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Instacart·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Apr 2026

Summary

Instacart coding screen with a pretty niche problem around temporal task tracking. Not your typical array or graph question, more of a data modeling puzzle with some simulation logic baked in.

Questions Asked (1)

Q1

Design and implement a function that, given a simulation timestamp and a user ID, returns the number of tasks assigned to that user which were active as of a specified historical point in time.

Algorithms & Data StructuresData ModelingSystem Design
Author's notes

The bi-temporal angle is what trips you up if you're not expecting it.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements first: define 'active' (e.g., task start and end timestamps), the input format (simulation timestamp, user ID, historical point), and output (count). Then propose a data model and algorithm, such as storing tasks with intervals and using a segment tree or sweep line for efficient queries, and discuss trade-offs between precomputation and on-the-fly computation.

Pro tip: Mention that in a real system like Instacart, tasks might be updated or cancelled, so the solution should handle dynamic updates or use an append-only log for auditability. Also, consider time zones and timestamp precision.

1. Clarify Requirements

Ask about the definition of 'active' (e.g., task start and end times), input parameters (simulation timestamp, user ID, historical point), and expected query patterns (frequency, real-time vs batch).

2. Design Data Model

Propose a schema for tasks: each task has user_id, start_time, end_time (or status changes). Consider storing intervals or events (start/end) for efficient querying.

3. Choose Algorithm

For a single query, filter tasks by user and check if historical point falls within [start, end]. For multiple queries, preprocess with interval trees, segment trees, or sweep line to answer in O(log n) or O(1).

4. Handle Edge Cases

Address tasks with null end_time (ongoing), tasks that start and end at the same time, and queries before any task or after all tasks. Also consider time zone consistency.

5. Discuss Scalability

Talk about indexing (e.g., on user_id and time), caching frequent queries, and distributed processing if data is large. Mention trade-offs between memory and query speed.

Key Points to Mention

  • Definition of 'active': task start and end timestamps, possibly with status changes.
  • Data structures for interval queries: segment trees, interval trees, or sweep line with events.
  • Time complexity: O(n) per query vs O(log n) with preprocessing; space-time trade-offs.
  • Handling dynamic updates: append-only log, versioning, or real-time indexing.
  • Edge cases: ongoing tasks, overlapping intervals, time zone and precision.
  • Scalability: partitioning by user, caching, and distributed query processing.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.