# Low Level Design: Geo-Fencing Service

Source: https://www.techinterview.org/post/3233470860/lld-geo-fencing/
Updated: 2026-04-17 · techinterview.org

## Geo-Fencing Service: Low Level Design

## Data Model

### GeoFence Table


```
GeoFence
--------
id              BIGINT PK
name            VARCHAR(255)
type            ENUM('circle','polygon')
center_lat      DOUBLE          -- circles only
center_lng      DOUBLE          -- circles only
radius_meters   INT             -- circles only
geom            GEOGRAPHY(POLYGON)  -- PostGIS, used for all shapes
owner_id        BIGINT
trigger_on      ENUM('enter','exit','both')
active          BOOLEAN
created_at      TIMESTAMPTZ
```


Circles are also stored as polygon geometries (approximated as a 64-point polygon) to unify spatial queries, but `center_lat`, `center_lng`, and `radius_meters` are retained for a fast Haversine pre-filter before invoking PostGIS.

### Event Table


```
FenceEvent
----------
id              BIGINT PK
fence_id        BIGINT FK
device_id       BIGINT
event_type      ENUM('enter','exit')
occurred_at     TIMESTAMPTZ
```


### GeoFenceSubscription Table


```
GeoFenceSubscription
--------------------
id              BIGINT PK
fence_id        BIGINT FK
callback_url    TEXT
secret          VARCHAR(64)     -- HMAC signing key for webhook payloads
created_at      TIMESTAMPTZ
```


## Location Update Ingestion

### API Endpoint


```
POST /locations
Content-Type: application/json

{
  "updates": [
    { "device_id": 42, "lat": 37.7749, "lng": -122.4194, "ts": 1700000000 },
    ...
  ]
}
```


Accepts 1–10 location updates per device per second. Updates are written to a Kafka topic `location-updates` for async fence evaluation, keeping the HTTP response fast. The ingestion service validates coordinate ranges and rate-limits per device.

## Fence Evaluation Pipeline

### Spatial Query


```
-- Find all active fences containing the new device location
SELECT gf.id, gf.trigger_on
FROM geofence gf
WHERE gf.active = TRUE
  AND gf.owner_id IN (/* fences relevant to this device */)
  AND ST_Within(
        ST_SetSRID(ST_MakePoint(:lng, :lat), 4326)::geography,
        gf.geom
      );
```


### Spatial Index


```
CREATE INDEX idx_geofence_geom ON geofence USING GIST (geom);
```


The GiST index enables a bounding-box pre-filter so PostGIS only runs the precise `ST_Within` check against a small candidate set. For circle fences, a Haversine distance check runs first as an additional pre-filter before the PostGIS call.

## State Tracking and Debounce

To prevent false events from GPS jitter, a device's inside/outside state per fence is tracked in Redis:


```
Key:   fence_state:{fence_id}:{device_id}
Value: { "state": "inside", "since": 1700000000 }
TTL:   300s
```


An **enter** event is only fired when the device has been continuously inside the fence for >30 seconds. An **exit** event is only fired when the device has been continuously outside for >30 seconds. This debounce window is configurable per fence.

## Webhook Delivery

When a confirmed enter/exit event is written to `FenceEvent`, the system looks up all `GeoFenceSubscription` rows for that fence and enqueues a webhook delivery task:


```
POST {callback_url}
X-Signature: HMAC-SHA256(secret, payload)
Content-Type: application/json

{
  "fence_id": 101,
  "device_id": 42,
  "event_type": "enter",
  "occurred_at": "2024-01-01T12:00:00Z"
}
```


Delivery uses exponential backoff with up to 5 retries. Failed deliveries are logged for manual inspection.

## Scalability Notes

  - Fence evaluation workers consume from the Kafka `location-updates` topic and scale horizontally.

  - Active fence sets per device are cached in Redis to avoid per-update DB lookups.

  - PostGIS runs on a read replica; writes go to primary.

  - Very high-volume deployments can shard by geographic region using a geohash prefix.

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: [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: [Airbnb Interview Guide 2026: Search Systems, Trust and Safety, and Full-Stack Engineering](https://www.techinterview.org/post/3233460279/airbnb-interview-guide-2026-search-systems-trust-and-safety-and-full-stack-engineering/)