When preparing for technical interviews, most candidates focus on classic data structures and algorithms: arrays, linked lists, binary trees, binary search, quicksort, dynamic programming, and the like. While these are undoubtedly essential, there’s a category of less celebrated—yet incredibly powerful—algorithms and data structures that are often overlooked. Mastering these can give you a substantial edge in interviews, especially when tackling complex or niche problems that stump other candidates.
In this post, we’ll delve into some underrated algorithms you should absolutely add to your toolbox before your next big interview. We’ll explore their use cases, how they work, and why they deserve your attention. So whether you’re preparing for FAANG or a top-tier startup, bookmark this list—you’ll thank yourself later.
1. Trie (Prefix Tree)
Why It’s Underrated
The Trie is one of those data structures that shows up often in competitive programming and systems design but is seldom seen in traditional curriculum or beginner-level tutorials. This tree-like structure is designed to store strings in a way that makes prefix lookups extremely efficient.
Use Cases:
- Autocomplete and spell-checking systems
- IP routing (longest prefix matching)
- Dictionary word validation
- Search engines (e.g., finding all words with a given prefix)
How It Works:
A Trie node typically contains an array (or hashmap) of child nodes, each representing a character. Starting from the root, words are inserted character by character. Each path from the root to a node represents a prefix.
Time Complexity:
- Insertion and search: O(m), where m is the length of the word.
Interview Tip:
If asked to implement an autocomplete feature or solve a problem involving prefix searches, pulling out a Trie will leave a strong impression.
2. Segment Tree
Why It’s Underrated
Segment Trees rarely make an appearance in most data structure courses but are incredibly powerful for range queries. If you’re dealing with problems that involve querying and updating ranges of an array, Segment Trees offer logarithmic performance.
Use Cases:
- Range sum, min, or max queries
- Range updates
- Interval queries in time-series data
- Image processing (e.g., cumulative histograms)
How It Works:
A Segment Tree is a binary tree where each node represents an interval or segment of an array. It allows you to perform both queries and updates in O(log n) time.
Time Complexity:
- Build: O(n)
- Query: O(log n)
- Update: O(log n)
Interview Tip:
If faced with a problem that includes phrases like “given an array, answer Q queries efficiently” or “perform updates in an array and still maintain range calculations,” a Segment Tree is likely the best fit.
3. Topological Sort
Why It’s Underrated
Topological Sorting often flies under the radar, especially among candidates who haven’t worked with DAGs (Directed Acyclic Graphs). It’s essential for scheduling problems and dependency resolution.
Use Cases:
- Task scheduling with dependencies
- Compilation order of source files
- Resolving package/module dependencies
- Determining course prerequisites
How It Works:
Topological sort is a linear ordering of vertices in a DAG such that for every directed edge u → v, vertex u comes before v in the ordering. This can be implemented via Depth-First Search (DFS) or using Kahn’s algorithm (a BFS-based approach).
Time Complexity:
- O(V + E), where V = vertices, E = edges
Interview Tip:
If you’re asked to resolve a dependency chain or schedule a series of tasks with prerequisites, bring up Topological Sort. It’s efficient, elegant, and often impresses interviewers.
4. Union-Find (Disjoint Set Union – DSU)
Why It’s Underrated
Although Union-Find is a staple in competitive programming, it doesn’t get the spotlight it deserves in interview prep. It’s brilliant for solving problems involving connected components.
Use Cases:
- Detecting cycles in undirected graphs
- Kruskal’s algorithm for MST
- Network connectivity
- Image segmentation
How It Works:
The DSU structure maintains a collection of disjoint sets. It supports two operations: Find (determining the set a particular element belongs to) and Union (merging two sets).
Optimizations:
- Path Compression
- Union by Rank/Size
Time Complexity:
- Nearly O(1) with path compression and union by rank
Interview Tip:
If asked about network or social graph problems where you need to track components, cycle detection, or friend groups, go for DSU. It’s fast and reliable.
5. Sliding Window Algorithm
Why It’s Underrated
Sliding window isn’t a distinct data structure but a technique that often gets overlooked. It is incredibly efficient for problems involving subarrays or substrings.
Use Cases:
- Maximum/minimum sum subarray of size k
- Longest substring without repeating characters
- Dynamic window problems (e.g., Netflix-style content buffering)
How It Works:
You maintain a window over a subset of your input and slide it forward as needed, adding new elements and removing old ones while maintaining the required condition.
Time Complexity:
- O(n), assuming constant-time update of window
Interview Tip:
If you find a brute-force solution with nested loops, think if you can apply sliding window to reduce complexity. It’s often the key to passing tight time constraints.
6. Binary Indexed Tree (Fenwick Tree)
Why It’s Underrated
The Binary Indexed Tree is like the Segment Tree’s lighter cousin—more space efficient and often easier to implement for prefix sum problems.
Use Cases:
- Prefix sums
- Inversion count
- Frequency tables in log-time
- 2D BITs for image processing or matrices
How It Works:
A BIT is an array that helps you compute prefix sums efficiently. Each element stores information about a range of the original array based on its binary representation.
Time Complexity:
- Build: O(n log n)
- Query/Update: O(log n)
Interview Tip:
When you want to perform frequent updates and queries on a numerical dataset and don’t need the full power of a Segment Tree, BIT might be your best option.
7. Rabin-Karp Algorithm
Why It’s Underrated
When candidates think of string matching, they often jump to brute force or the KMP algorithm. Rabin-Karp offers a clever twist using hashing.
Use Cases:
- Plagiarism detection
- Substring search
- Detecting repeated patterns
How It Works:
Rabin-Karp uses a rolling hash to efficiently find patterns in text. By comparing hash values instead of substrings directly, it speeds up matching significantly.
Time Complexity:
- Average: O(n + m)
- Worst: O(nm) (with hash collisions)
Interview Tip:
If the problem involves searching multiple patterns or large texts, bring up Rabin-Karp. It’s particularly efficient in practice with a good hash function.
8. Floyd-Warshall Algorithm
Why It’s Underrated
Floyd-Warshall doesn’t come up as often as Dijkstra’s algorithm, but it shines in specific use cases involving all-pairs shortest paths.
Use Cases:
- Road networks
- Game maps with teleporters
- Social network analysis (degrees of separation)
How It Works:
It uses dynamic programming to find shortest paths between all pairs of vertices in a weighted graph.
Time Complexity:
- O(V^3)
Interview Tip:
For small graphs (V < 200), it’s often the simplest and most effective method. It also handles negative weights (but not negative cycles).
Final Thoughts: Build a Competitive Edge
In interviews, going beyond the basics can significantly set you apart. Most candidates can code up a binary search or reverse a linked list, but few will bring up a Trie when asked to implement an autocomplete feature or explain how to use a Segment Tree to answer dynamic range queries.
By mastering these underrated algorithms, you’re not only preparing for the unexpected but also signaling to your interviewer that you have a deeper understanding of algorithmic problem-solving. This can often be the difference between a good interview and a great one.
So, as you gear up for your next coding challenge, don’t just drill the classics. Learn and implement these powerful tools. They might just be your secret weapon.
Now it’s your turn:
Which underrated algorithm are you going to learn next?
Stay sharp, keep coding, and happy interviewing!
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