How Many Types of Searching Are There in Data Structure?


Angela Bailey

When it comes to data structure, searching plays a vital role in retrieving specific information efficiently. There are several types of searching algorithms that are commonly used in data structure operations. In this article, we will explore some of the most widely used searching techniques and understand their advantages and disadvantages.

Linear Search

Linear search is the simplest and most basic type of searching algorithm. It works by sequentially checking each element of the data structure until a match is found or the end of the structure is reached.

This searching technique is commonly used when the data is unsorted or when there is no specific order to follow while performing the search. However, linear search has a time complexity of O(n), which means that as the size of the data increases, the time taken to perform the search also increases linearly.

Binary Search

Binary search is a more efficient searching algorithm compared to linear search. However, it requires the data to be sorted in ascending or descending order before performing the search.

  • Bold text: Binary search works by repeatedly dividing the sorted list into halves and comparing the Target element with the middle element.

    Based on this comparison, it eliminates half of the remaining elements in each iteration until a match is found.

  • Underlined text: The key advantage of binary search is its time complexity, which is O(log n). This means that as the size of the data increases, binary search performs significantly better than linear search.


Hashing is another popular searching technique that uses a hash table for efficient retrieval of information. In this method, a hash function converts each item into an address or index where it can be stored or retrieved from.

Hashing offers constant time complexity for searching, which is O(1), making it extremely efficient. However, collisions can occur when two items are mapped to the same hash value, and resolving these collisions can affect the overall performance of the algorithm.

Interpolation Search

Interpolation search is an improvement over binary search for uniformly distributed and sorted data. It works by estimating the position of the Target element based on the values of the first and last elements in the structure.

  • Bold text: Interpolation search calculates a probable position using interpolation formula and then performs a comparison to refine its estimation in each iteration until a match is found.
  • Underlined text: The advantage of interpolation search is that it adapts to the distribution of data, resulting in improved performance compared to binary search for large datasets with non-uniformly distributed values.


In conclusion, there are several types of searching algorithms in data structure with varying complexities and advantages. Linear search is simple but inefficient for large datasets, whereas binary search offers improved performance but requires pre-sorted data.

Hashing provides constant-time complexity but may encounter collisions. Interpolation search adapts well to non-uniformly distributed data. Choosing the right searching technique depends on factors such as data size, distribution, and sorting requirements.

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