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Stripe·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026Remote

Summary

Stripe SWE interview with a meaty simulation problem that kept growing in scope, part by part. Each part added a new constraint on top of the last, so if your data structures weren't flexible enough early on you'd be rewriting half your code by part 4.

Questions Asked (1)

Q1

Design and implement a load balancer simulator that routes long-lived WebSocket connections across m backend servers, supporting CONNECT, DISCONNECT, and SHUTDOWN request types with rules for least-loaded routing, object stickiness, per-server capacity limits, and connection eviction on shutdown.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

This was a single problem with five escalating parts.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a high-level design with core data structures and algorithms. Implement the solution incrementally, explaining trade-offs and handling edge cases like capacity limits and eviction.

Pro tip: Focus on the stickiness requirement: it often conflicts with least-loaded routing, so discuss how to balance them (e.g., sticky sessions with fallback to least-loaded when capacity is reached).

1. Clarify Requirements

Ask about expected scale, connection duration, stickiness definition, and shutdown semantics. Confirm whether eviction should be graceful or immediate.

2. Design Data Structures

Choose structures to track server loads, connection-to-server mappings, and object-to-server stickiness. Consider heaps or balanced trees for efficient least-loaded lookup.

3. Implement Core Operations

Write code for CONNECT (routing with stickiness and capacity), DISCONNECT (updating loads and mappings), and SHUTDOWN (evicting connections and redistributing).

4. Handle Edge Cases

Address scenarios like all servers at capacity, sticky object with no available server, and shutdown with active connections. Discuss fallback strategies.

5. Analyze Trade-offs

Discuss time/space complexity, consistency vs. availability, and potential optimizations (e.g., lazy updates, caching).

Key Points to Mention

  • Least-loaded routing algorithm and efficient implementation (e.g., min-heap with lazy updates)
  • Stickiness mechanism: mapping object IDs to servers, handling server failures or capacity limits
  • Capacity enforcement: per-server connection limits and rejection/queueing strategies
  • Shutdown eviction: graceful draining, connection reassignment, and client notification
  • Concurrency considerations: thread safety, locking, or event-driven architecture
  • Scalability: distributed load balancing, consistent hashing for stickiness

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