What Is Hashing in Data Structure?


Angela Bailey

Hashing is an essential concept in data structure that plays a crucial role in various applications. It is a technique used to map data items to a fixed-size array called a hash table. In this article, we will explore what hashing is, how it works, and its significance in data structure.

Understanding Hashing

Hashing is essentially a process of converting an input (or key) into an index within the hash table using a hash function. The hash function takes the key as input and computes the corresponding index or address within the hash table where the value associated with that key will be stored.

The primary goal of hashing is to achieve efficient retrieval and storage of data by minimizing search time. By mapping keys directly to their corresponding memory addresses, hashing allows for constant-time access to values, making it ideal for applications where quick searches are necessary.

Hash Functions

A crucial component of hashing is the hash function. It takes an input and produces a fixed-size output called the hash code or hash value. A good hash function should have the following properties:

  • Deterministic: Given the same input, the hash function should always produce the same output.
  • Uniform Distribution: The output should be evenly distributed across the entire range of possible values.
  • Collision Resistance: Collisions occur when two different inputs produce the same output. A good hash function minimizes collisions.

The quality of a hash function greatly affects the efficiency and effectiveness of hashing. A poorly designed or weak hash function may result in frequent collisions, leading to degraded performance.

The Hash Table

The actual storage structure used in hashing is called a hash table. It is an array of fixed size, typically based on the expected number of elements or the desired performance characteristics. Each element in the hash table is called a bucket or slot.

When inserting a value into the hash table, the hash function is applied to the key to determine the index where the value will be stored. If there are no collisions (i.e., multiple keys mapping to the same index), the value can be directly placed in that bucket. However, if a collision occurs, various techniques are used to handle it.

Collision Handling

Collisions are unavoidable in most practical scenarios due to limited hash table sizes and different keys generating the same hash code. There are several methods used to handle collisions:

  • Separate Chaining: Each bucket in the hash table contains a linked list of values that map to that index. When a collision occurs, new values can be appended to this linked list.
  • Open Addressing: In this approach, when a collision occurs at a particular index, an alternative index is determined using predefined rules such as linear probing or quadratic probing.

The choice of collision handling technique depends on factors such as expected data distribution and retrieval patterns.


Hashing is an efficient data structure technique used for quick storage and retrieval of data items. It leverages hash functions and hash tables to map keys directly to memory addresses, resulting in constant-time access. Understanding hashing and its associated concepts is crucial for developing efficient algorithms and managing large datasets effectively.

To summarize:

  1. Hashing maps data items to a fixed-size array called a hash table.
  2. A hash function converts the input (key) into an index within the hash table.
  3. A good hash function is deterministic, uniformly distributes values, and minimizes collisions.
  4. The hash table is the actual storage structure used in hashing.
  5. Collision handling techniques include separate chaining and open addressing.

By incorporating hashing into your algorithms, you can significantly improve performance when dealing with large datasets or frequently accessed data items.

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