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Microsoft·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediateRejected
Jun 2026

Summary

Microsoft SWE coding round, no AI tools allowed. Had to implement a SingleFlight component to handle burst traffic in front of an LLM service, and my code didn't compile by the end which I'm pretty sure tanked the whole thing.

Questions Asked (1)

Q1

Design and implement a SingleFlight component that sits in front of an LLM service to batch or deduplicate burst traffic. Then analyze the edge cases your implementation handles.

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

The core idea is straightforward enough: if ten requests come in for the same key at the same time, only one should actually hit the backend and the rest should wait and share the result.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a SingleFlight component using a map of in-flight requests with synchronization primitives to deduplicate concurrent calls. Implement the core logic with careful attention to race conditions, error handling, and resource cleanup, then systematically analyze edge cases and trade-offs.

Pro tip: Emphasize idempotency and failure isolation: ensure that a single failed request doesn't poison all waiters, and consider how to handle context cancellation and timeouts to prevent goroutine leaks.

1. Clarify Requirements and Scope

Ask about expected traffic patterns, latency SLAs, and whether batching or pure deduplication is needed. Confirm if the component should be in-process or distributed, and what consistency guarantees are required.

2. Design the Core Data Structures

Propose a concurrent map (e.g., sync.Map or sharded map) keyed by request parameters, with each entry containing a call object that tracks waiters and result. Use a mutex or channels to coordinate access.

3. Implement Deduplication Logic

For each incoming request, check if an identical call is in-flight; if so, attach as a waiter and block until result is available. Otherwise, initiate the call, store it in the map, and broadcast the result to all waiters upon completion.

4. Handle Edge Cases and Failure Modes

Address scenarios like context cancellation, timeouts, panics, and partial failures. Ensure proper cleanup of map entries and waiter channels to avoid leaks, and decide on error propagation strategy.

5. Analyze Trade-offs and Optimizations

Discuss batching vs. deduplication, memory overhead, lock contention, and potential for request coalescing. Consider metrics, logging, and how to test the component under load.

Key Points to Mention

  • Use of synchronization primitives (mutex, channels, sync.Map) to safely manage concurrent access to in-flight requests.
  • Handling of context cancellation and timeouts to prevent goroutine leaks and ensure waiters are released.
  • Error propagation strategy: whether to share the same error with all waiters or allow retries, and how to avoid cascading failures.
  • Memory management: timely removal of completed requests from the map to prevent unbounded growth.
  • Batching vs. deduplication: when to coalesce multiple distinct requests into a single LLM call for efficiency.
  • Testing approach: unit tests for concurrency, race detection, and load testing to validate performance under burst traffic.

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