Hash Table Questions at PhonePe: What to Expect

PhonePe’s technical interviews heavily feature hash tables because they are fundamental to building scalable, real-time financial systems. With 24 out of 102 total tagged problems, hash table questions are not just common—they are critical. PhonePe handles massive transaction volumes, requiring data structures that offer fast lookups, deduplication, and efficient caching. Hash tables provide average O(1) time complexity for these operations, making them indispensable for features like user session management, fraud detection (duplicate transaction checks), and wallet balance indexing. If you can’t use a hash map effectively, you’ll struggle with PhonePe’s performance-focused problems.

What to Expect — Types of Problems

You will encounter hash tables both as a primary solution and as an optimization tool. Expect these categories:

1. Direct Mapping Problems: Questions where you use a hash map to store counts or indices. Examples include finding two numbers that sum to a target, checking for duplicates, or implementing a basic LRU Cache (which combines a hash map and a doubly linked list).
2. Frequency Analysis: A dominant pattern at PhonePe. You’ll be given arrays or strings and asked to find the most frequent element, anagrams, or characters meeting specific count criteria. These test your ability to use a hash map as a frequency counter.
3. Lookup Optimization: Problems where a naive nested loop solution is O(n²). The hash table is used to cache previously seen values, reducing the time complexity to O(n). This is common in array and substring problems.
4. System Design Components: While not a pure algorithm question, you may be asked to explain how you’d use a hash table in a real PhonePe feature, like storing merchant IDs for quick retrieval or managing distributed session data.

How to Prepare — Study Tips with One Code Example

Master the frequency counter pattern. It’s the single most applicable technique. Start by solving the classic “Two Sum” problem, then immediately move to anagram and top K frequent element problems. Always articulate the time and space complexity trade-off of using the hash table.

For example, a common PhonePe-style question is: Given an array of transaction IDs, find the first unique transaction (the first ID that appears exactly once). A brute-force check for each element would be O(n²). The optimal solution uses a hash map to count frequencies in one pass, then a second pass to find the first count of 1.

def first_unique_transaction(transactions):
    freq = {}
    for txn in transactions:
        freq[txn] = freq.get(txn, 0) + 1
    for txn in transactions:
        if freq[txn] == 1:
            return txn
    return None  # or a sentinel value

Recommended Practice Order

Tackle problems in this sequence to build competence systematically:

1. Fundamentals: Two Sum, Valid Anagram.
2. Frequency Patterns: Top K Frequent Elements, Group Anagrams, First Unique Character in a String.
3. Advanced Mapping: LRU Cache (implement from scratch), Subarray Sum Equals K.
4. PhonePe Specific: Solve all 24 hash table problems tagged for PhonePe on CodeJeet. Focus on the problems with the highest frequency reports.

Practice Hash Table at PhonePe