Minimize batches and allocate riders by time
Company: DoorDash
Role: Machine Learning Engineer
Category: Coding & Algorithms
Difficulty: medium
Interview Round: Technical Screen
## Coding prompts (same interview round)
### 1) Minimum number of batches for time points
You are given a list of event time points (integers), e.g. `[1, 3, 5, ...]`.
You need to group all events into **batches** under these constraints:
- Each batch can contain **at most `B`** events (capacity).
- Let `t0` be the **earliest time** in a batch. Then **every** event time `t` in that batch must satisfy:
- `t - t0 <= W` (i.e., all events lie within a window of size `W` starting at the earliest event in the batch).
**Goal:** return the **minimum number of batches** needed to include all events.
**Input:**
- `times`: array of integers (not guaranteed sorted)
- integers `B` (capacity), `W` (window)
**Output:**
- minimum number of batches
**Notes:**
- You may assume `B >= 1`, `W >= 0`.
- The implementation should be runnable and include self-chosen test cases.
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### 2) Follow-up: minimum number of riders (Meeting Rooms II variant)
In a delivery setting, each order occupies a rider for an interval of time `[start, end)` (or `[start, end]`—clarify your convention).
Given a list of order time intervals, compute the **minimum number of riders** needed so that no rider is assigned overlapping orders.
**Input:** list of intervals `(start, end)`
**Output:** minimum number of riders required
(Expect a sweep-line / min-heap style solution.)
Quick Answer: This question set evaluates algorithmic problem-solving in scheduling and resource allocation, assessing understanding of batching under capacity and time-window constraints and interval-overlap reasoning for concurrent resource assignment within the Coding & Algorithms domain, and emphasizes practical application of algorithmic techniques and complexity analysis rather than purely theoretical proof. These problems are commonly asked to measure a candidate's ability to model time-based constraints, reason about resource utilization and scalability, and design efficient approaches for real-world scheduling and allocation scenarios.
Part 1: Minimum Batches for Time Points
You are given a list of integer event times. Group all events into batches such that each batch contains at most B events, and if t0 is the earliest time in a batch, then every event t in that batch must satisfy t - t0 <= W. Return the minimum number of batches needed to include all events. The input list is not guaranteed to be sorted.
Constraints
- 0 <= len(times) <= 200000
- -10^9 <= times[i] <= 10^9
- 1 <= B <= 200000
- 0 <= W <= 10^9
Examples