Design real-time top-K POI retrieval on maps

Q: Design real-time top-K POI retrieval on maps

This question evaluates a candidate's ability to design scalable, low-latency real-time retrieval and personalized ranking systems for geospatial data, testing competencies in spatial indexing and tiling, candidate generation versus re-ranking pipelines, caching and freshness strategies, latency and availability SLOs, deduplication and pagination, and model/ANN decision-making; the domain is ML system design for map-based POI retrieval and it assesses both high-level architectural reasoning and practical engineering trade-offs. Such problems are commonly asked to assess how an engineer balances consistency, stability and freshness under strict latency and availability targets while reasoning about operational concerns like load shedding, offline versus online evaluation, instrumentation and cost.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Onsite rounds at Snapchat.

Q: What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Snapchat during technical interviews.

Question

Real-Time Top-K POIs in Viewport: System Design

Context

Design a real-time system for a mobile map that continuously shows the top-K points of interest (POIs) within the user’s current viewport as they pan and zoom. The system must feel instant, remain stable while the viewport moves, and support personalization.

Requirements

Functional

Return top-K POIs for the current viewport rectangle and zoom level, with stable ordering during small pans/zooms.
Support filters (e.g., category, open-now), and pagination/infinite scroll for lists.
Deduplicate POIs across tiles and avoid flicker when moving.
Personalize ordering per user when available; provide sensible defaults for cold start.

Latency/SLOs

End-to-end p95 latency < 150 ms (from gesture end to POIs rendered), p99 < 250 ms.
Freshness: most POI attribute updates visible within 1–5 minutes; critical updates (closures) < 30 seconds.
Availability ≥ 99.9%.

Specify

Architecture across client, edge, and backend services.
Spatial indexing and tiling strategy (e.g., quadtree/S2, R-tree), cell covering, and range queries.
Candidate generation and cell unions.
Retrieval vs. re-ranking pipeline.
Caching (client/edge): tile-level and viewport-level.
Freshness and incremental updates.
Metrics (latency, quality, stability, freshness, cost).
When to use approximate nearest neighbor (ANN) for retrieval vs. learned models for ranking; decision criteria and hybrid approach.
Load shedding, pagination while panning, dedup across tiles, cold start, offline vs. online evaluation, and cost considerations.

Design real-time top-K POI retrieval on maps

Quick Overview

Design real-time top-K POI retrieval on maps

Real-Time Top-K POIs in Viewport: System Design

Context

Requirements

Functional

Latency/SLOs

Specify

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