Binary Search Questions at Swiggy: What to Expect

Binary Search isn't just about finding an element in a sorted array. At Swiggy, a company built on logistics, delivery time optimization, and inventory management, it's a fundamental tool for solving efficiency problems at scale. With 4 out of 41 of their technical interview questions tagged with Binary Search, they clearly value candidates who can apply this O(log n) paradigm to real-world constraints, like determining the minimum delivery capacity for riders or the optimal radius for a restaurant search.

What to Expect — Types of Problems

You won't see textbook "find 5 in this array" questions. Swiggy's problems typically involve applying the Binary Search pattern to an abstracted scenario. Expect these two main types:

1. Search in a Sorted Structure: This is the classic application, but often disguised. You might search for a specific delivery ID in a sorted log, find a restaurant's rating in a sorted list, or locate a geographic point in partitioned delivery zones. The data structure might be an array, a matrix sorted row and column-wise, or a stream of ordered events.

2. Binary Search on Answer (or "Search Space"): This is the most critical and frequent pattern for Swiggy interviews. Here, you use Binary Search to find the optimal value of something, not to find an existing element. The problem will describe a constraint (e.g., "deliver all orders in N hours"), and you must find the minimum or maximum feasible solution (e.g., "minimum speed for delivery executives"). You define a search space (e.g., possible speeds from 1 to max order distance), write a helper function to check if a given mid-value is feasible, and binary search to find the optimal point.

How to Prepare — Study Tips with One Code Example

Master the pattern, not just the algorithm. Internalize this three-step framework for "Binary Search on Answer" problems:

1. Identify the Search Space: What is the range of possible answers? Define low and high.
2. Create a Feasibility Function (canDo(mid)): This is the core logic. Given a candidate answer mid, determine if it's possible to achieve the goal under the given constraints.
3. Apply Standard Binary Search: Narrow the search space based on the feasibility function's result. Decide if you're finding the minimum feasible value or the maximum.

Consider this classic problem that mirrors delivery optimization: "Find the minimum capacity for a delivery vehicle to ship all packages within D days."

def min_capacity(weights, days):
    def can_ship(capacity):
        current_load = 0
        days_needed = 1
        for w in weights:
            if current_load + w > capacity:
                days_needed += 1
                current_load = 0
            current_load += w
        return days_needed <= days

    low, high = max(weights), sum(weights)
    while low < high:
        mid = (low + high) // 2
        if can_ship(mid):
            high = mid  # Try for a smaller capacity
        else:
            low = mid + 1  # Need more capacity
    return low

Recommended Practice Order

Build your skill progressively:

1. Foundation: Standard Binary Search (first occurrence, last occurrence).
2. Rotation & Search: Search in a rotated sorted array.
3. 2D Application: Search in a row-column sorted matrix.
4. Core Pattern: "Binary Search on Answer" problems (capacity to ship, Koko eating bananas, split array largest sum).
5. Swiggy-Specific: Practice problems directly from their question bank to acclimate to their phrasing and constraints.

Practice Binary Search at Swiggy