We all are using Google or Instagram in our day-to-day lives and both the applications can find the things quickly, and the answer to that logic comes under hashing in data structure. In the world of computer science we are dealing with lot of data everyday in the form of files, numbers, names and many more.
And searching all data one by one is like a hell for findings that’s where the concept of hashing in data structure comes in picture. It’s like giving every piece of data a shortcut or unique address to search for. In this blog we will discuss about what is hashing data structure, how it works, types of hashing in data structure, collision resolution techniques, time and space complexity of hash data structure and so on.
What is Hashing in Data Structure?
Hashing in data structure is a technique which is used to store and find data quickly. Instead of checking every element one by one, hashing is using a special term called hash function to perform the respective tasks. This hash value tells the computer exactly where to store or look for your data in a hash table.
Let’s explore a practical example to understand this concept properly. For that think a real world example where we are storing books in a library and instead of searching each selves one by one to be there in a library room, we have assigned respective numbers to the books under each locker so that finding books from the shelves becomes easy and flexible. So this example considered to be a proper example to understand the concept of learning in hashing data structure.
How Hashing Data Structure Works?
In this section we will look at the different steps to understand the working of hashing data structure in computer science.
| Step | Action | Example |
| Step 1 | Take the input key | Key = 27 |
| Step 2 | Apply the hash function | Hash = key % table_size = 27 % 10 = 7 |
| Step 3 | Use the result as an index in hash table | Store the value at index 7 in the hash table |
| Step 4 | To search, apply the same hash function | Search Key = 27 → Hash = 27 % 10 = 7 |
| Step 5 | Access the index directly to get the value | Go to index 7 and retrieve the data |
To understand the above table we have taken input key is 27 and using the hash function to generalize the formula to fetch the entry from the hash table. In the second step we are basically applying the hash function where we generalize it with the formula Hash = key % table_size = 27 % 10 = 7. Post this we use the result as an index in hash table.
If we want to search any element the we need to apply the same hash function and access the index directly to get the value. So these all are basic steps that we need to add for the hashing data structure to work properly.
Types of Hashing in Data Structure
Direct Addressing
In direct addressing, each key is used as an index to store data directly in an array. This method is very fast (O(1) time), but it only works well when the key values are small and not too far apart. It is not practical for large key ranges due to high memory usage.
Chaining
Chaining handles collisions by storing multiple elements at the same index using a linked list (or any dynamic structure). When two keys hash to the same index, they are simply added to the list at that index. This keeps the hash table efficient even when collisions occur.
Open Addressing
In open addressing, all elements are stored within the hash table itself. When a collision occurs, the algorithm searches for the next available slot using methods like linear probing or quadratic probing. This approach avoids linked lists but requires careful probing to avoid clustering.
Perfect Hashing
Perfect hashing is used when the set of keys is fixed and known in advance. It creates a hash function that guarantees no collisions, making search operations always O(1). This method is ideal for static datasets like reserved keywords in compilers.
Collision Resolution Techniques
| Type | How It Works? | When to Use |
| Direct Addressing | Key = index directly | Small, fixed key ranges |
| Chaining | Linked list at each slot | Handles collisions easily |
| Open Addressing | Probe sequence within table | Compact tables, no extra lists |
| Perfect Hashing | Collision-free, static set | Fixed key sets—no dynamic updates |
| Universal Hashing | Random hash function from a family | Adversarial or unpredictable keys |
| Division/Folding/Mid-square | Simple arithmetic or bit methods | Basic use cases, easy implementation |
| Extendible/Linear | Dynamic, grows/shrinks on demand | DBs & filesystems with dynamic load |
| Consistent Hashing | Virtual ring, ideal for distributed systems | Data sharding & distributed caches |
| 2‑Choice Hashing | Two hash choices, choose the less full slot | High-performance in-memory tables |
Time and Space Complexity of Hashing Data Structure
| Operation | Best Case | Average Case | Worst Case |
| Search | O(1) | O(1) | O(n) |
| Insert | O(1) | O(1) | O(n) |
| Delete | O(1) | O(1) | O(n) |
| Space | Nil | O(n) | O(n) |
Advantages of Hashing Data Structure
- Fast data access using key based lookup (O(1) time on average).
- Efficient for large datasets with quick insert, delete, and search.
- Easy to implement with simple arithmetic functions.
- Ideal for applications like databases, caches, and dictionaries.
- Can handle dynamic and static data efficiently with proper methods.
Disadvantages of Hashing in Data Structure
- Collisions can reduce performance if not handled properly.
- Hashing does not maintain any order of data.
- Fixed-size hash tables can become inefficient if overloaded.
- Designing a good hash function can be tricky.
- Rehashing during resizing is time-consuming and costly.
Applications of Hashing in Data Structure
- Used in implementing hash tables, maps, and dictionaries.
- Helps in quick data retrieval in database indexing.
- Used for storing and verifying passwords securely.
- Supports caching mechanisms in web browsers and servers.
- Plays a key role in compiler symbol tables and memory management.
Conclusion
Hashing in data structure helps store and find data quickly and easily it avoids checking each item one by one by using a special function that sends the data directly to the right place this method saves a lot of time and works well for tasks like checking passwords searching in databases and storing user details
A good hash function and proper handling of collisions make hashing more effective and reliable it performs well especially when the data is large and needs quick access many systems like search engines and online shopping platforms.
You can use hashing to create faster programs and smarter solutions it is easy to learn and powerful for both new and experienced programmers who want to write better and more efficient code
Frequently Asked Questions
What is the purpose of performing hashing in data structure?
Hashing data structure is a very important concept in computer science and we would use this concept when we use to retrieve data faster and might it uses key that align with the table and converts keys into indexes using hash table.
What is a collision in hashing data structure?
Collision is a condition that occurs when two keys are collide at the same index of the hash table.
Which is the best collision resolution technique?
There is no one size that fits all but most probably chaining is easier to implement whereas open addressing collision technique requires more memory as it is less memory efficient.
Is hashing data structure used in AI & machine learning?
Yes, hashing data structure is used in feature engineering and also in several concepts of AI & Machine Learning.
Can we resize a hash table?
Yes we can resize a hash table but it is very costly operation to perform.