← Instacart Interview Insights
Spent too long second-guessing whether to use a stable sort or just partition.
Clarify the problem constraints (e.g., input format, priority definition, capacity) and then propose an efficient solution using two queues to separate priority and non-priority riders. Simulate boarding by dequeuing from the priority queue first, then the non-priority queue, until the bus is full, ensuring relative order is preserved within each group.
Pro tip: Mention that this is essentially a stable partition problem and that using two queues gives O(n) time and O(n) space; also discuss edge cases like empty line, zero capacity, or all priority riders.
Ask questions to confirm input format (e.g., list of riders with priority flag), bus capacity, and expected output (boarded and waiting lists). Ensure understanding of 'priority' and 'relative arrival order'.
Select two queues (or lists) to maintain the arrival order of priority and non-priority riders separately. This allows O(1) enqueue and dequeue operations.
Iterate through the waiting line, enqueue each rider into the appropriate queue. Then, while the bus has capacity, dequeue from the priority queue first; if empty, dequeue from the non-priority queue.
After boarding, any riders left in either queue remain in the waiting line. Combine them in the correct order (priority first, then non-priority) to return the waiting list.
State time complexity O(n) and space O(n). Discuss edge cases: empty line, capacity 0, capacity >= total riders, and all riders being priority or non-priority.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The alternative I gave was a priority queue keyed on (isPriority, arrivalIndex) which gets you O(n log n) vs my O(n) partition pass.
Start by clearly stating the problem your boarding implementation solves and the key constraints (e.g., real-time updates, scale). Then compare your approach with an alternative, focusing on time/space complexity and practical tradeoffs. Finally, discuss production hardening: monitoring, failure modes, and scalability improvements.
Pro tip: Tie your tradeoffs to Instacart's business metrics (e.g., delivery times, shopper efficiency) to show you think beyond code. Also, acknowledge any assumptions you made and how you'd validate them with data.
Briefly explain what 'boarding' means in your context (e.g., assigning shoppers to orders) and the key requirements: real-time, scale, fairness, etc.
Outline your approach, its time/space complexity, and the tradeoffs you made (e.g., simplicity vs. optimality, latency vs. accuracy).
Introduce a different algorithm or design (e.g., greedy vs. matching, batch vs. streaming) and analyze its complexity and tradeoffs.
Directly compare the two on time/space complexity, scalability, and suitability for different scenarios.
Discuss what you'd change for production: monitoring, fault tolerance, scalability, and how you'd measure success.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I listed: empty queue, queue with only priority riders, only non-priority, exact capacity fit, one over capacity, and the case where a priority rider at the back of the line still boards before a non-priority rider at the front.
Start by clarifying the boarding logic's requirements and inputs/outputs, then systematically identify edge cases across input validation, boundary conditions, and state transitions. Structure your answer by grouping edge cases into categories and explaining how you would test each with specific unit test examples.
Pro tip: Demonstrate test-driven development mindset by mentioning that you would write tests before implementation to clarify requirements, and use parameterized tests to cover multiple edge cases efficiently.
Ask questions to understand the exact rules, inputs, and expected outputs of the boarding logic. For example, what defines a valid boarding pass, how are passengers prioritized, and what are the constraints?
Brainstorm edge cases in categories such as input validation (null, empty, invalid formats), boundary conditions (first/last passenger, capacity limits), and state transitions (boarding order, group changes).
Rank edge cases by likelihood and impact, focusing on those that could cause critical failures or security issues. Consider both common and rare scenarios.
For each prioritized edge case, outline a specific unit test: setup, input, expected output, and assertions. Mention using mocks or stubs for dependencies like databases or external services.
Explain how you would measure coverage (e.g., branch coverage) and which testing frameworks (e.g., JUnit, pytest) you would use. Mention the importance of fast, isolated tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the existing solution's data structures and how capacity and priority are currently handled. Then, extend the model to track wheelchair users separately, enforcing the max-2 constraint and variable capacity consumption, while preserving priority and stable ordering. Finally, discuss algorithmic adjustments, complexity, and edge cases.
Pro tip: Emphasize that wheelchair capacity is a separate resource with its own limit, and that stable ordering must be maintained even when wheelchair users are prioritized. This shows you understand multi-dimensional constraints.
Ask about the existing solution's interface, how capacity is measured, and what 'priority boarding' and 'stable ordering' mean in this context. Confirm that wheelchair users consume variable capacity units and that at most 2 can be accommodated.
Represent each rider with attributes: isWheelchair, capacityUnits, priority, and arrival order. Maintain separate counts for wheelchair users and total capacity used.
Modify the selection logic to first consider priority, then stable order, while ensuring wheelchair count ≤ 2 and total capacity ≤ limit. Use a greedy approach or dynamic programming if needed.
Discuss time and space complexity of the extended solution. Compare with alternatives (e.g., sorting vs. priority queue) and justify choices based on constraints.
Consider scenarios like more than 2 wheelchair users, wheelchair users with high capacity consumption, and ties in priority. Verify stable ordering is preserved.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.