering of keys, sorted order, or maintaining insertion order, making Map a versatile choice for different use cases.
4. Associative Arrays: A Map can be viewed as an associative array or a dictionary that lets you associate values with keys for easier access.

8 Ways to Retrieve Elements from Map Object

package com.niteshsynergy.map;

import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import java.util.Collection;
import java.util.Map.Entry;

public class MapExample {
   public static void main(String[] args) {
       // Create a new HashMap
       Map<String, Integer> map = new HashMap<>();
       
       // Add entries to the map
       map.put("Apple", 10);
       map.put("Banana", 20);
       map.put("Orange", 30);

       // 1. Using get() to retrieve a value by key
       Integer appleValue = map.get("Apple");
       System.out.println("Apple: " + appleValue);  // Output: Apple: 10

       // 2. Using keySet() to iterate over keys and retrieve values
       System.out.println("\nUsing keySet():");
       Set<String> keys = map.keySet();
       for (String key : keys) {
           System.out.println(key + ": " + map.get(key));
       }
       // Output:
       // Apple: 10
       // Banana: 20
       // Orange: 30

       // 3. Using values() to iterate over values
       System.out.println("\nUsing values():");
       Collection<Integer> values = map.values();
       for (Integer value : values) {
           System.out.println(value);
       }
       // Output:
       // 10
       // 20
       // 30

       // 4. Using entrySet() to iterate over key-value pairs
       System.out.println("\nUsing entrySet():");
       Set<Entry<String, Integer>> entries = map.entrySet();
       for (Entry<String, Integer> entry : entries) {
           System.out.println(entry.getKey() + ": " + entry.getValue());
       }
       // Output:
       // Apple: 10
       // Banana: 20
       // Orange: 30

       // 5. Using forEach() with lambda
       System.out.println("\nUsing forEach():");
       map.forEach((key, value) -> System.out.println(key + ": " + value));
       // Output:
       // Apple: 10
       // Banana: 20
       // Orange: 30

       // 6. Using computeIfAbsent() to add a value if the key doesn't exist
       Integer valueForMango = map.computeIfAbsent("Mango", key -> 40);
       System.out.println("\nUsing computeIfAbsent():");
       System.out.println("Mango: " + valueForMango);  // Output: Mango: 40

       // 7. Using getOrDefault() to retrieve a value or a default value
       Integer valueForGrapes = map.getOrDefault("Grapes", 50);
       System.out.println("\nUsing getOrDefault():");
       System.out.println("Grapes: " + valueForGrapes);  // Output: Grapes: 50

       // 8. Using remove() to delete an entry
       Integer removedValue = map.remove("Apple");
       System.out.println("\nUsing remove():");
       System.out.println("Removed Apple: " + removedValue);  // Output: Removed Apple: 10
   }
}
 

HashTable: Concepts, Rules, Use Cases, and Guidelines

The Hashtable class in Java is part of the java.util package. It implements a collection that uses a hash table to store key-value pairs. Below are its concepts, rules, use cases, and guidelines:

Concepts of Hashtable

1. Key-Value Pair Storage:
   • Keys and values are stored as pairs. Each key is unique, and its associated value can be retrieved using the key.
2. Thread-Safe:
   • All methods in Hashtable are synchronized, making it thread-safe. Multiple threads can access it without external synchronization.
3. Hashing:
   • Internally, Hashtable uses hashing to map keys to indices in a bucket array. Collisions are handled by linked lists or other chaining mechanisms.
4. Null Handling:
   • Hashtable does not allow null keys or null values.
5. Fail-Fast Iterators:
   • Iterators on Hashtable are fail-fast, meaning any modification to the structure during iteration throws a ConcurrentModificationException.

Rules for Using Hashtable

1. Thread-Safe by Default:
   • No need to synchronize externally for thread-safe operations.
2. No Null Keys/Values:
   • Attempting to insert a null key or value results in a NullPointerException.
3. Synchronized Operations:
   • Operations like put, get, and remove are synchronized. However, this may lead to performance bottlenecks in high-concurrency scenarios.
4. Performance Trade-off:
   • Due to synchronization, Hashtable is slower than HashMap for single-threaded applications.
5. Key Uniqueness:
   • Duplicate keys are not allowed; adding a value with an existing key will overwrite the previous value.

Use Cases for Hashtable

1. Multi-Threaded Applications:
   • When a thread-safe map is required without external synchronization.
   • Example: Session management in a web application where multiple threads access a shared data store.
2. Legacy Systems:
   • When working with older Java applications that were designed with Hashtable before ConcurrentHashMap was introduced.
3. Simple Cache:
   • Implementing a basic thread-safe cache for read-write operations.
4. Resource Locking:
   • Maintaining a mapping of resources to locks for synchronized access in a multithreaded application.

Guidelines for Using Hashtable

1. Prefer ConcurrentHashMap for High-Concurrency Needs:
   • Use ConcurrentHashMap for better scalability and performance in multithreaded applications.
2. Avoid in Single-Threaded Applications:
   • Use HashMap or LinkedHashMap instead, as they provide better performance without synchronization overhead.
3. Monitor Performance:
   • For high-throughput applications, analyze the performance impact due to synchronization.
4. Use Iterators Carefully:
   • Avoid modifying the Hashtable during iteration to prevent ConcurrentModificationException.
5. Serialization:
   • Hashtable is serializable but may not be suitable for large datasets due to potential serialization overhead.
6. Capacity and Load Factor:
   • Optimize initial capacity and load factor to reduce rehashing overhead for large datasets.

Default Initial Capacity

• The default initial capacity of a Hashtable is 11 buckets.

Load Factor

• The load factor is a measure of how full the Hashtable is allowed to get before it resizes (rehashes) its buckets.
• The default load factor for a Hashtable is 0.75. This means that the Hashtable will resize when it becomes 75% full.

Constructor Variants

1. Default Constructor:
   • Creates a Hashtable with an initial capacity of 11 and a load factor of 0.75.
2. Constructor with Initial Capacity:
   • Allows you to specify the initial capacity. For example:
     • Creates a Hashtable with an initial capacity of 20 buckets.
3. Constructor with Initial Capacity and Load Factor:
   • Allows you to specify both the initial capacity and the load factor. For example:
     • Creates a Hashtable with an initial capacity of 20 and a load factor of 0.5.

Hashtable<K, V> table = new Hashtable<>();

Hashtable<K, V> table = new Hashtable<>(int initialCapacity);

Hashtable<String, Integer> table = new Hashtable<>(20);

Hashtable<K, V> table = new Hashtable<>(int initialCapacity, float loadFactor);

Hashtable<String, Integer> table = new Hashtable<>(20, 0.5f);

How Initial Capacity Works

• The initial capacity determines how many key-value pairs can be stored before resizing occurs, based on the load factor.
• For example:
  • If the initial capacity is 20 and the load factor is 0.75, the Hashtable will resize when it reaches 15 entries (20 × 0.75 = 15).

Why Choose a Custom Initial Capacity?

• Performance Optimization:
  • To minimize the cost of resizing (rehashing), set an initial capacity large enough to accommodate the expected number of entries.
• Reduce Memory Waste:
  • Avoid allocating excessive memory for small datasets.

import java.util.Hashtable;

public class HashtableExample {
   public static void main(String[] args) {
       // Create a Hashtable
       Hashtable<String, Integer> table = new Hashtable<>();

       // Add key-value pairs
       table.put("Apple", 10);
       table.put("Banana", 20);
       table.put("Mango", 30);

       // Retrieve value
       System.out.println("Value for 'Apple': " + table.get("Apple")); // Output: 10

       // Remove a key-value pair
       table.remove("Banana");
       System.out.println("After removing 'Banana': " + table); // Output: {Apple=10, Mango=30}

       // Check for key and value existence
       System.out.println("Contains key 'Mango': " + table.containsKey("Mango")); // Output: true
       System.out.println("Contains value 20: " + table.containsValue(20)); // Output: false

       // Iterate through Hashtable
       table.forEach((key, value) -> System.out.println(key + " -> " + value));
       // Output:
       // Apple -> 10
       // Mango -> 30
   }
}
 

Advantages

• Thread-safe and reliable for concurrent access.
• Fail-fast iterators ensure data consistency during modifications.
• Suitable for small datasets requiring synchronized access.

Disadvantages

• Slower than HashMap due to synchronization.
• Does not allow null keys or values.
• Legacy class, superseded by ConcurrentHashMap.

A HashMap in Java is a part of the java.util package and is one of the most commonly used collections for storing key-value pairs.

 It is backed by an array of buckets (also known as an array of entries), and uses hashing to locate where each entry should go.

 Here’s a step-by-step breakdown of how HashMap works internally:

package com.niteshsynergy.map;

import java.util.HashMap;

public class Demo03HashMap {

   public static void main(String[] args) {
       // Create a HashMap with initial capacity of 4 and load factor of 0.75
       HashMap<String, Integer> map = new HashMap<>(4, 0.75f);

       // Adding key-value pairs to the HashMap
       map.put("Apple", 10);
       map.put("Banana", 20);
       map.put("Cherry", 30);
       map.put("Date", 40);
       map.put("Elderberry", 50);  // This triggers resizing

       // Accessing values using get()
       System.out.println("Apple: " + map.get("Apple"));
       System.out.println("Banana: " + map.get("Banana"));
       System.out.println("Cherry: " + map.get("Cherry"));
       System.out.println("Date: " + map.get("Date"));
       System.out.println("Elderberry: " + map.get("Elderberry"));

       // Removing an entry
       map.remove("Banana");
       System.out.println("After removing Banana: " + map);
   }
}
 

Step 1: Initialization

• Initial Capacity: The HashMap is initialized with an initial capacity of 4. It means that it will start with 4 buckets.
• Load Factor: The load factor is 0.75, meaning when 75% of the map's capacity is filled, it will resize. So, after inserting 3 elements, the map will trigger a resize.

Step 2: Inserting Elements

1. Inserting "Apple" -> 10:
   • Hashing: The hashCode() for "Apple" is computed.
   • Bucket Index Calculation: hashCode("Apple") % 4 determines the bucket where this key-value pair will go.
   • Insertion: Since bucket 1 (index 1) is empty, "Apple" -> 10 is inserted there.
2. Inserting "Banana" -> 20:
   • Hashing: The hashCode() for "Banana" is computed.
   • Bucket Index Calculation: hashCode("Banana") % 4 determines the bucket index.
   • Insertion: The key-value pair "Banana" -> 20 is inserted into bucket 3.
3. Inserting "Cherry" -> 30:
   • Hashing: The hashCode() for "Cherry" is computed.
   • Bucket Index Calculation: hashCode("Cherry") % 4 determines the bucket index.
   • Insertion: The key-value pair "Cherry" -> 30 is inserted into an available bucket.
4. Inserting "Date" -> 40:
   • Hashing: The hashCode() for "Date" is computed.
   • Bucket Index Calculation: hashCode("Date") % 4 determines the bucket index.
   • Insertion: "Date" -> 40 is inserted into a bucket.
5. Inserting "Elderberry" -> 50:
   • This operation triggers resizing because the number of elements in the HashMap exceeds the load factor threshold (0.75 * 4 = 3).
   • Resize Triggered: The capacity of the HashMap is doubled from 4 to 8. This means the bucket array size increases to accommodate more entries.
   • Rehashing: After resizing, all the existing entries are rehashed and redistributed into the new bucket array.
     • The keys are placed in different buckets based on their hash values.

Step 3: Retrieving Values (get() Method)

• map.get("Apple"): The hash value for "Apple" is calculated, and the bucket index is derived. The key "Apple" is found in the corresponding bucket, and its value 10 is returned.
• map.get("Banana"): The hash value for "Banana" is calculated, and it’s found in the corresponding bucket. The value 20 is returned.
• map.get("Cherry"): Similarly, "Cherry" is retrieved, returning the value 30.
• map.get("Date"): The key "Date" is hashed, and the value 40 is returned.
• map.get("Elderberry"): The key "Elderberry" is hashed, and its value 50 is returned.

Step 4: Removing an Entry (remove() Method)

• map.remove("Banana"): The key "Banana" is hashed, and its bucket is located. The key-value pair is removed from the map, and the updated map is printed.

Step 5: Understanding Internal Collision Handling (if applicable)

In this example, let’s assume that "Apple", "Banana", "Cherry", "Date", and "Elderberry" all have different hash values (or hash values that map to different buckets), so no collisions occurred.

However, if there were collisions (i.e., two or more keys hashed to the same bucket), HashMap would handle them as follows:

• Before Java 8: Collisions were resolved using a linked list. Multiple entries with the same bucket index would be stored in a linked list in that bucket.
• Java 8 and later: When the bucket becomes too large (more than 8 entries), the linked list is replaced by a Red-Black Tree to improve lookup performance from O(n) to O(log n).

Step 6: Resizing (Rehashing)

As soon as the number of entries in the HashMap exceeds 3 (because 0.75 * 4 = 3), the HashMap will resize. This resizing involves:

• Doubling the number of buckets (from 4 to 8).
• Rehashing: All existing key-value pairs are rehashed and redistributed into the new bucket array based on their hash codes.
  
  
  Next→

Step 1: Understanding the Basics of HashMap

A HashMap in Java is part of the java.util package and is used to store key-value pairs. It uses a hash table to store the data. Here's how it works:

• The key is hashed, and a hash code is generated from it.
• The hash code is then used to determine the bucket index in the underlying array of the hash table (the array of size 16 by default).
• Collisions can occur if two keys hash to the same bucket. In this case, the HashMap uses a linked list or a tree (for larger buckets) to store multiple entries at the same index.

Step 2: Default Capacity and Load Factor

• Default Capacity: The default capacity of a HashMap is 16. This means it will create an internal array of 16 buckets.
• Load Factor: The default load factor is 0.75. This means when the map has filled up 75% of its capacity (i.e., 12 out of 16 entries), the HashMap will resize (double the capacity).

Step 3: How Hashing Works

When you add a key-value pair into the HashMap, the key is hashed, and the hash code is used to determine the index where the value will be stored in the internal array.

For example:

Emp emp1 = new Emp(101, "Nitesh");
Emp emp2 = new Emp(102, "Ajay");
 

Both of these Emp objects will go through the hashing process:

• The hashCode() method is called on the key (emp1.getId(), emp2.getId()), and a hash value is generated.
• The index for the array bucket is calculated as hashCode % capacity (capacity is 16 by default).

Step 4: Handling Collisions

Collisions occur when two keys have the same hash code (or the same bucket index). In this case, both values will be stored in a linked list or a balanced tree within the same bucket.

Let’s say we have two objects, emp1 and emp2, that both hash to the same bucket index. In this case, both emp1 and emp2 will be stored in the same bucket, and they will be added in a linked list or a tree structure.

Step 5: Example with Code

Here’s an example of HashMap with potential collisions, and how to add and retrieve values:

import java.util.HashMap;
import java.util.Map;

class Emp {
   int id;
   String name;

   Emp(int id, String name) {
       this.id = id;
       this.name = name;
   }

   // Override hashCode() to create a scenario for collision
   @Override
   public int hashCode() {
       return id % 16;  // This forces a collision for id 1 and 17 (same hash code)
   }

   // Override equals() to check equality of Emp objects by id
   @Override
   public boolean equals(Object obj) {
       if (this == obj) return true;
       if (obj == null || getClass() != obj.getClass()) return false;
       Emp emp = (Emp) obj;
       return id == emp.id;
   }

   @Override
   public String toString() {
       return "Emp{id=" + id + ", name='" + name + "'}";
   }
}

public class HashMapExample {
   public static void main(String[] args) {
       Map<Emp, String> empMap = new HashMap<>();

       // Adding entries to the HashMap
       Emp emp1 = new Emp(1, "Nitesh");
       Emp emp2 = new Emp(17, "Ajay"); // Same hashCode as emp1
       Emp emp3 = new Emp(2, "Ravi");

       empMap.put(emp1, "Employee 1");
       empMap.put(emp2, "Employee 17");
       empMap.put(emp3, "Employee 2");

       // Retrieving the values
       System.out.println(empMap.get(emp1)); // Should print "Employee 1"
       System.out.println(empMap.get(emp2)); // Should print "Employee 17"
       System.out.println(empMap.get(emp3)); // Should print "Employee 2"
   }
}
 

Step 6: In-Depth Explanation of the Code

1. The Emp Class:
   • We have overridden the hashCode() method to make sure that Emp objects with ids 1 and 17 will hash to the same value (collision).
   • We also override the equals() method to ensure that two Emp objects with the same id are considered equal, which is necessary for HashMap to retrieve the correct value during lookup.
2. The HashMap Behavior:
   • When emp1 and emp2 are added to the HashMap, both will hash to the same index (since their hashCode() values are the same). Therefore, a collision occurs, and the HashMap stores both emp1 and emp2 in the same bucket.
   • Internally, the HashMap uses a linked list or tree (depending on the number of elements in the bucket) to store multiple elements at the same bucket index.
3. Retrieving Values:
   • When we retrieve the values using empMap.get(emp1) or empMap.get(emp2), the HashMap checks the hash code, finds the correct bucket, and then uses the equals() method to match the correct entry.
   • Even though emp1 and emp2 are at the same index, the equals() method ensures that the right Emp object is returned.

Step 7: How to Avoid Collisions (or Minimize Them)

• Proper Hash Function: Use a good hashCode() function that distributes the hash codes more evenly across the buckets. This minimizes the likelihood of collisions.
• Resize the HashMap: As the HashMap grows (over 75% full), it will automatically resize and rehash the entries, reducing the chances of collisions.

Tree-based Buckets: If a collision occurs in a bucket with a large number of elements, Java will use a balanced tree (since Java 8) to improve retrieval time from O(n) to O(log n).
 

Full Code Example: Emp Class and HashMap Implementation: Read this

import java.util.HashMap;
import java.util.Map;
import java.util.Objects;

class Emp {
   int id;
   String name;

   // Constructor for Emp class
   Emp(int id, String name) {
       this.id = id;
       this.name = name;
   }

   // Override hashCode() to generate unique hash codes based on 'id'
   @Override
   public int hashCode() {
       // Using a prime number (31) for better distribution of hash codes
       return Objects.hash(id);
   }

   // Override equals() to compare Emp objects based on their 'id' values
   @Override
   public boolean equals(Object obj) {
       // If the object is the same, return true
       if (this == obj) return true;
       
       // If the object is null or not an instance of Emp, return false
       if (obj == null || getClass() != obj.getClass()) return false;
       
       // Cast the object to Emp and compare the 'id'
       Emp emp = (Emp) obj;
       return id == emp.id;
   }

   // Override toString() for better readability while printing Emp objects
   @Override
   public String toString() {
       return "Emp{id=" + id + ", name='" + name + "'}";
   }
}

public class HashMapExample {
   public static void main(String[] args) {
       // Create a HashMap to store Emp objects as keys with String values
       Map<Emp, String> empMap = new HashMap<>();

       // Create Emp objects
       Emp emp1 = new Emp(1, "Nitesh");
       Emp emp2 = new Emp(17, "Ajay"); // Same hashCode as emp1 due to the same id % 16
       Emp emp3 = new Emp(2, "Ravi");

       // Add Emp objects to the HashMap
       empMap.put(emp1, "Employee 1");
       empMap.put(emp2, "Employee 17");
       empMap.put(emp3, "Employee 2");

       // Retrieve and print values from the HashMap
       System.out.println(empMap.get(emp1)); // Should print "Employee 1"
       System.out.println(empMap.get(emp2)); // Should print "Employee 17"
       System.out.println(empMap.get(emp3)); // Should print "Employee 2"

       // Print the entire map to observe how the entries are stored
       System.out.println("HashMap contents: " + empMap);
   }
}
 

Detailed Explanation

1. The Emp Class:

• Fields: The Emp class has two fields: id (integer) and name (String).
• Constructor: The constructor initializes the Emp object with an id and name.

2. Overriding hashCode():

The hashCode() method is used by the HashMap to calculate the index in the internal hash table where the key-value pair will be stored. In our case, the hashCode() is overridden as follows:

@Override
public int hashCode() {
   return Objects.hash(id); // Generate a hash code using the 'id' field.
}
 

• Objects.hash(id): This is a good practice as it generates a hash code based on the id field of the Emp object. The hashCode() method helps to distribute the keys evenly across the buckets to minimize collisions. The formula id % 16 is implicitly handled by the default HashMap capacity (16), but we’ve simplified it using Objects.hash(id) for better clarity and distribution.

3. Overriding equals():

The equals() method is used to compare two Emp objects for equality, based on their id. The overridden equals() method ensures that two Emp objects with the same id are considered equal, even if they are different instances.

@Override
public boolean equals(Object obj) {
   if (this == obj) return true; // Same object reference
   if (obj == null || getClass() != obj.getClass()) return false; // Null or different class
   Emp emp = (Emp) obj; // Cast the object to Emp
   return id == emp.id; // Compare the 'id' fields for equality
}
 

• this == obj: First, it checks if both objects are the same using reference comparison. If true, it returns true.
• getClass() != obj.getClass(): This checks whether the other object is of the same class (Emp).
• id == emp.id: Finally, it compares the id of both Emp objects to check if they are the same.

4. Overriding toString():

This method is overridden to make it easier to print out the Emp objects.

@Override
public String toString() {
   return "Emp{id=" + id + ", name='" + name + "'}";
}
 

This ensures that when you print the Emp object, it will display its id and name fields in a readable format.

5. Using HashMap:

In the main method, we create a HashMap and insert Emp objects as keys. The map stores the Emp objects with a String value. When you call empMap.get(emp1), it looks up the key using the hashCode() and equals() methods to find the correct entry.

// Adding Emp objects to the HashMap
empMap.put(emp1, "Employee 1");
empMap.put(emp2, "Employee 17");
empMap.put(emp3, "Employee 2");

// Retrieving and printing values
System.out.println(empMap.get(emp1)); // Should print "Employee 1"
System.out.println(empMap.get(emp2)); // Should print "Employee 17"
System.out.println(empMap.get(emp3)); // Should print "Employee 2"
 

• Even though emp1 and emp2 have different ids (1 and 17), they hash to the same bucket index (because of the hashCode() method), but they are correctly distinguished by the equals() method.

6. Handling Collisions:

In our case, both emp1 and emp2 have id values that result in the same hash code (due to id % 16), which causes a collision. The HashMap handles this collision by storing the entries in a linked list or tree structure within the same bucket. When retrieving values, it uses equals() to differentiate between the entries and returns the correct one.

Output:

The output will be as follows:

Employee 1
Employee 17
Employee 2
HashMap contents: {Emp{id=1, name='Nitesh'}=Employee 1, Emp{id=17, name='Ajay'}=Employee 17, Emp{id=2, name='Ravi'}=Employee 2}
 

• The hashCode() method ensures that keys are distributed evenly across the HashMap.
• The equals() method ensures that two Emp objects with the same id are considered equal, even if they are different instances.
• In case of collisions, the HashMap will handle them using a linked list or tree, and the equals() method is used to resolve which entry to return.

Best Solutions for Handling Collisions in HashMap and How to Avoid Them

In a HashMap, collisions occur when two or more keys generate the same hash code and thus map to the same bucket. Although hash collisions are common, especially when dealing with large sets of data, they can significantly degrade performance if not handled properly.

1. What is a Collision?

A collision happens when two or more distinct keys produce the same hash value, meaning they would be placed in the same bucket. For example:

• hash("key1") == hash("key2")
• This results in both keys being stored in the same bucket, causing a collision.

2. Default Collision Handling in HashMap

HashMap resolves collisions by storing multiple entries in the same bucket using a linked list (before Java 8) or a Red-Black Tree (after Java 8 if the bucket has more than 8 entries). However, this can cause a performance hit if the collisions are frequent because:

• Before Java 8: Collisions were resolved using a linked list. If there were many collisions in the same bucket, it could degrade the time complexity of operations from O(1) to O(n).
• After Java 8: If a bucket exceeds 8 entries, a Red-Black Tree is used, which improves the lookup time from O(n) to O(log n).

Best Practices to Avoid Collisions

1. Use a Good Hash Function

   • A good hash function is crucial to minimizing collisions. The hash function should distribute the hash values uniformly across the available buckets. This ensures that keys are spread out across different buckets, reducing the chance of collisions.
   • If you're using custom objects as keys in a HashMap, ensure that the hashCode() method is well-designed to generate unique hash codes for distinct objects.
     • Override hashCode() properly by using multiple fields of the object and avoiding simplistic hash code implementations.

   Example:

   @Override
   public int hashCode() {
      return Objects.hash(field1, field2);
   }

2. Avoid Using Very Large Key Sets with Limited Initial Capacity

   When initializing a HashMap, set the initial capacity appropriately. If your HashMap will store a large number of keys, make sure the initial capacity is large enough to avoid triggering frequent resizing and collisions.

You can set the initial capacity and load factor when creating the HashMap.

HashMap<String, Integer> map = new HashMap<>(100, 0.75f); // Initial capacity of 100
 

Rehashing When Collisions Occur

• If you notice that your map is experiencing frequent collisions, consider increasing its capacity (or decreasing the load factor). This allows for a more spacious bucket array, which will reduce the likelihood of collisions.
• HashMap automatically resizes itself when the number of entries exceeds the load factor threshold, but you can control this by choosing a smaller load factor.
•  

HashMap<String, Integer> map = new HashMap<>(16, 0.5f); // Lower load factor (50%)
 

1. Choosing a Proper Bucket Size
   • If your hash table’s bucket size (capacity) is too small, there will be more collisions. On the other hand, a large capacity could lead to wasted space. Choose a capacity that balances performance and space.
   • Keep in mind that the HashMap automatically grows when needed, but setting an optimal initial capacity can help avoid unnecessary resizing.
2. Using Custom Hashing Strategies for Specific Use Cases
   • If you know certain properties of your keys, you can design custom hashing strategies. For example, if you are using strings as keys, and you know that they are relatively short or that they follow certain patterns, you can use a more specialized hash function to spread the keys across buckets evenly.
3. Minimize Key Clustering
   • Keys that are similar in nature or have similar hash values (e.g., sequential strings or numbers) may end up in the same bucket. This is called key clustering, and it can lead to more collisions.

   • Avoid creating keys that are too predictable or uniform.

      

How Does HashMap Resolve Collisions?

1. Before Java 8: Linked List in Buckets

   • When a collision occurs (i.e., multiple keys hash to the same bucket), the HashMap stores the key-value pairs in a linked list within that bucket.
   • This works well for a small number of collisions, but as the number of collisions increases, the performance degrades to O(n), where n is the number of elements in the bucket.

   Example:
   
   // Bucket 1: [Apple -> 10, Banana -> 20] (Collision)
   Java 8 and Later: Red-Black Tree in Buckets

2. In Java 8 and later, if a bucket exceeds 8 entries, the HashMap converts the linked list into a Red-Black Tree to maintain O(log n) performance for operations like get(), put(), and remove().
3. Why a Red-Black Tree?: A Red-Black Tree provides balanced tree structure with logarithmic time complexity for searching, insertion, and deletion.
4. Example of Tree in Bucket:

// Bucket 3: [Apple -> 10, Banana -> 20, Cherry -> 30, Date -> 40] (Using Red-Black Tree)
 

Real-world Example of Collisions

Let’s say you have a HashMap that stores employees, where the key is the employee’s ID (a string) and the value is the employee’s name.

• Good Case: Employee IDs like E001, E002, E003, E004 have different hash codes, so no collision occurs, and the HashMap is efficient.
• Collision Example: If employee IDs are generated using a simple mechanism like sequential numbers (E001, E002, E003), the hash codes may collide, especially if the number range is small. This would result in the keys being stored in the same bucket, affecting performance.

FAQ on HashMap Collision Resolution

1. What is the time complexity of HashMap operations with collisions?
   • If there are no collisions, the time complexity for get(), put(), and remove() operations is O(1).
   • If there are many collisions and they are stored in a linked list, the time complexity can degrade to O(n), where n is the number of elements in the bucket.
   • If collisions trigger a Red-Black Tree (in Java 8+), the time complexity improves to O(log n).
2. Can you manually resolve collisions in a HashMap?
   • In a typical use case, HashMap automatically handles collisions through linked lists and Red-Black Trees, so manual intervention is usually not required.
   • However, you can optimize your hashing function or choose a better initial capacity to reduce collisions.
3. What happens if the hashCode() method returns the same value for multiple keys?
   • If the hashCode() method generates the same value for multiple keys, the HashMap will store those keys in the same bucket. The performance will degrade, and you’ll need to rely on proper collision handling (linked lists or Red-Black Trees) to maintain efficiency.
4. Can you reduce collisions by choosing better keys?
   • Yes, choosing keys that have diverse characteristics (e.g., using strings with varied lengths or numbers with diverse distributions) can help reduce collisions. Keys with uniform characteristics are more likely to produce the same hash code.

• Collisions in HashMap can degrade performance, but they are handled efficiently by using linked lists and Red-Black Trees.
• To avoid collisions, ensure you have a good hash function and avoid using keys with similar or predictable hash values.
• Properly set the initial capacity and load factor to minimize the need for resizing.
• Consider rehashing when the number of entries increases and when the bucket size becomes inadequate.

Interview FAQ Based on HashMap

1. What happens when the number of elements exceeds the load factor threshold?
   • When the number of elements exceeds the load factor threshold (capacity * load factor), the HashMap resizes. It doubles the number of buckets and rehashes the existing entries to distribute them evenly across the new array.
2. How does HashMap handle hash collisions?
   • Hash collisions are handled by storing multiple entries in the same bucket. Before Java 8, a linked list was used; after Java 8, if the bucket exceeds a threshold (more than 8 entries), a Red-Black Tree is used for better performance.
3. What is the difference between HashMap and TreeMap?
   • A HashMap uses hashing for storing key-value pairs and offers constant-time O(1) access on average, but with possible collisions. A TreeMap, on the other hand, uses a red-black tree structure and ensures O(log n) time complexity for all operations, but it requires the keys to be ordered.
4. What are the performance considerations when dealing with collisions in HashMap?
   • If the HashMap has many collisions (i.e., many keys hash to the same bucket), performance can degrade from O(1) to O(n) for lookup, insertion, and deletion operations. Using a good hash function can help distribute keys evenly across buckets, reducing the chances of collisions.
5. What is the default initial capacity and load factor of HashMap?

   • The default initial capacity is 16 and the default load factor is 0.75. This means the map will resize when it is 75% full.
     
     
     Thanks for reading HashMap Internal from Nitesh Synergy………………

      

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LinkedHashMap Overview

LinkedHashMap is a hash table and linked list combination that maintains the insertion order (or optionally access order) of the keys. It implements the Map interface, like HashMap, but adds extra functionality to maintain the ordering of elements.

• Insertion Order: By default, it maintains the order in which entries are inserted into the map.
• Access Order: You can also configure it to maintain the order in which the entries were last accessed, which can be useful for certain caching strategies.

Thread-Safety of LinkedHashMap

LinkedHashMap is not thread-safe by default. Like HashMap, if multiple threads access a LinkedHashMap concurrently, and at least one of them modifies the map, it should be externally synchronized (e.g., using Collections.synchronizedMap() or ConcurrentHashMap if thread safety is required).

Thread-Safety Workaround

If thread safety is a concern, you can:

• Use Collections.synchronizedMap(new LinkedHashMap<K, V>()) to make it synchronized.
• Or, you can use ConcurrentHashMap, though it may behave slightly differently due to its concurrent access features.

Use Case for LinkedHashMap

• Maintaining Insertion Order: If you want to keep the order in which elements were added to the map, LinkedHashMap is ideal.

  Example: You are storing user session data and want to process the data in the order it was inserted.

• LRU Cache Implementation: By setting it to access order, LinkedHashMap can be used for implementing a Least Recently Used (LRU) cache. The least recently accessed elements are moved to the front or back of the map, making it easy to remove them when the cache exceeds a certain size.

  Example: Caching the 10 most recent items accessed from a database, where the least recently used data can be discarded when space is needed.

package com.niteshsynergy.map;

import java.util.LinkedHashMap;
import java.util.Map;

public class Demo02LinkedHashMap {
   public static void main(String[] args) {
       // Create a LinkedHashMap
       LinkedHashMap<String, Integer> map = new LinkedHashMap<>();

       // Adding elements to the LinkedHashMap
       map.put("A", 1);
       map.put("B", 2);
       map.put("C", 3);
       map.put("D", 4);

       // 1. Accessing using keySet()
       System.out.println("Using keySet():");
       for (String key : map.keySet()) {
           System.out.println(key + ": " + map.get(key));
       }

       // 2. Accessing using values()
       System.out.println("\nUsing values():");
       for (Integer value : map.values()) {
           System.out.println(value);
       }

       // 3. Accessing using entrySet()
       System.out.println("\nUsing entrySet():");
       for (Map.Entry<String, Integer> entry : map.entrySet()) {
           System.out.println(entry.getKey() + ": " + entry.getValue());
       }

       // 4. Accessing using forEach (Java 8+)
       System.out.println("\nUsing forEach (Java 8+):");
       map.forEach((key, value) -> System.out.println(key + ": " + value));

       // Checking insertion order
       System.out.println("\nInsertion Order Maintained:");
       for (Map.Entry<String, Integer> entry : map.entrySet()) {
           System.out.println(entry.getKey() + ": " + entry.getValue());
       }
   }
}
 

4 Ways to Access LHM

package com.niteshsynergy.map;

import java.util.LinkedHashMap;
import java.util.Map;

public class LinkedHashMapAccessExample {
   public static void main(String[] args) {
       // Create and populate a LinkedHashMap
       LinkedHashMap<String, Integer> map = new LinkedHashMap<>();
       map.put("A", 1);
       map.put("B", 2);
       map.put("C", 3);

       // 1. Using keySet()
       System.out.println("Using keySet():");
       for (String key : map.keySet()) {
           System.out.println(key + ": " + map.get(key));
       }

       // 2. Using values()
       System.out.println("\nUsing values():");
       for (Integer value : map.values()) {
           System.out.println(value);
       }

       // 3. Using entrySet()
       System.out.println("\nUsing entrySet():");
       for (Map.Entry<String, Integer> entry : map.entrySet()) {
           System.out.println(entry.getKey() + ": " + entry.getValue());
       }

       // 4. Using forEach() (Java 8+)
       System.out.println("\nUsing forEach():");
       map.forEach((key, value) -> System.out.println(key + ": " + value));
   }
}