The Interview Edge: Top 10 Algorithms You Must Know for Tech Interviews
Ayan Sarkar
Engineering Leader | Sr. Director @ DigitalOcean | Building Scalable AI/ML Systems & Teams | Ex-Amazon | Writing at the Edges of Tech, Leadership & Self | Always in Beta
Introduction
Technical interviews for mid-to-senior engineers are not just about solving problems but about demonstrating a structured thought process, optimization techniques, and trade-off analysis. Interviewers evaluate how well candidates understand and apply algorithms in real-world scenarios.
This article expands on 10 essential algorithms that engineers must master for interviews and practical system design. Beyond implementation, we’ll explore why they matter, where they’re used, when not to use them, and how they impact real-world systems. Additionally, we will emphasize the understanding of time complexity and the importance of estimating orders of magnitude in time, volume, traffic, and storage while applying these algorithms.
Understanding Time Complexity & System Constraints
Every algorithm has a performance profile. Understanding Big O notation helps assess whether a solution scales with increasing input size. Before selecting an algorithm, consider:
Estimating the order of magnitude (e.g., 10³, 10⁶, 10⁹) of data volume, traffic, and storage is essential to choosing the right tool for the job.
Technical interviews for mid-to-senior engineers are not just about solving problems but about demonstrating a structured thought process, optimization techniques, and trade-off analysis. Interviewers evaluate how well candidates understand and apply algorithms in real-world scenarios.
This article expands on 10 essential algorithms that engineers must master for interviews and practical system design. Beyond implementation, we’ll explore why they matter, where they’re used, when not to use them, and how they impact real-world systems. Additionally, we will emphasize the understanding of time complexity and the importance of estimating orders of magnitude in time, volume, traffic, and storage while applying these algorithms.
1. Binary Search – When Less is More
Concept: Efficiently search for an element in sorted data by halving the search space in each step (O(log n)).
Common Applications:
Real-World Example: Search engines use binary search on ranked web pages for fast pagination, ensuring relevant results appear quickly.
When Not to Use: If the data is unsorted or frequently changing, sorting before searching may be inefficient.
Key Thought Process: Can I sort the data? Can I reduce my search space at each step?
2. Two Pointers – Optimizing Space and Speed
Concept: Uses two indices to traverse an array efficiently, often reducing space complexity (O(n)).
Common Applications:
Real-World Example: Video streaming platforms optimize buffering mechanisms using two pointers to preload content seamlessly.
When Not to Use: If multiple passes are needed or data isn’t structured sequentially.
Key Thought Process: Can I track two elements simultaneously to avoid extra memory usage?
3. Hashing – Constant Time Lookups
Concept: Uses hash functions to store and retrieve data in constant time (O(1) on average).
Common Applications:
Real-World Example: Modern databases use hash maps to speed up queries by indexing frequently accessed records.
When Not to Use: Hashing can lead to collisions and high memory usage when dealing with very large datasets.
Key Thought Process: Can I trade space for time to eliminate unnecessary loops?
4. Sorting Algorithms – Choosing the Right One
Concept: Arranges data efficiently, with different algorithms optimized for different use cases.
Common Sorting Methods:
Real-World Example: E-commerce platforms use bucket sort for filtering product prices efficiently.
When Not to Use: Sorting is unnecessary when insertion order matters or when dealing with dynamic streams of data.
Key Thought Process: Do I need a stable sort? Is the data nearly sorted?
5. Graph Algorithms – Navigating Networks
Concept: Graph traversal techniques for handling interconnected data structures.
Common Applications:
Real-World Example: Google Maps uses Dijkstra’s Algorithm for finding the shortest path between locations.
When Not to Use: When dealing with simple linear relationships or non-networked data.
Key Thought Process: Is my data a network? Do I need shortest path or connectivity analysis?
6. Dynamic Programming – Breaking Problems into Subproblems
Concept: Solves complex problems by storing and reusing solutions to overlapping subproblems.
Common Applications:
Real-World Example: Flight booking systems use dynamic programming to find the best ticket pricing strategiesbased on historical trends.
When Not to Use: When problems don’t have overlapping subproblems or independent computations.
Key Thought Process: Can I store and reuse results instead of recalculating?
7. Bit Manipulation – Speeding Up Computation
Concept: Uses bitwise operations to optimize performance.
Common Applications:
Real-World Example: Cryptographic protocols rely on XOR operations for fast encryption.
When Not to Use: When clarity is more important than raw performance.
Key Thought Process: Can I replace loops with bitwise operations for efficiency?
8. Union-Find – Efficiently Managing Connections
Concept: Tracks elements in a set and efficiently merges connected components.
Common Applications:
Real-World Example: Social media platforms use union-find to group mutual friends and suggest connections.
When Not to Use: When frequent modifications or real-time updates are needed.
Key Thought Process: Do I need to dynamically merge and track connected components?
9. Trie – Fast Prefix-Based Search
Concept: A tree-based data structure for efficient string searches.
Common Applications:
Real-World Example: Search engines use tries for instant query suggestions as users type.
When Not to Use: When memory efficiency is a priority since tries can consume a lot of space.
Key Thought Process: Can I optimize lookups using a prefix-based structure?
10. Reservoir Sampling – Handling Large Data Streams
Concept: Enables random sampling from a large or infinite dataset with limited memory.
Common Applications:
Real-World Example: Online analytics tools use reservoir sampling to generate random samples from large data streams without loading everything into memory.
When Not to Use: When the entire dataset can fit into memory and simple random sampling suffices.
Key Thought Process: Can I process large data efficiently without extra storage?
Final Thoughts
Understanding these algorithms is not just about coding solutions but about knowing when and why to use them. Strong candidates demonstrate:
To reinforce these concepts, try implementing each algorithm and solving problems on platforms like LeetCode, Codeforces, or HackerRank. Practicing real-world applications will solidify your understanding and prepare you for high-stakes interviews.
This is the second article in The Interview Edge series. Up next: How to Ace System Design Interviews—covering frameworks, common pitfalls, and case studies from real-world architectures.
Which of these algorithms have you used in your work? Share your thoughts in the comments!
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Director of Engineering
4moThanks for sharing, Ayan