Data structures 101: Implement hash tables in JavaScript

Q: Is `Map` in JavaScript a hash table?

No, the `Map` object in JavaScript is not strictly a hash table, but it is similar in functionality. Both `Map` and hash tables store key-value pairs and allow efficient lookups. However, `Map` is an abstracted implementation that uses hash functions internally, along with other optimizations, to manage its data. Unlike a traditional hash table, `Map` supports any data type (objects, functions, primitives) as keys, ensuring flexibility and efficient operations.

Q: What is the difference between `HashTable` and `Map` in JavaScript?

**`HashTable`:** - A traditional data structure that stores key-value pairs. - Keys are hashed to generate an index for efficient retrieval. - Not built-in into JavaScript, but can be implemented manually or via libraries. **`Map`:** - A built-in JavaScript object introduced in ES6. - Allows keys of any type (e.g., objects, functions). - Maintains the insertion order of entries. - Provides built-in methods like `.set()`, `.get()`, and `.delete()` for easy manipulation.

Q: Which is better, `HashMap` or `Hashtable`?

**`HashMap` (not built-in in JavaScript but common in Java)** - Non-synchronized (not thread-safe). - Faster performance in single-threaded environments. - Allows `null` as a key. **`Hashtable`** - Synchronized (thread-safe). - Slower performance due to synchronization overhead. - Typically doesn't allow `null` keys. For JavaScript specifically, `Map` is often preferred over custom hashtable implementations due to its simplicity, built-in support, and efficiency.

Q: Why does `HashMap` allow null keys?

`HashMap` in Java (not JavaScript) allows `null` keys because its internal implementation treats a `null` key as a special case. The key is stored in the first bucket, and operations like `put()` or `get()` are handled explicitly for `null`. This design decision makes the data structure more flexible. In JavaScript, since `Map` supports keys of any type, you can also use `null` or `undefined` as keys: ```js const map = new Map(); map.set(null, "value"); console.log(map.get(null)); // Output: "value" ```

Key takeaways:

A hash table is a data structure that maps keys to values using a hash function, enabling constant-time lookup, insertion, and deletion operations. In short, it operates like a dictionary, which is ideal for fast data retrieval.
A hash function maps keys to specific indexes in an array for efficient data distribution.
Linear probing, chaining, resizing the array, and double hashing are common strategies to manage collisions.
Hash tables are extensively used in database indexing, caches, and managing large datasets with fast lookups.

Ever wondered how Google Maps finds the nearest coffee shop almost instantly? Behind the scenes, hash tables are doing the heavy lifting, enabling quick data lookups that feel like magic.

Mastering hash tables isn’t just about writing efficient code; it’s a must-have skill for cracking coding interviews and building scalable systems.

We’ll continue our data structures series with one of the top data structures: the hash table. In this blog, we’ll learn what hash tables are, what they are used for, and how to implement them in JavaScript.

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What is a hash table?#

A hash table (often called a hash map) is a data structure that maps keys to values using a hash function. Hash tables efficiently combine lookup, insert, and delete operations. The key is sent to a hash function that performs arithmetic operations on it. The result (called the hash value or hash) is an index of the key-value pair. This allows for fast access to values associated with a specific key.

Pro tip: Think of this as a signature on a block of data that allows us to search in constant time. A hash table operates like a dictionary that we can use to map from the hash to the desired data.

This data structure is widely used in computer software, particularly for associative arrays, database indexing, caches, and sets. Usually, this operation returns the same hash for a given key.

The performance of a hash table depends on three fundamental factors: the hash function, the size of the hash table, and the collision handling method.

Hash tables are made up of two parts:

Data storage object: An object with a table where the data is stored. The array holds all the key-value entries in the table. The size of the array should be set according to the amount of data expected.
Hash function (or mapping function): This function determines the index of our key-value pair. It should be a one-way function and produce a different hash for each key.

Note: In JavaScript, hash tables are generally implemented using arrays as they provide access to elements in constant time.

Now that we’ve seen how hash tables work, let’s explore where they shine in real-world applications.

Uses of hash tables#

Hash tables provide constant-time access to elements, so they are highly recommended for algorithms that prioritize search and data retrieval operations. Hashing is ideal for large amounts of data, as it takes a constant amount of time to perform insertion, deletion, and search.

In terms of time complexity, the operation is $O(1)$ . On average, a hash table lookup is more efficient than other table lookup data structures. Some common uses of hash tables are ad follows:

Database indexing: Hash tables are used to quickly search for records.
Caches: Hash tables store frequently accessed data for fast retrieval.
Associative arrays: Many programming languages, including JavaScript, use hash tables to implement associative arrays (objects).
Unique data representation: Hash tables can efficiently store unique keys, ensuring data integrity and fast retrieval.
Lookup in an unsorted array: A hash table provides a faster alternative to linear search when checking for the existence of an element in an unsorted dataset.
Lookup in sorted array using binary search: Hash tables can complement binary search in sorted arrays by acting as an index or cache for rapid lookups.

Hash tables vs. trees#

Hashing and trees perform similar jobs, but various factors in your program determine when to use one over the other.

Trees are more useful when an application needs to order data in a specific sequence. Hash tables are the smarter choice for randomly sorted data due to its key-value pair organization.

Hash tables can perform in constant time, while trees usually work in $O(\log n)$ . In the worst-case scenario, the performance of hash tables can be as low as $O(n)$ . An AVL tree, however, would maintain $O(\log n)$ in the worst case.

An efficient hash table requires a hash function to distribute keys. A tree is simpler, since it accesses extra space only when needed and does not require a hash function. Here’s the table comparing hash tables and trees:

Aspect	Hash Table	Tree
Data Organization	Organizes data in key-value pairs.	Organizes data hierarchically with nodes and edges, often maintaining a specific order.
Performance	Average time complexity for operations is O(1). Worst-case time complexity is O(n).	Time complexity for operations is O(log n), even in the worst case (e.g., AVL tree).
Order of Data	No inherent order; best for randomly sorted data.	Maintains a specific sequence or order of data.
Use Case	Ideal for fast lookups and random access.	Suitable when data needs to be sorted or accessed in a defined order.
Space Requirements	Requires a hash function and extra space for hashing.	Requires extra space only for tree nodes and balancing as needed.
Hash Function	Essential to distribute keys efficiently.	Not required, making implementation simpler.

What is a hash function?#

A hash function is a method or function that takes an item’s key as input, assigns a specific index to that key, and returns the index whenever the key is looked up. This operation usually returns the same hash for a given key. A good hash function should be efficient to compute and uniformly distribute keys.

Hash functions help to limit the range of the keys to the boundaries of the array, so we need a function that converts a large key into a smaller key. This is the job of the hash function.

Common hash functions#

There are many kinds of hash functions that have different uses. Let’s take a look at some of the most common hash functions used in modern programming.

Arithmetic modular: In this approach, we take the modular of the key with the list/array size: index=key MOD tableSize. So, the index will always stay between 0 and tableSize - 1.
Truncation: Here, we select a part of the key as the index rather than the whole key. We can use a mod function for this operation, although it does not need to be based on the array size.
Folding: This approach involves dividing the key into small chunks and applying a different arithmetic strategy at each chunk.

Hash table collisions#

Sometimes, a hash function can generate the same index for more than one key. This scenario is referred to as a hash collision. Collisions are a problem because every slot in a hash table is supposed to store a single element.

Best collision handling strategies in hash tables#

When two keys collide in a hash table, it’s like two cars fighting over the same parking spot. Here’s how we keep the traffic flowing smoothly.

Linear probing: Linear probing works by skipping over an index that is already filled. It could be achieved by adding an offset value to an already computed index. If that index is also filled, add it again, and so on.
One drawback of using this strategy is that if you don’t pick an offset wisely, you can jump back to where you started and miss out on so many possible positions in the array.

Resizing the array or list: Another way to reduce collisions is to resize the list or array. We can set a threshold, and once it is crossed, we can create a new table that is double the size of the original. All we have to do then is to copy the elements from the previous table. Resizing the list or array significantly reduces collisions, but the function itself is costly. Therefore, we need to be careful about the threshold we set. A typical convention is to set the threshold at 0.6, which means that when 60% of the table is filled, the resize operation needs to take place.
Double hashing: In double hashing, there are two hash functions. The second hash function provides an offset value in case the first function causes a collision. Double hashing can find the next free slot faster than a linear probing approach. This is useful for applications with a smaller hash table. The following function is an example of double hashing:

In the above code:

The calculateHash() method is called to compute the hash for the given key. This hash determines the bucket (or slot) where the key-value pair will be stored.
If the hash does not exist as a property in the values object, a new empty object is created at that hash location.
If the specific key does not exist within the bucket (object) at the hash location, the length property of the hash table is incremented, indicating a new unique key.
The key-value pair is added to the hash table at the appropriate hash bucket. If the key already exists, its value is updated with the new value.

The search() function#

Searching in a hash table is very fast. Unlike with an array, where we have to go through all of the elements until we reach the item we need, with a hash table, we simply get the index.

Let’s add the complete code for our hash table implementation below.

Similar to the add() function, the search() function:

Calculates the hash for the given key using the calculateHash() method. This identifies the bucket where the key might be stored.
Checks if the hash exists in the hash table using hasOwn() and if the specific key exists within that hash bucket.
- If both the hash and key exist, the function retrieves and returns the value associated with the key.
- If either the hash or key does not exist, the function returns null to indicate that the key was not found.

The delete() function#

Now, let's look at how the delete() function works to remove key/value pairs from the hash table:

In the above code:

The delete() function begins by calculating the hash of the provided key using the calculateHash() method. This determines the location of the key in the hash table.
The delete() function checks if the hash exists in the hash table and if the specified key exists within that hash bucket. If the key is found, we proceed to delete it.
The delete operator is used to remove the key/value pair from the corresponding hash bucket.
After successfully deleting the key, the function decrements the length property of the hash table to reflect the removal of the key/value pair.
If the hash bucket becomes empty after the key is removed (i.e., no other keys remain under that hash), the entire hash bucket is deleted to free up memory and keep the hash table clean.
If the key was successfully deleted, the function returns true. If the key doesn’t exist in the hash table, the function returns false.

The complete code#

That completes our basic JavaScript hash table implementation.

Javascript (babel-node-es2024)

class HashTable {
    constructor() {
        this.values = {};
        this.length = 0;
        this.size = 0;
    }
    calculateHash(key) {
        return key.toString().length % this.size;
    }
    add(key, value) {
        const hash = this.calculateHash(key);
        if (!Object.hasOwn(this.values, hash)) {
            this.values[hash] = {};
        }
        if (!Object.hasOwn(this.values[hash], key)) {
            this.length++;
        }
        this.values[hash][key] = value;
    }
    search(key) {
        const hash = this.calculateHash(key);
        if (Object.hasOwn(this.values, hash) && Object.hasOwn(this.values[hash], key)) {
            return this.values[hash][key];
        } else {
            return null;
        }
    }
    delete(key) {
        const hash = this.calculateHash(key);
         if (Object.hasOwn(this.values, hash) && Object.hasOwn(this.values[hash], key)) {
            delete this.values[hash][key];
            this.length--;
            // Clean up empty buckets
            if (Object.keys(this.values[hash]).length === 0) {
                delete this.values[hash];
            }
            return true;
        }
        return false;
    }
}
// Create an object of type HashTable
const ht = new HashTable();
ht.size = 10; // Set the size of the hash table
// Add data to the hash table
ht.add("Canada", "300");
ht.add("Germany", "100");
ht.add("Italy", "50");
// Search
console.log(ht.search("Italy")); // Output: 50
// Delete
console.log(ht.delete("Italy")); // Output: true
console.log(ht.search("Italy")); // Output: null
console.log(ht.delete("Italy")); // Output: false

Note that we used an Object to represent our hash table. Objects in JavaScript are actually implemented using hash tables themselves! Many programming languages also provide support for hash tables either as built-in associative arrays or as standard library modules.

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The constructor of the HashEntry class initializes an entry to store a key-value pair. It also includes a next property to handle collisions by linking entries in the same bucket. This structure is designed to store data efficiently in a hash table.

The revised HashTable class#

Now, we’ll create the HashTable class, which is a collection of HashEntry objects. We’ll keep track of the number of slots in the table and the current size of the hash table. These variables will come in handy when we need to resize the table.

The HashTable class provides the basic structure and functionality of a hash table. Here's a breakdown of the code:

The constructor initializes the hash table. We specify the total number of slots (buckets) in the hash table, which will hold the entries. We keep track of the current number of entries in the hash table. It is used to monitor when resizing might be needed. We create an array to store the entries. Each entry will be a HashEntry object. We initialize all slots in the bucket array to null. This ensures the hash table starts empty.
The get_size() function returns the number of entries currently in the hash table. It helps track how many elements are stored and monitor when resizing is required.
The isEmpty() function checks if the hash table is empty by comparing the size to 0. We return true if the hash table has no entries; otherwise, false.

The getIndex() function#

The next thing we need is a hash function. In the previous lessons, we tried out some different approaches. For our implementation, we will simply take the modular of the key with the total size of the hash table (slots).

add(key, value) {
    const index = this.getIndex(key); // Compute the hash index
    const newEntry = new HashEntry(key, value); // Create a new entry
    if (this.bucket[index] === null) {
        this.bucket[index] = newEntry; // Insert if bucket is empty
    } else {
        let current = this.bucket[index];
        while (current.next !== null) { // Traverse to the end
            if (current.key === key) { // Key exists
                current.value = value; // Update value
                return;
            }
            current = current.next;
        }
        current.next = newEntry; // Add new entry to the chain
    }
    this.size++; // Increment size
}

The add() function

delete(key) {
    const index = this.getIndex(key); // Compute the hash index
    let current = this.bucket[index];
    let prev = null;
    while (current !== null) {
        if (current.key === key) { // Key found
            if (prev === null) {
                // If the key is the first in the chain
                this.bucket[index] = current.next;
            } else {
                // Skip the current entry
                prev.next = current.next;
            }
            this.size--; // Decrement size
            return true; // Successfully deleted
        }
        prev = current;
        current = current.next; // Move to the next entry
    }
    return false; // Key not found
}

The delete() function

Javascript (babel-node-es2024)

class HashEntry {
  constructor(key, value) {
    this.key = key;         // Key for the entry
    this.value = value;     // Value associated with the key
    this.next = null;       // Reference to the next entry in the chain
  }
}
class HashTable {
  constructor(slots = 10) {
    this.slots = slots;                  // Number of slots in the hash table
    this.size = 0;                       // Current number of entries
    this.bucket = new Array(this.slots).fill(null); // Array of buckets
  }
  // Hash function
  getIndex(key) {
    return key % this.slots;             // Compute index using modulo operation
  }
  // Add key-value pair
  add(key, value) {
    const index = this.getIndex(key);    // Compute the hash index
    const newEntry = new HashEntry(key, value);
    if (this.bucket[index] === null) {
      this.bucket[index] = newEntry;   // Insert entry if bucket is empty
    } else {
      let current = this.bucket[index];
      while (current !== null) {
        if (current.key === key) {   // Key already exists, update value
          current.value = value;
          return;
        }
        if (current.next === null) break; // Traverse to the end of the chain
        current = current.next;
      }
      current.next = newEntry;        // Add new entry at the end of the chain
    }
    this.size++;                         // Increment size
  }
  // Search for a key
  search(key) {
    const index = this.getIndex(key);    // Compute the hash index
    let current = this.bucket[index];   // Retrieve the chain at the index
    while (current !== null) {           // Traverse the chain
      if (current.key === key) {       // Key found
        return current.value;        // Return the associated value
      }
      current = current.next;          // Move to the next entry
    }
    return null;                         // Key not found
  }
    // Delete a key-value pair
  delete(key) {
    const index = this.getIndex(key);    // Compute the hash index
    let current = this.bucket[index];
    let prev = null;
    while (current !== null) {
      if (current.key === key) {       // Key found
        if (prev === null) {
          this.bucket[index] = current.next; // Remove first entry
        } else {
          prev.next = current.next; // Skip the current entry
        }
        this.size--;                  // Decrement size
        return true;                  // Successfully deleted
      }
      prev = current;
      current = current.next;           // Move to the next entry
    }
    return false;                         // Key not found
  }
}
// Test the Hash Table implementation
const ht = new HashTable();
ht.add(1, "Educative");
ht.add(11, "JavaScript");
ht.add(21, "HashTable");
// Search for a key
console.log(ht.search(11));  // Output: JavaScript
// Delete a key
console.log(ht.delete(11));  // Output: true
// Try to search for the deleted key
console.log(ht.search(11));  // Output: null

Note that we used a HashEntry class to represent each entry and an array to represent the hash table’s buckets. Many programming languages provide built-in support for hash tables, either as associative arrays or standard library modules, but this implementation demonstrates how a hash table can be built from scratch using JavaScript.

Comparison of hash table implementations#

In general, bucket chaining provides better overall performance and flexibility, especially in cases where collision management is a concern. Without bucket chaining, the hash table is more straightforward and efficient for simple use cases with low collision rates, but may suffer in more complex situations. Here is a detailed comparison table that highlights the differences between the hash function without bucket chaining and the hash function with bucket chaining:

Aspect	Without Bucket Chaining	With Bucket Chaining
Collision Handling	Uses linear probing or double hashing to resolve collisions.	Stores multiple key-value pairs in linked lists at each index.
Data Structure	Single value per index (each index stores one key-value pair).	Each index contains a linked list (chain) or another collection to store multiple key-value pairs.
Key-value Pair Storage	Limited to one key-value pair at each index, leading to overwriting or collisions in case of key conflicts.	Supports multiple key-value pairs at the same index, thus preventing overwriting.
Search Complexity	O(1) in the best case, O(n) in the worst.	O(1) in the best case, O(n/k) in the average case (k = bucket size).
Insertion Complexity	Fast for unique keys but slower with collisions due to probing.	Slightly slower due to traversal, but better handles collisions.
Delete Complexity	Deletion is complex, as probing may require shifting elements to fill the gap left by the deleted key-value pair.	Deletion is easier, as it involves unlinking the key-value pair from the linked list at the respective index.
Space Efficiency	More efficient initially, but may degrade as collisions grow.	Slightly more space due to the need for linked lists.
Scalability	Degrades with more data or poor hash functions.	Scales better with unpredictable datasets.
Performance in Dense Data	Performance suffers when the hash table is nearly full, leading to more collisions and, thus, more probing or overwriting.	Handles dense data better, as collisions can be managed by simply adding more entries to the chain rather than needing to rehash or probe.
Implementation Complexity	Simpler to implement but may require handling special cases for collisions (like probing or rehashing).	More complex to implement due to linked list (or array) management for each index, but the collision handling is more elegant and scalable.
Memory Management	Generally less memory-intensive per slot (single key-value), but requires more memory in the form of extra space when collisions occur or the table needs resizing.	Memory usage can increase as chains grow, especially if there are many collisions, but chains prevent wastage by keeping entries at the same index.
Handling of Large Datasets	Performance degrades with the growth of data, particularly with larger key sets and collisions.	Can handle large datasets more efficiently due to chaining, which spreads the load across linked lists, making it more resilient to collisions.
Example Use Case	Useful when collisions are rare or predictable and the hash table space is not frequently full.	Ideal when there are frequent or unpredictable collisions, and space efficiency is less critical.

Wrapping up and interview questions#

Hash tables aren’t just an academic concept—they power some of the fastest applications in the world. So, here’s your challenge: Try re-implementing a common JavaScript feature (like object key lookups) using a custom hash table. It’ll be tough, but you’ll level up fast.

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Frequently Asked Questions

Is `Map` in JavaScript a hash table?

No, the Map object in JavaScript is not strictly a hash table, but it is similar in functionality. Both Map and hash tables store key-value pairs and allow efficient lookups. However, Map is an abstracted implementation that uses hash functions internally, along with other optimizations, to manage its data. Unlike a traditional hash table, Map supports any data type (objects, functions, primitives) as keys, ensuring flexibility and efficient operations.

Is there a hash function in JavaScript?

JavaScript does not have a built-in hash function in its core language. However, developers can implement custom hash functions or use libraries like crypto (Node.js) for cryptographic hashing. For example, you can use the crypto.createHash() method in Node.js to generate hash values. Additionally, objects in JavaScript use internal mechanisms to generate “hash-like” keys for efficient storage and retrieval.

Are there hash tables in JavaScript?

JavaScript does not have a native “HashTable” data structure, but it provides similar functionality through objects and the Map object. Objects in JavaScript can store key-value pairs, with string keys implicitly hashed for efficient lookup. However, Map is more versatile, supporting keys of any data type and offering better performance for frequent additions and deletions.

What is the difference between `HashTable` and `Map` in JavaScript?

HashTable:

A traditional data structure that stores key-value pairs.
Keys are hashed to generate an index for efficient retrieval.
Not built-in into JavaScript, but can be implemented manually or via libraries.

Map:

A built-in JavaScript object introduced in ES6.
Allows keys of any type (e.g., objects, functions).
Maintains the insertion order of entries.
Provides built-in methods like .set(), .get(), and .delete() for easy manipulation.

Which is better, `HashMap` or `Hashtable`?

HashMap (not built-in in JavaScript but common in Java)

Non-synchronized (not thread-safe).
Faster performance in single-threaded environments.
Allows null as a key.

Hashtable

Synchronized (thread-safe).
Slower performance due to synchronization overhead.
Typically doesn’t allow null keys.

For JavaScript specifically, Map is often preferred over custom hashtable implementations due to its simplicity, built-in support, and efficiency.

Why does `HashMap` allow null keys?

HashMap in Java (not JavaScript) allows null keys because its internal implementation treats a null key as a special case. The key is stored in the first bucket, and operations like put() or get() are handled explicitly for null. This design decision makes the data structure more flexible.

In JavaScript, since Map supports keys of any type, you can also use null or undefined as keys:

const map = new Map();
map.set(null, "value");
console.log(map.get(null));  // Output: "value"

Data structures 101: Implement hash tables in JavaScript

What is a hash table?#

Uses of hash tables#

Hash tables vs. trees#

What is a hash function?#

Common hash functions#

Hash table collisions#

Best collision handling strategies in hash tables#

How to implement a hash table in JavaScript#

The HashTable class#

The calculateHash() function#

The add() function#

The search() function#

The delete() function#

The complete code#

Implementing a hash table with bucket chaining#

The HashEntry class#

The revised HashTable class#

The getIndex() function#

The search() function#

The add() function#

The delete() function#

The complete code#

Comparison of hash table implementations#

Wrapping up and interview questions#

Continue reading about JavaScript#

Frequently Asked Questions

Is `Map` in JavaScript a hash table?

Is there a hash function in JavaScript?

Are there hash tables in JavaScript?

What is the difference between `HashTable` and `Map` in JavaScript?

Which is better, `HashMap` or `Hashtable`?

Why does `HashMap` allow null keys?

Data structures 101: Implement hash tables in JavaScript

What is a hash table?#

Uses of hash tables#

Hash tables vs. trees#

What is a hash function?#

Common hash functions#

Hash table collisions#

Best collision handling strategies in hash tables#

How to implement a hash table in JavaScript#

The HashTable class#

The calculateHash() function#

The add() function#

The search() function#

The delete() function#

The complete code#

Implementing a hash table with bucket chaining#

The HashEntry class#

The revised HashTable class#

The getIndex() function#

The search() function#

The add() function#

The delete() function#

The complete code#

Comparison of hash table implementations#

Wrapping up and interview questions#

Continue reading about JavaScript#

Frequently Asked Questions

Is Map in JavaScript a hash table?

Is there a hash function in JavaScript?

Are there hash tables in JavaScript?

What is the difference between HashTable and Map in JavaScript?

Which is better, HashMap or Hashtable?

Why does HashMap allow null keys?

Is `Map` in JavaScript a hash table?

What is the difference between `HashTable` and `Map` in JavaScript?

Which is better, `HashMap` or `Hashtable`?

Why does `HashMap` allow null keys?