A lightweight program for storing and accessing location data published by OwnTracks apps via MQTT or HTTP.
OwnTracks Recorder is a server-side program that stores and manages location data published by OwnTracks mobile apps. It acts as a backend for the OwnTracks ecosystem, receiving data via MQTT or HTTP, storing it efficiently in files, and providing APIs for retrieval and visualization. It solves the problem of securely and privately hosting location history without relying on third-party services.
Developers and hobbyists running personal or small-scale location tracking systems who want full control over their data. It's ideal for those using OwnTracks apps and needing a self-hosted, lightweight backend.
Developers choose OwnTracks Recorder for its simplicity, lack of external database dependencies, and comprehensive built-in API. Its ability to run on low-end hardware and support for both MQTT and HTTP ingestion make it a versatile, privacy-focused alternative to cloud-based tracking services.
Store and access data published by OwnTracks apps
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Stores data in plain files without an external database, enabling efficient operation on low-end hardware like Raspberry Pi, as emphasized in the design philosophy.
Provides a REST API and WebSocket server for querying location data, including last positions, historical tracks, and GeoJSON, reducing the need for additional backend services.
Supports Lua scripts to modify behavior, add processing, or gateway data, offering flexibility for advanced use cases as documented in HOOKS.md.
Integrates with services like OpenCage and Google, caching results to minimize external API calls and enrich location data with addresses and time zones.
The README explicitly notes that configuring Friends in HTTP mode, especially in Docker, is difficult, which can hinder deployments and limit functionality.
Designed primarily for OwnTracks apps and MQTT, making it less adaptable for other location tracking systems without custom integration efforts.
Relies on plain files and LMDB, which may not handle massive datasets efficiently compared to traditional databases, potentially affecting performance at scale.