Instacart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Instacart coding screen focused on building a basic task store with the four standard CRUD operations. Pretty straightforward premise but the details matter more than you'd expect.

Questions Asked (1)

Implement a task store that supports Create, Read, Update, and Delete operations. Each operation takes a task ID, and Create/Update also take a description.

Algorithms & Data StructuresSystem DesignAPI & Integrations

Author's notes

Seemed simple enough at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a clean interface with separate methods for each operation. Implement using an in-memory hash map for O(1) average time complexity, and discuss trade-offs for persistence and concurrency.

Pro tip: Mention that you would use a unique ID generator and handle edge cases like non-existent IDs gracefully. Also, briefly discuss how you would extend this to a distributed system, showing awareness of scale.

1. Clarify Requirements

Ask about expected scale, persistence needs, concurrency, and whether IDs are provided or generated. Confirm the exact method signatures and return types.

2. Design Interface

Define a TaskStore interface with methods: createTask(description), getTask(id), updateTask(id, description), deleteTask(id). Specify return values (e.g., task object, boolean success).

3. Choose Data Structure

Use a hash map (dictionary) for O(1) average-time operations. For persistence, consider a database; for concurrency, use locks or concurrent data structures.

4. Implement Operations

Write code for each operation, handling edge cases: create generates unique ID, read returns task or null, update modifies description if exists, delete removes task.

5. Discuss Trade-offs and Extensions

Talk about time/space complexity, persistence options (SQL vs NoSQL), concurrency control, and how to scale (sharding, caching).

Key Points to Mention

Use of hash map for O(1) average time complexity
Unique ID generation (e.g., UUID, auto-increment)
Handling non-existent IDs gracefully (return null or throw exception)
Concurrency considerations (locks, atomic operations)
Persistence options (in-memory vs database)
Scalability and distributed system design (sharding, replication)

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