rippling·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Rippling SWE interview threw a pretty focused systems/design problem at me around driver balance tracking. The core challenge was less about the data structure itself and more about justifying the caching strategy under different write patterns. Left feeling like I probably undersold the trade-off discussion.

Questions Asked (1)

You're receiving a continuous stream of delivery events that each update a specific driver's balance. Design a data structure that can return the combined total balance across all drivers in O(1) time, and explain how you'd keep that cached total consistent through inserts, updates, and deletes.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

My first instinct was just 'keep a running sum' and I said it pretty fast, which I think made it sound too easy.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: O(1) total balance retrieval, handling inserts, updates, and deletes. Propose a hash map from driver ID to balance, plus a running total variable that is updated on every operation. Explain how each operation maintains the total, and discuss edge cases like negative balances and concurrency.

Pro tip: Mention that you'd use a lock or atomic operations to ensure thread safety, and consider using a read-write lock if reads are frequent. This shows awareness of production concerns beyond the basic algorithm.

1. Clarify requirements and assumptions

Confirm that events are updates to driver balances, and we need O(1) total. Assume single-threaded unless specified, but be prepared to discuss concurrency.

2. Design the data structure

Use a hash map (driver ID -> balance) to store individual balances, and a separate variable to hold the total sum. This allows O(1) access to any driver's balance and O(1) total retrieval.

3. Define operations for consistency

For insert: add new driver with balance, update total. For update: adjust total by difference. For delete: subtract balance from total and remove from map. Ensure all operations are O(1).

4. Address edge cases and concurrency

Handle negative balances, zero balances, and duplicate inserts. Discuss thread safety using locks or atomic operations, and potential performance trade-offs.

5. Summarize and analyze complexity

Reiterate that all operations are O(1) time and O(n) space. Mention that this is optimal for the given requirements.

Key Points to Mention

Hash map for driver balances and a running total variable.
O(1) time for insert, update, delete, and total retrieval.
Updating total by delta on each operation to maintain consistency.
Handling edge cases: negative balances, zero balances, duplicate driver IDs.
Concurrency considerations: locks, atomic operations, or read-write locks.
Space complexity: O(n) for n drivers.

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