Hash Table Questions at eBay: What to Expect

Hash Tables are the most frequently tested data structure at eBay, appearing in 25% of their technical interview questions (15 out of 60). This focus is practical: eBay's core systems—managing user sessions, product catalogs, real-time bids, and recommendation engines—rely heavily on fast key-value lookups. Understanding hash tables demonstrates you can design efficient, scalable solutions for problems involving data indexing, deduplication, and frequency analysis, which are daily realities in their platform's operations.

What to Expect — Types of Problems

You will encounter problems that test both your knowledge of the data structure itself and your ability to apply it as a tool. Expect questions in these categories:

1. Direct Application: Classic problems where a hash table (dictionary, map, or set) is the obvious and optimal solution. Examples include finding duplicates, checking for anagrams, or implementing a cache (LRU Cache is a frequent advanced question).
2. Frequency Counting: A dominant pattern at eBay. You'll use a hash table to count occurrences of elements (e.g., items viewed, user actions, product IDs) to find majorities, top K items, or unique counts.
3. Complement Searching: Problems where you check if the complement of the current element (like target - current_value) exists in a previously seen set. This is central to problems like Two Sum, which tests the ability to trade space for time.
4. System Design Components: While not a full system design interview, you may be asked how you'd use hash tables to design a specific feature, like a real-time "items you've viewed" list or a session store.

How to Prepare — Study Tips with Code Example

Move beyond theory. Practice by identifying the hash table pattern within the first minute of reading a problem. Ask: "Is this about tracking existence, counting frequency, or storing a computed state for later?"

Drill the frequency counting and complement search patterns until they are automatic. For example, the classic Two Sum problem perfectly illustrates using a hash map to store seen numbers for O(1) lookups, transforming an O(n²) brute-force solution into O(n).

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

Build competence progressively:

1. Fundamentals: Two Sum, Contains Duplicate, Valid Anagram.
2. Frequency Patterns: Top K Frequent Elements, First Unique Character.
3. Advanced Patterns: Group Anagrams (hashing a key), LRU Cache (combining hash map and linked list).
4. eBay Context: Practice problems related to user behavior analytics (e.g., finding frequent item pairs) and real-time data lookups.

Focus on writing clean, correct code under time pressure. Verbalize your trade-off analysis (time vs. space) as you would in the interview.

Practice Hash Table at eBay