Internal Working of HashMap: Key Concepts and Mechanisms

A HashMap is a type of data structure that uses hashing to hold key-value pairs. It allows for fast retrieval, insertion, and deletion operations. HashMaps are widely used due to their efficiency in handling large amounts of data.

• Keys must be unique and typically immutable (e.g., Strings, Integers).
• Values can be any object.
• Operations (put, get, remove) typically run in O(1) time complexity in ideal cases.
• It uses hashing to determine the storage index.

Entry Class (Node Structure of HashMap)

A hashmap internal implementation consists of an array of Entry (or Node) objects. Each entry represents a key-value pair along with additional metadata:

Structure of an Entry

Each node contains:

1. Key: Immutable and unique identifier for a value.
2. Value: Data associated with the key.
3. Hash: Computed hash code of the key, used for indexing.
4. Next: A reference to the next entry in case of collisions (linked list-based chaining).

Example Implementation in Java

static class Entry<K, V> implements Map.Entry<K, V> {
    final K key;         // Unique key
    V value;             // Associated value
    final int hash;      // Precomputed hash for faster lookup
    Entry<K, V> next;    // Pointer to next node in case of collision

    public Entry(K key, V value, int hash, Entry<K, V> next) {
        this.key = key;
        this.value = value;
        this.hash = hash;
        this.next = next;
    }

    @Override
    public K getKey() {
        return key;
    }

    @Override
    public V getValue() {
        return value;
    }

    @Override
    public V setValue(V newValue) {
        V oldValue = this.value;
        this.value = newValue;
        return oldValue;
    }
}

How it Works:

• The hash value in hashmap internal implementation determines the bucket index in the internal array.
• If a collision occurs (two keys map to the same index), the next pointer links the new entry to the existing one, forming a linked list.
• When retrieving a value, the HashMap first calculates the hash, checks the corresponding bucket, and iterates through the linked list if needed.

Table Array in HashMap

In the internal working of HashMap, the HashMap stores entries in an array called table[]. This array serves as a collection of buckets, where each bucket is either:

• Empty (null), or
• A linked list (or a tree in case of Java 8+, when the list grows large enough).

Each index in this array corresponds to a computed hash value for a key. The index determines where an entry is placed.

Structure of the Table Array

transient Entry<K, V>[] table;

The table[] array size in the internal working of hashmap is typically a power of two (e.g., 16, 32, 64…) for optimized performance. This hashmap internal implementation helps in using bitwise operations instead of costly modulus operations when computing indices.

Example Internal Representation

table[0]  -> null  
table[1]  -> Entry(key1, value1, hash1) -> Entry(key5, value5, hash5)  
table[2]  -> Entry(key2, value2, hash2)  
table[3]  -> null  
table[4]  -> Entry(key3, value3, hash3)  
table[5]  -> Entry(key4, value4, hash4)  
table[6]  -> null  
...

In this case, an entry at index 1 is denoted as Entry(key1, value1, hash1). A linked list is created when many entries hash to the same index (Entry -> Entry -> Entry).

To understand how a HashMap does quick key-value operations, one must have a solid understanding of its underlying structure. The structure is made up of a number of fundamental elements that cooperate to effectively handle data.

Core Components of a HashMap

1. Bucket Array (table[])

• Definition:
  At its core, a HashMap maintains an internal array often referred to as the "bucket array" or table[]. Each element (bucket) in this array can store zero or more entries.
• Initial Capacity:
  Although it may be modified upon creation, the default starting capacity is usually 16.
• Load Factor:
  When the HashMap should boost its capacity to sustain performance is determined by the load factor, which has a default value of 0.75.

2. Nodes or Entry Class

• Entry Class:
  Each key-value pair is encapsulated in a node, commonly known as an "Entry" (or "Node" in later Java versions).
  Each entry contains:
  
  • The key
  • The value
  • The hash code of the key
  • A reference to the next entry (for collision handling)
• Linked List:
  Within a bucket, items are linked together in a single linked list when several keys map to the same bucket index (collision).

3. Red-Black Tree (Java 8+)

• Treeification:
  To enhance lookup efficiency from O(n) to O(log n), a bucket's linked list is transformed into a balanced red-black tree if it exceeds a specific threshold (often 8 items).

4. Hash Table Organization

• Hash Function:
  The bucket index is calculated by combining the hashCode of a key with the length of the array.
• Key-Value Pair Storage:
  In either a linked list or a red-black tree, each key-value pair is kept in a node inside the relevant bucket.

Example Internal Representation

• table[0] → null
• table[1] → Entry(key1, value1, hash1) → Entry(key5, value5, hash5)
• table[2] → Entry(key2, value2, hash2)
• table[3] → null
• …
• table[n-1] → null or linked list/tree of entries

Summary

Hashing is the core mechanism behind the internal working of hashmap, ensuring efficient storage and retrieval of key-value pairs.

Hash Function

In order to find the index in the internal array (table[]) where the key-value pair should be placed, a hash function must first turn a given key into an integer.

Formula for Index Calculation

index = Math.abs(key.hashCode() % array.length);

However, Java optimizes this using bitwise operations in HashMap:

index = (hash & (array.length - 1));

Components of the Hash Function

1. hashCode() Method

Every object in Java has a hashCode() method, inherited from the Object class. It returns a unique integer (hash code) for that object.

Example

String key = "apple";
int hashValue = key.hashCode();  // Output: 93029210

2. Modulus Operation (% array.length)

The modulus operation converts the hashmap internal working value into an index that fits within the array bounds.

Example (assuming table.length = 16)

index = 93029210 % 16 = 10

This means "apple" will be stored at table[10].

3. Bitwise Optimization in Java 8+

Instead of using the modulus operator (%), Java 8+ optimizes hashmap internal implementation index calculation using bitwise operations.

Example

index = (hash & (table.length - 1));

This is faster than %, as bitwise operations are computationally cheaper.

Example: How Hashing Works

public class HashFunctionExample {
    public static void main(String[] args) {
        String key = "banana";
        int hash = key.hashCode();  // Generate hash
        int index = (hash & (16 - 1));  // Get index (if table size = 16)
        
        System.out.println("Hash Code: " + hash);
        System.out.println("Index in HashMap: " + index);
    }
}

Output:

Hash Code: 93987239
Index in HashMap: 7

Uniform Distribution in Hashing

A good hash function should:

1. Spread entries evenly across the table.
2. Minimize clustering, which happens when multiple keys get assigned to the same index.

If the hash function is poor, entries get concentrated in a few indexes, leading to:

• Collisions, in which many keys hash to the same index.
• Longer search times when retrieving values ininternal working of hashmap.

How Java Ensures Uniform Distribution

1. Secondary Hash Function (Java 8+)

An extra hashmap internal working step was added in Java 8 to improve uniform distribution by jumbling poorly dispersed hash codes.

Example

static final int hash(Object key) {
    int h = key.hashCode();
    return (h ^ (h >>> 16));
}

The bottom 16 bits and the higher 16 bits (h >>> 16) are XORed (^). This lessens clustering and collisions by distributing hash values more equally throughout the array.

2. Choosing Power-of-Two Table Size

Table.length is always set by Java as a power of two (e.g., 16, 32, 64). This makes it possible to calculate indexes efficiently using bitwise operations.

Example

index = (hash & (table.length - 1));

This ensures faster computation and better distribution of keys, minimizing collisions.

The dependability and efficiency of a HashMap depend on the hashCode() and equals() functions being implemented correctly. These methods determine how keys are stored, retrieved, and whether two keys are considered equal within the map.

Why hashCode() and equals() Matter

• hashCode():
  An integer hash for a key is generated by the hashCode() function. This hash is used by the HashMap to determine the bucket index in which the key-value pair will be kept. The hash function should evenly distribute keys among all buckets for effective storage and retrieval.
• equals():
  The equals() method checks if two keys are logically equivalent. HashMap utilizes equals() to differentiate between keys in the linked list or tree at a bucket when several keys hash to the same bucket (a collision).

The Contract Between hashCode() and equals()

• If two keys are considered equal by equals(), they must return the same hashCode().
• If two keys have the same hashCode(), they may or may not be equal according to equals() (collisions are possible).

How These Methods Affect HashMap Behavior

• Storage:
  When you insert a key-value pair, HashMap:
  
  1. Computes the key’s hash code to determine the bucket index.
  2. Looks for an existing entry with the same hash in the bucket.
  3. To verify key equality, use equals(). The value is changed if a match is discovered.
• Retrieval:
  When you retrieve a value by key, HashMap:
  
  1. Computes the hash code to find the bucket.
  2. Traverses the linked list or tree in that bucket.
  3. Uses equals() to locate the exact key.
• Custom Key Classes:
  If you use custom objects as keys, always override both hashCode() and equals() to ensure correct behavior. Failing to do so can result in lost entries, duplicate keys, or unexpected results.
• Mutable Objects as Keys:
  Mutable objects should not be used as keys. The key may become unusable or result in inconsistencies if the parameters that hashCode() or equals() depend on are changed after the insertion.

Example

class MyKey {

    private int id;
    private String name;

    // Always override both methods
    @Override
    public int hashCode() {
        return Objects.hash(id, name);
    }

    @Override
    public boolean equals(Object o) {
        if (this == o)
            return true;

        if (o == null || getClass() != o.getClass())
            return false;

        MyKey myKey = (MyKey) o;
        return id == myKey.id && Objects.equals(name, myKey.name);
    }
}

Practical Implications

• Effective hashCode implementations distribute entries equitably by reducing hash collisions.
• Key uniqueness and accurate value substitution are guaranteed by proper equals implementations. 
• Violating the contract can lead to hard-to-debug issues, such as missing or duplicate entries.

Collision resolution techniques are employed to effectively resolve conflicts when two keys provide the same hash index. Chaining is the main method used by Java's HashMap; balanced trees are also used as needed. Let's examine these methods in more depth.

1. Chaining

How

It Works

• Each index in the table holds a linked list (or a balanced tree if too many collisions occur).
• Multiple keys are saved as nodes in the linked list when they hash to the same index.
• On retrieval, Java iterates through the list to find the correct key.

Example Implementation

static class Entry<K, V> {
    final K key;
    V value;
    Entry<K, V> next;  // Pointer to the next entry in case of collision

    Entry(K key, V value, Entry<K, V> next) {
        this.key = key;
        this.value = value;
        this.next = next;
    }
}

2. Open Addressing

Open addressing locates an empty space within the hash table itself, in contrast to chaining, which uses linked lists to manage collisions. In the event of a collision, it uses a probing approach to find the next open place. To save memory cost, entries are stored directly in the array rather than storing several items at the same index.

Types of Probing

Linear Probing

In linear probing, the algorithm searches for the next available slot sequentially by incrementing the index by 1 each time until an empty slot is found.

Example

index = (hash + i) % table.length;

‍Quadratic Probing

In quadratic probing, the algorithm searches for the next available slot using a quadratic sequence, increasing the gap between probes exponentially.

Example

index = (hash + i * i) % table.length;

3. Double Hashing

In double hashing, a second hash function determines the step size for probing in internal working of hashmap. This ensures that each key has a unique probe sequence, preventing clustering issues.

Example

index = (hash + i * secondHash(key)) % table.length;

Load Factor

The load factor determines when a HashMap should expand. It represents the ratio of occupied buckets to the total number of buckets in the table.

Default Load Factor in Java

Java’s HashMap has a default load factor of 0.75, meaning the map resizes when 75% of the array is filled. This value provides a balance between time complexity and memory usage. A lower load factor reduces collisions but increases memory consumption, while a higher load factor saves space but increases search time.

Rehashing

When the total number of elements surpasses the threshold (capacity * load factor), Java doubles the array size and redistributes all entries to new positions.

Steps in Rehashing

1. Make a new array that is twice as large as the last table.
2. Using the increased table size, recalculate the index for every current item.
3. To ensure a fair distribution, move components to new locations.
4. If a chain has more than eight elements, convert long linked lists to red-black trees (Java 8+ optimization).

Example of Resizing

// Default capacity is 16, with a load factor of 0.75
HashMap<String, Integer> map = new HashMap<>(16, 0.75f);

The three main operations offered by HashMap are insertion, retrieval, and deletion. Hashing, bucket indexing, and collision handling techniques are used in these procedures to ensure optimal performance.

1. Insertion (put())

How It Works

1. Determine the index by computing the key's hash code.
2. Make a new entry if the bucket at the index is empty.
3. Go through the linked list in the event of a collision:
4. Update the key's value if it already exists.
5. If not, add the new entry at the chain's conclusion.
6. Convert a chain to a red-black tree (Java 8+) if it has more than eight elements.

Example

map.put("apple", 5);  // Hash computed, index found, value stored.
map.put("banana", 10); // New key, new index, value stored.
map.put("apple", 8);  // Key exists, updates value from 5 to 8.

2. Retrieval (get())

How It Works

1. Determine the matching index by computing the hash.
2. Return null if the bucket is empty.
3. Find the matching key by navigating the linked list or tree if there is a collision.
4. If the associated value is discovered, return it.

Example

System.out.println(map.get("apple")); // Output: 8
System.out.println(map.get("grape")); // Output: null (key not found)

3. Deletion (remove())

How It Works

1. Compute the hash and locate the key's index.
2. The key doesn't exist if the bucket is empty.
3. To locate the entry, navigate through any linked lists that may exist.
4. To keep the structure intact, remove the entry and modify the chain connections.
5. Delete the node and rebalance the tree if there is a tree at the index.

Example

map.remove("apple");  // "apple" entry is deleted.
System.out.println(map.get("apple")); // Output: null

HashMap maintains an average temporal complexity of O(1) for insertions, retrievals, and deletions by effectively managing load factors, rehashing, and core operations.

Quick Summary 

• Put() adds new key, value pairs or updates existing ones, which may require collision resolution and hashing.
• Get() retrieves values by identifying the right bucket and matching keys.
• Remove() deletes entries and may prompt the reorganization of linked lists or trees.
• When dealing with high, collision buckets, treeification (Java 8+) helps to prevent performance degradation.
• These operations achieve average O(1) time complexity when hashing is efficient.

The efficiency of a HashMap depends on how quickly it can store, retrieve, and remove elements. While HashMap is known for its fast performance, its actual efficiency is influenced by several internal factors such as collisions, resizing, key distribution, and synchronization.

1. Average Time Complexity

HashMap operations typically take O(1) time.

• The get() function uses the key's hash to directly obtain values.
• Key-value pairs are inserted into the proper buckets using the put() function.
• Based on the key lookup, the remove() action removes all items from the map.

If hash values are spread uniformly and collisions are minimized, this consistent time complexity can be attained. 

2. Worst-Case Time Complexity

Under adverse conditions, hashmap operations can degenerate to O(n) time.

• This occurs when multiple keys generate the same hash value
• A large number of collisions causes many entries to be stored in one bucket
• The map then behaves like a linear data structure

As a result, search, insert, and delete operations require traversing all entries in the bucket, leading to linear time complexity.

3. Collisions and Their Effect on Performance

When two or more keys map to the same bucket, a collision happens.

• The amount of comparisons needed during lookup is increased by collisions.
• Get(), put(), and delete() procedures are slowed down by high collision rates.
• Overall performance is greatly diminished by excessive collisions.

Using good hash functions and well-designed keys helps minimize collisions.

4. Load Factor and Resizing Cost

When a HashMap resizes is determined by the load factor.

• Performance and memory use are balanced by the default load factor of 0.75.
• The HashMap resizes and rehashes every entry when the threshold is surpassed.
• Resizing lowers performance momentarily and is computationally costly.

Reducing the frequency of resizing can be achieved by setting an adequate beginning capacity. 

5. Key Distribution and Hashing Quality

The distribution of keys has a significant impact on performance.

• The distribution of uniform keys disperses entries throughout several buckets.
• Bucket clustering is caused by poor key distribution.
• Clustering lengthens lookup times and raises the likelihood of collisions.

Distribution and efficiency are increased when immutable keys are used with trustworthy hash implementations. 

6. Fail-Fast Iterators and Safety

To ensure consistency throughout traversal, HashMap offers fail-fast iterators.

• An exception is raised if the map undergoes structural changes during iteration.
• This stops erratic behavior and logical mistakes.
• Fail-fast behavior aids in the early runtime detection of defects

It safeguards performance integrity even if it has nothing to do with speed. 

7. Synchronization and Its Impact on Performance

By default, HashMap is not thread-safe.

• Introducing synchronization causes locking overhead.
• Excessive synchronization lowers throughput.
• Access with improper sync might be harmful to the performance of multi, threaded systems.

It is advised to use specialized concurrent collections for concurrent situations.

Bottom Line

HashMap generally offers O(1) time complexity for get, put, and remove operations, but factors such as collisions, poor key distribution, resizing, and synchronization can degrade performance to O(n). Understanding these aspects is critical for building efficient and scalable applications.

A hash map is a powerful tool for the rapid search, insertion, and removal of elements, typically with a time complexity of O(1). This feature has made it one of the indispensable data structures that many real-world applications rely on, such as web development, database management systems, and compilers.

• Caching: In the internal working of HashMap, HashMap is used to store frequently accessed data in memory, reducing database queries and improving response time. It is widely used in web browsers, API gateways, and content delivery networks (CDNs). 
• Databases: To quickly retrieve records, Databases use HashMap, based indexing to map unique keys (e. g., primary keys) to the corresponding records. This greatly increases the speed of search without having to scan the entire dataset.
• Compiler Symbol Tables: The compiler uses the HashMap to store and quickly retrieve variable names, function definitions, and object references. This facilitates scope resolution, type checking, and memory allocation during code compilation.

Java HashMap incorporates a set of rules regarding the handling of null keys and null values, which are significant to fully grasp for the correct and productive usage of HashMap.

Null Key Handling

• One Null Key Allowed:
  HashMap only allows one null key. The prior value linked to null will be replaced if you add another item with a null key.
• Internal Storage:
  When a null key is inserted, HashMap treats it as a special case. The internal hash function assigns a hash of 0 to null, which always maps the entry to bucket/index 0 of the internal array.
• Retrieval and Update:
  All operations involving the null key (put, get, remove) interact with the entry stored at index 0. Only one such mapping can exist at a time.
• Example:

map.put(null, "first"); 
map.put(null, "second"); // The value for null is now "second"

Null Values Handling

• Multiple Null Values Allowed:
  HashMap allows any number of keys to be associated with a null value. There is no restriction on how many null values can be stored.
• No Special Treatment:
  Unlike null keys, null values are not treated specially during hashing or storage. They are handled just like any other value.
• Example:

map.put("a", null); 
map.put("b", null); // Both "a" and "b" are mapped to null

Practical Implications

• Null Key Use Cases:
  The ability to use a null key is helpful for representing missing or default values. However, frequent use of null keys can make code harder to read and maintain.
• Null Values:
  Storing null values is useful when you need to distinguish between a key not being present and a key explicitly mapped to null.
• Best Practices:
  
  • Avoid heavy reliance on null keys or values unless necessary for your application logic.
  • Be cautious when iterating or performing operations that may not expect nulls.

Summary Table

Understanding special behaviors of collections and following best practices helps you write efficient, safe, and predictable code, especially in real-world and multi-threaded applications.

1. Iteration Order

Not all collections maintain the order in which elements are inserted.

• HashMap does not guarantee any iteration order
• LinkedHashMap maintains insertion order (or access order if configured)
• TreeMap maintains sorted order based on keys

Best Practice:

Choose LinkedHashMap when a predictable iteration order is required, such as caching or displaying data in sequence.

2. Thread-Safety and Concurrent Access

Most collection classes are not thread-safe by default. When multiple threads modify a collection simultaneously, it may cause data inconsistency or runtime exceptions.

• ConcurrentHashMap provides thread-safe mechanisms without locking the entire map
• It allows high concurrency and better performance compared to synchronized maps

Best Practice:

Use ConcurrentHashMap in multi-threaded environments instead of synchronizing a HashMap.

3. Fail-Fast Iterators and Concurrent Modification

Fail-fast iterators detect structural modifications to a collection during iteration.

• If a collection is modified while iterating (except via iterator methods), a ConcurrentModificationException is thrown
• This behavior helps detect bugs early

Best Practice:

‍Avoid modifying collections while iterating, or use concurrent collections when modification is necessary.

4. Avoiding Mutable Keys

Keys used in maps should be immutable.

• Mutable keys can change their hash value after insertion
• This makes the entry unreachable and breaks map behavior

Best Practice:

Always use immutable keys such as String, wrapper classes, or custom immutable objects in maps.

5. Load Factor and Initial Capacity

When a hash-based collection resizes is determined by the load factor.

• 0.75 is the default load factor.
• Resizing has an impact on performance and is costly.

Best Practice:

To prevent repeated resizing, establish the starting capacity and select a suitable load factor if you know the approximate number of pieces beforehand. 

6. Choosing the Right Collection

Selecting the appropriate collection will largely depend on your ordering requirements, thread safety, read/write patterns, and performance needs.

Best Practice:

Choose collections based on use case requirements rather than convenience.

Bottom Line

It is more about understanding iteration behavior, thread safety, immutability, and the underlying resizing mechanics rather than only syntax that will allow you to use collections effectively.

An essential Java data structure, hash maps offer effective key-value storage with an average O(1) time complexity for insertion, lookup, and deletion. Java boosts HashMap's level of work for real-world scenarios by using methods like hashing, collision resolution, and treeification. HashMaps are frequently used in caching, indexing, and compiler design because they strike a compromise between memory economy and quick retrieval. Developers may design more effective code and identify performance issues in large-scale systems by understanding the internal working of HashMap.

Points to Remember 

1. To increase efficiency, bitwise operations on the hash value rather than a straight modulus operation are used to determine the bucket index in a hash map.
2. Java prevents performance loss by converting linked lists into red-black trees when collisions exceed a certain threshold.
3. Rehashing all of the current entries is an expensive process that should be avoided when resizing a hash map.
4. Because changes impact hashCode and equals consistency, using mutable objects as keys might disrupt HashMap behavior.
5. The quality of the hash distribution has a greater impact on hash map performance than the quantity of data stored.

1. What is the main advantage of using a HashMap?

For big datasets, a hash map is significantly quicker than data structures like arrays or linked lists since it offers O(1) time complexity for key-based lookups, insertions, and removals.

2. How are collisions handled by HashMap?

Chaining is the main method used by Java's HashMap to store numerous elements at the same index in a linked list. For improved performance, a bucket that has more than eight entries changes to a balanced tree.

3. Why does Java's HashMap employ a power-of-two table size?

Instead of utilizing the slower modulus operation, power-of-two sizing enables efficient index computation using bitwise operations (hash & (table.length - 1)).

4. What happens when a HashMap reaches its load factor threshold?

Java resizes the HashMap by doubling its capacity and rehashing all current entries to new indices when the load factor (default 0.75) is surpassed.

5. What is treeification in HashMap?

When a bucket has more than eight entries, treeification takes place, transforming the linked list into a red-black tree to reduce search time from O(n) to O(log n).

6. Is it possible for a HashMap to include null values and null keys?

Yes, one null key and several null values are supported by Java's HashMap. However, in some applications, regular use of null keys might result in unexpected behavior.

7. How does HashMap ensure uniform key distribution?

Java uses an improved hashing mechanism (hash ^ (hash >>> 16)) to spread hash codes more evenly, reducing clustering and improving performance.