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Photon is solid - but it comes with a very different operational profile than what I am aiming for.

Photon is built on Elasticsearch (Java) - so it tends to mean a heavier index + higher RAM/CPU expectations and more moving parts. That's fine on a beefy server, but it is a rough fit for the "drop-in appliance on small edge/on-prem boxes (amd64/arm64) + simple ops" goal.

Corviont's geocoder is intentionally "boring": a single SQLite file + an HTTP service, built from Nominatim-derived data. Fast startup, low RAM, easy to ship per-region, and it stays consistent with the rest of the stack.

That said - if there is demand for a "server-grade geocoder option" for people already comfortable running Elastic, I am not opposed to offering it as an alternative profile. The default is just optimized for constrained edge hardware and minimal moving parts.

discuss

dennis16384|1 month ago

Have you measured actual memory and disk requirements of a photon OpenSearch index vs your sqlite database?

packet_mover|1 month ago

No. When I looked at Photon and saw that it involves running Java plus an OpenSearch/Elasticsearch backend on the device, I assumed it would be heavier in terms of memory and moving parts than my setup (single SQLite file + small HTTP API).

Have you (or anyone here) actually run Photon on edge-class hardware? If you have real-world numbers, I'd be interested in seeing them. When I add house-number search, Photon might be an easier route than enhancing my current approach.