# Low Level Design: Mapping Service

Source: https://www.techinterview.org/post/3233471611/lld-mapping-service/
Updated: 2026-07-13 · techinterview.org

## What Is a Mapping Service?

A mapping service provides geographic data, visual map rendering, and spatial queries to clients. Think Google Maps or OpenStreetMap: users pan and zoom across a rendered map, search for places, and query distances or boundaries. The service must store a massive graph of the physical world, render tiles efficiently, and answer geospatial queries at low latency.

## Data Model

The world is represented as a weighted directed graph:

  - **Node**, a geographic point: `(node_id BIGINT, lat DOUBLE, lon DOUBLE, elevation FLOAT)`

  - **Edge**, a road segment between two nodes: `(edge_id BIGINT, src_node BIGINT, dst_node BIGINT, length_m FLOAT, speed_limit SMALLINT, road_class ENUM('motorway','trunk','primary','secondary','local'), one_way BOOLEAN)`

  - **Place**, a named entity (business, landmark): `(place_id BIGINT, name TEXT, category VARCHAR(64), lat DOUBLE, lon DOUBLE, polygon GEOMETRY)`

  - **Tile metadata**, pre-rendered raster or vector tiles indexed by `(zoom, x, y)` following the Slippy Map convention.

Spatial indexes (PostGIS, S2 geometry library, or H3 hexagonal grid) accelerate bounding-box and radius queries. The graph itself is partitioned by region using a technique like METIS or KD-tree decomposition so that cross-partition edges are minimized.

## Core Components and Workflow

### Tile Server

Map data is pre-rendered into tiles at each zoom level (0–20). A tile pipeline ingests OSM data, applies styling rules (Mapnik, Mapbox GL), and stores tiles in an object store (S3-compatible). A CDN sits in front, caching tiles by `(zoom, x, y)`. Cache hit rates above 95% are typical for popular regions. On a cache miss, the tile server regenerates the tile on demand and warms the cache.

### Geocoding

Forward geocoding (address → coordinates) uses an inverted index over place names combined with a spatial filter. Elasticsearch with geo_point fields or a custom trie structure handles fuzzy matching. Reverse geocoding (coordinates → address) uses a spatial join against administrative boundary polygons stored in PostGIS.

### Spatial Query Engine

Bounding-box searches use R-tree indexes. Nearby searches use a Haversine filter combined with an index scan on a geohash prefix column, reducing candidate rows before exact distance computation.

## Failure Handling

  - On tile generation failure, serve stale tile from cache with a short TTL; retry generation asynchronously.

  - On database partition unavailability, route read queries to a replica; writes queue in a durable log (Kafka) and replay when the primary recovers.

  - On a CDN origin miss storm, use request coalescing (one origin fetch serves many waiting clients) to prevent thundering herd on popular tiles.

  - On geocoding index corruption, maintain a blue/green index pair; promote the green index after a successful rebuild before cutting over traffic.

## Scalability Considerations

  - Tile serving is stateless and horizontally scalable; add CDN edge nodes near user population centers.

  - The graph database shards by geographic region; inter-shard queries (cross-border routes) are handled by a thin routing layer that stitches partial paths.

  - Read replicas handle the heavy geocoding read load; writes (map edits) go through a change queue processed by a dedicated ingestion service.

  - Hot-spot tiles (e.g., city centers at zoom 14) benefit from in-memory caching (Redis) in front of the object store.

See also: [Netflix Interview Guide 2026: Streaming Architecture, Recommendation Systems, and Engineering Excellence](https://www.techinterview.org/post/3233460274/netflix-interview-guide-2026-streaming-architecture-recommendation-systems-and-engineering-excellence/)

See also: [Uber Interview Guide 2026: Dispatch Systems, Geospatial Algorithms, and Marketplace Engineering](https://www.techinterview.org/post/3233460273/uber-interview-guide-2026-dispatch-systems-geospatial-algorithms-and-marketplace-engineering/)

See also: [Scale AI Interview Guide 2026: Data Infrastructure, RLHF Pipelines, and ML Engineering](https://www.techinterview.org/post/3233460275/scale-ai-interview-guide-2026-data-infrastructure-rlhf-pipelines-and-ml-engineering/)