Hash Table Questions at Myntra: What to Expect

Hash Table questions appear in nearly one-third of Myntra’s technical interviews (7 out of 24 total problems). This frequency reflects their critical role in Myntra’s core systems—handling millions of product SKUs, user sessions, shopping carts, and real-time inventory checks requires fast lookups, deduplication, and frequency counting. Mastering hash tables is non-negotiable for efficiently solving problems related to caching, data indexing, and distributed systems at this scale.

What to Expect — Types of Problems

Myntra’s hash table questions typically focus on real-world e-commerce scenarios. You can expect:

• Frequency Analysis: Counting product views, identifying top-selling items, or analyzing user behavior logs.
• Deduplication & Uniqueness: Managing unique visitor sessions, removing duplicate product IDs from recommendation lists, or ensuring unique coupon codes.
• Pair-Finding & Two-Sum Variants: Matching complementary products (e.g., “frequently bought together”), validating promotional bundles, or checking for inventory conflicts.
• Caching & Memoization: Simulating LRU caches for product detail pages or optimizing repeated calculations in pricing engines. Problems often combine hash tables with other concepts like arrays, strings, or sliding windows. The emphasis is on choosing the right data structure for optimal time complexity, usually O(1) for lookups or O(n) for single-pass solutions.

How to Prepare — Study Tips with One Code Example

Focus on understanding the core operations: insertion, lookup, and deletion. Practice using language-specific implementations: dict in Python, Map/Set in JavaScript, and HashMap/HashSet in Java. A key pattern is using a hash map to store previously seen elements to achieve O(n) time, avoiding nested loops.

A common example is the Two-Sum problem, where you must find two indices such that their numbers add to a target—a direct analog for finding product pairs that match a promotional price.

def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

Recommended Practice Order

1. Start with fundamentals: Two-Sum, First Repeating Character, and Valid Anagram.
2. Move to frequency counting: Top K Frequent Elements and Group Anagrams.
3. Tackle advanced patterns: Subarray Sum Equals K and LRU Cache implementation.
4. Finally, solve Myntra’s specific company-tagged hash table problems to understand their problem style and constraints.

Practice Hash Table at Myntra