Hash Table Questions at DoorDash: What to Expect

Hash tables are the most frequently tested data structure at DoorDash, appearing in 20 of their 87 total coding questions. This isn't surprising. DoorDash's core business—matching drivers, restaurants, and customers in real-time—is fundamentally about fast lookups, grouping related data, and detecting duplicates or conflicts. Whether it's tracking order statuses, managing delivery windows, or counting user actions, the O(1) average-time complexity for insertions and lookups is critical for scaling their platform. Mastering hash tables is non-negotiable for passing their technical screen.

What to Expect — Types of Problems

DoorDash uses hash tables to test your ability to model real-world data relationships efficiently. Expect problems in these categories:

1. Frequency Counting & Aggregation: The most common pattern. You'll be asked to count occurrences of items (e.g., tracking the most common delivery zones or menu items ordered). Problems often combine counting with finding max/min values or filtering by threshold.
2. Mapping & Lookup: Using a hash map to store a direct relationship for instant access. This is used for problems like two-sum variants, checking for complementary delivery times, or storing cached results (like restaurant ratings) to avoid redundant computation.
3. Deduplication & Set Operations: Using hash sets to track seen items, find unique elements, or detect cycles in data streams (e.g., identifying duplicate driver assignments or fraudulent transaction patterns).
4. Prefix Sum with Hashing: A more advanced pattern where you track running totals (prefix sums) in a hash map to solve subarray problems in O(n) time, such as finding delivery routes that sum to a specific time or distance.

The problems are rarely just about implementing a hash table. They test if you can recognize when a hash table is the optimal tool to reduce time complexity, often from O(n²) to O(n).

How to Prepare — Study Tips with One Code Example

Focus on pattern recognition. When you read a problem, ask: "Do I need instant access to a value based on a key?" or "Do I need to know if I've seen this element before?" Practice translating the problem's nouns (e.g., "user IDs," "order times") into hash map keys and their associated data (counts, indices, or complementary values) into the map's values.

A key pattern is using a hash map to store a needed complement. Here is a classic "Two Sum" implementation, a fundamental building block for many DoorDash problems.

def two_sum(nums, target):
    seen = {}  # value -> index
    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 competency progressively:

1. Start with fundamental operations: counting frequencies and using hash sets for deduplication.
2. Master the Two Sum pattern and its variants.
3. Practice grouping problems (e.g., grouping orders by customer or status).
4. Tackle prefix sum with hash map problems, which often appear in more complex array challenges.
5. Finally, solve DoorDash's tagged hash table questions to see how they combine these patterns in a domain-specific context.

Practice Hash Table at DoorDash