Heap (Priority Queue) Questions at DoorDash: What to Expect

Heap (Priority Queue) questions are a significant part of DoorDash's technical interview, comprising about 14% of their problem pool. This focus is not accidental. DoorDash's core business—matching drivers, orders, and restaurants in real-time—is fundamentally about dynamic ordering and optimization. The heap data structure is the perfect tool for these scenarios, as it efficiently manages a constantly changing set of elements where you repeatedly need to access the "most important" one, whether that's the nearest driver, the highest priority order, or the most efficient delivery route. Mastering heaps demonstrates you can think about the scalable, performance-critical systems that power on-demand logistics.

What to Expect — Types of Problems

You will typically encounter heap problems embedded within larger, practical scenarios. The common patterns include:

• Scheduling and Batching: Problems involving assigning tasks based on time, priority, or location. Think "merge k sorted lists" of delivery times or "meeting rooms II" for driver shift scheduling.
• Top K Elements: A classic pattern. You might be asked to find the top K highest-rated restaurants in an area, the K most frequent order items, or the K slowest delivery zones from a live data stream.
• Two Heaps for Medians: Maintaining a running median is useful for metrics and analytics, such as tracking the median delivery time or order value over a rolling window.
• Pathfinding Enhancements: While Dijkstra's algorithm (which uses a min-heap) is less common in pure form, understanding how heaps optimize graph traversal is valuable for complex routing questions.

The problems often involve sorting through dynamic data or merging multiple streams of information, mirroring the real-time decision-making in their platform.

How to Prepare — Study Tips

1. Internalize the Core Operations: Know that insertion and popping the root (O(log n)) are the key operations. Peeking at the root is O(1). Understand that in most languages, a "priority queue" is implemented as a heap.
2. Pattern Recognition: Don't just memorize problems. When you see keywords like "k largest/smallest," "closest," "frequent," "median," or "merge k sorted," immediately consider if a heap is the right tool.
3. Practice Implementation: While you'll use built-in libraries in the interview, knowing how to implement a heapify function or a heap from scratch solidifies your understanding.
4. Master the Two-Heap Pattern: This is crucial for median problems. Maintain a max-heap for the lower half of numbers and a min-heap for the upper half, balancing them to find the median in constant time.

Here is a fundamental code example for the Top K Frequent Elements pattern, a highly relevant DoorDash problem type (e.g., find top K ordered menu items).

import heapq
from collections import Counter

def topKFrequent(nums, k):
    count = Counter(nums)
    # Use a min-heap of size k to store (frequency, element)
    heap = []
    for num, freq in count.items():
        heapq.heappush(heap, (freq, num))
        if len(heap) > k:
            heapq.heappop(heap) # Remove the least frequent
    # Extract elements from heap
    return [num for _, num in heap]

Recommended Practice Order

Build your competency progressively:

1. Start with basic heap operations: Kth Largest Element in a Stream.
2. Move to core patterns: Top K Frequent Elements, Merge K Sorted Lists.
3. Tackle the two-heap pattern: Find Median from Data Stream.
4. Finally, solve DoorDash-specific problems that layer heaps onto scheduling or simulation scenarios.

Practice Heap (Priority Queue) at DoorDash