The core logic took me a while to structure cleanly.
Start by clarifying the data model and business rules: how delivery records, paid IDs, and surge windows are structured, and how surge multipliers apply (e.g., per delivery or per time window). Then outline an algorithm that filters unpaid deliveries, computes each delivery's payout using rate and surge, sums the total in cents, and collects the IDs to mark as paid. Emphasize efficiency (e.g., O(n log n) or O(n)) and correctness with edge cases.
Pro tip: Mention that you would use integer arithmetic (cents) to avoid floating-point errors, and that you'd confirm whether surge windows are inclusive/exclusive and how overlapping windows are handled—these details often trip up candidates.
Ask about the format of delivery records (e.g., ID, timestamp, base rate, duration), paid IDs (set or list), and surge windows (start/end time, multiplier). Confirm how surge applies: per delivery based on its start time, or prorated over its duration.
Use a hash set of paid IDs for O(1) lookups to efficiently exclude already-paid deliveries from further processing.
For each unpaid delivery, determine the applicable surge multiplier by checking which surge window(s) contain its time (handle overlaps per business rules). Multiply the base rate by the multiplier, ensuring integer arithmetic in cents.
Sum the payouts for all unpaid deliveries to get the total payout in cents, and collect their IDs into a list to return as the deliveries to mark as paid.
Consider edge cases: no unpaid deliveries, no surge windows, overlapping surge windows, deliveries spanning multiple windows, and large input sizes. Optimize by sorting surge windows and using binary search if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining that idempotency ensures a payout is processed exactly once even if the request is retried due to timeouts. Propose using a unique idempotency key per payout request, stored server-side with the operation's state, so retries can detect and return the original result. Then discuss implementation details like key generation, storage, and handling concurrent requests.
Pro tip: Emphasize that idempotency must be enforced on the server side, not just the client, and that the idempotency key should be tied to the business operation (e.g., payout ID) rather than the HTTP request. Also mention the importance of setting an appropriate expiration for idempotency records to avoid unbounded storage growth.
Generate a unique key for each payout operation, such as a client-generated UUID or a deterministic hash of the payout details. This key must be sent with every retry of the same operation.
Use a database or distributed cache to store the idempotency key along with the operation's status (e.g., pending, succeeded, failed) and the response. Ensure the write is atomic to handle concurrent requests.
On each request, first check if the idempotency key exists. If it does, return the stored response (or appropriate status) without re-executing the payout. If not, proceed but mark as pending.
If the payment API times out, the operation remains pending. On retry, the system sees the pending state and can either wait, retry the payment API with the same idempotency key (if supported), or return a conflict.
Set a TTL for idempotency records to prevent indefinite storage. After the TTL, the same key could be reused, but ensure it's long enough to cover retry windows.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said merge or stack depending on business rules, then asked which behavior they wanted.
Clarify that 'surge windows' are intervals with start and end times, and overlapping means finding intersections or merging them. Then propose an algorithmic solution like sorting by start time and merging intervals, while discussing trade-offs such as time/space complexity and real-time constraints.
Pro tip: Mention that in production systems like DoorDash, surge windows might be dynamic and require efficient updates, so consider data structures like interval trees or segment trees for frequent queries. Also, discuss how to handle edge cases like zero-length windows or adjacent intervals.
Ask questions to understand what 'handle' means: detect overlaps, merge them, or compute something like total surge duration? Confirm input format and constraints.
Propose sorting intervals by start time and then merging overlapping ones in a single pass. Explain why this is efficient (O(n log n) time).
Compare with alternative approaches like using an interval tree for dynamic updates, and discuss time/space complexity, scalability, and real-time requirements.
Mention edge cases: intervals that touch at endpoints, zero-length intervals, large input sizes, and concurrency if updates happen in real-time.
Relate to DoorDash: surge windows might represent peak delivery times; overlapping windows could mean higher demand, so merging helps in resource allocation or pricing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem: paying based on the union of active delivery time means we need to merge overlapping time intervals across deliveries and sum the total unique time. Then, outline an algorithm that collects all active intervals, sorts them, and merges overlaps to compute the union length, contrasting it with the independent sum approach.
Pro tip: Mention that this is essentially the classic 'merge intervals' problem, and highlight the importance of handling edge cases like zero-length intervals and timezone consistency, which shows attention to real-world data issues.
Confirm that 'active delivery time' refers to periods when the driver is actively working on a delivery, and that the union means non-overlapping total time. Ask about data granularity (e.g., timestamps) and whether intervals can be adjacent or overlapping.
Represent each delivery as a time interval [start, end]. The goal is to compute the total length of the union of these intervals, which may overlap if the driver handles multiple deliveries simultaneously.
Sort intervals by start time, then iterate and merge overlapping intervals by updating the end time to the maximum of current end and next end. Accumulate the length of merged intervals.
The algorithm runs in O(n log n) due to sorting, with O(n) space for merged intervals. In contrast, independent computation is O(n) but overcounts overlapping time, so the union approach is more accurate but slightly more complex.
Address zero-length intervals, intervals that touch but don't overlap, timezone normalization, and potential need for real-time or streaming updates if data arrives continuously.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.