When studying Data Structures and Algorithms (DSA), searching stands out as one of the core tasks. This means finding one specific thing inside a larger set of data. You might look for a product in an inventory, a word in a document, or a certain record in a database. Searching algorithms ensure that this process works and .
This article dives into the key searching algorithms included in DSA, their categories, the time it takes to execute them, and how they get used in real-life situations. To improve programming skills or prepare to tackle coding interviews, grasping these concepts is essential.
What does a searching algorithm mean?
A searching algorithm helps locate a particular value within a data structure such as an array linked list, tree, or graph. It returns the location of the item you want if it is there. If the value is not there, the algorithm provides a failure response like -1.
Types of Searching Algorithms
You can divide searching algorithms into these main groups:
- Linear Search
- Binary Search
- Hash-based Search
- Tree-based Search
- Interpolation and Exponential Search (Advanced)
Let’s understand each of them in detail.
1. Linear Search (Sequential Search)
Overview:
Linear Search is the simplest algorithm, where we traverse the list from start to end, comparing each element with the target.
Time Complexity:
- Best Case: O(1)
- Average/Worst Case: O(n)
Use Case:
- Unsorted arrays or linked lists.
- When the dataset is small.
Example in Python:
def linear_search(arr, target):
for i in range(len(arr)):
if arr[i] == target:
return i
return -1
2. Binary Search
Overview:
Binary Search is a highly efficient algorithm but only works on sorted data structures.It keeps cutting the search area into two equal parts.
Time Complexity:
- Best Case: O(1)
- Average/Worst Case: O(log n)
Use Case:
- Large, sorted arrays or lists.
- Searching in sorted databases, dictionaries.
Example in Python:
def binary_search(arr, target):
low, high = 0, len(arr) – 1
while low <= high:
mid = (low + high) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
low = mid + 1
else:
high = mid – 1
return -1
3. Hash-Based Search
Overview:
In a hash-based search, we use hash tables to store key-value pairs, allowing for near-constant time complexity on average.
Time Complexity:
- Best/Average Case: O(1)
- Worst Case: O(n) (in case of collisions)
Use Case:
- Fast lookup operations (e.g., dictionaries, caches, symbol tables).
Example in Python:
hash_table = {“apple”: 1, “banana”: 2, “cherry”: 3}
print(hash_table.get(“banana”, -1)) # Output: 2
4. Tree-Based Search (Binary Search Tree)
Overview:
In Binary Search Trees, each node links to both a left child and a right child. The left child holds a value smaller than its parent, while the right one holds a larger value.
Time Complexity:
- Average Case: O(log n)
- Worst Case: O(n) (for unbalanced trees)
Use Case:
- Databases, file systems, routers.
5. Interpolation Search (Advanced)
Overview:
A better method than binary search to handle sorted data that is distributed. It guesses where the target might be based on the values of the endpoints.
Time Complexity:
- Best Case: O(1)
- Average Case: O(log log n)
- Worst Case: O(n)
Use Case:
- Searching in uniformly distributed datasets like zip codes or numerical IDs.
Comparison of Searching Algorithms
| Algorithm | Best Case | Average Case | Worst Case | Works On | Space Complexity |
| Linear Search | O(1) | O(n) | O(n) | Unsorted data | O(1) |
| Binary Search | O(1) | O(log n) | O(log n) | Sorted array | O(1) |
| Hash Search | O(1) | O(1) | O(n) | Key-value pairs | O(n) |
| Tree Search (BST) | O(log n) | O(log n) | O(n) | Tree structure | O(n) |
| Interpolation | O(1) | O(log log n) | O(n) | Sorted, uniform | O(1) |
Factors to Consider When Choosing a Searching Algorithm
- Size of the dataset
- Whether the data is sorted
- Time complexity requirements
- Space availability
- Frequency of search operations
Picking the best algorithm comes down to what your app requires. Binary search works well when handling big sorted datasets. Hash tables are useful when you need quick and repeated lookups.
Applications of Searching Algorithms
- Search Engines: Keyword lookup and index retrieval.
- Databases: Query processing, record lookup.
- E-commerce Platforms: Product search, filtering.
- Operating Systems: File search, memory management.
- Artificial Intelligence: Pathfinding algorithms (e.g., A* search).
Conclusion
Learning searching algorithms in DSA forms the base to grow as a software developer or a computer science student. From simple linear searches to advanced hash-based methods and tree exploration, these algorithms fit various data types and situations.
Whether you’re working on backend systems preparing for coding interviews, or developing intricate apps, a good understanding of these searching methods can be a valuable skill. Keep practicing, study how time complexities work, and try using these ideas to solve practical problems to get better at thinking about algorithms.
Launch Your Data Science Career with Sharpener’s Pay After Placement Program
At Sharpener, we help students and professionals become job-ready data scientists and analysts in just 90 days—and you don’t pay a rupee until you land a job!
Why Choose Sharpener?
✅ Learn from industry experts
✅ 1:1 mentorship and interview preparation
✅ Hands-on, project-based training
✅ Pay After Placement – No upfront costs
Whether you’re just starting out or looking to shift into a data-driven role, Sharpener’s Pay After Placement Data Science & Analytics Course is your fastest track to a rewarding career.
Sharpenerian’s work at the best companies!
Do you want to know more? Stay tuned for our next post on sorting algorithms in DSA and how they complement search techniques for optimal performance.
