danielgraf

Cool, thanks for the feedback.

For: 1: There should be something about downloading in the Photon logs. You can check with docker compose logs photon. I do not remember how long it took to download the index for Germany, but it was noticeable. It depends a lot on how you are connected to the internet and then how long it takes to extract the index.

2: You should also see that in the log of Photon. If the default does not work reliably, you can create an account on Geoapify; it is free, and you get 3000 reverse geocode requests per day. I personally have Photon for DE and then the default service and additional Geoapify.

3: Yeah, I was also thinking about that. In the end, a per-user setting and then the overlay should be disabled, but it could take a while until I can get to it.

I am glad it worked out for you in importing the first file. I am still puzzled why it took that long.

For the new format, did you have Android or iOS? With the timeline export from Google Maps on iOS, we can not do anything at the moment because there is actually no raw data in it. Only information like you stayed in this timeframe at that point and you traveled between these points. It's actually a little bit funny that it aggregates to the same data Reitti uses in the end.

If you are on Android, it could also be a bug when importing that file. I only had a small one from one of my accounts to test. If you mind creating a bug report, I will have a look. If you do not want to attach the export file there, feel free to send it to daniel@dedicatedcode.com. I will have a look at it then privately. No problem.

For the overlap in exports, it depends. If the points are the same, meaning they have the same timestamp, then Reitti will discard them. If not, they will be handled like every other new data and will end in recalculating visits and trips around that particular time.

Glad I could help :)

This process is not triggered by any external events.

Every ten minutes, an internal background job activates. Its function is to scan the database for any RawLocationPoints that haven't been processed yet. These unprocessed points are then batched into groups of 100, and each batch is sent as a message to be consumed by the stay-detection-queue. This process naturally adds to the workload of that queue.

However, if no new location data is being ingested, once all RawLocationPoints have been processed and their respective flags set, the stay-detection-queue should eventually clear, and the system should return to a idle state. I'm still puzzled as to why this initial queue (stay-detection-queue) is exhibiting such slow performance for you, as it's typically one of the faster steps.

Thank you for testing Reitti. 🙏

It depends on two key requirements for Reitti:

1. First, it finds all photos from Immich taken on the day you selected.
2. Then, it filters these photos based on the selected map bounds, using the embedded EXIF geolocation data (where the photo was shot).

If the EXIF data does not contain geolocation information, we currently cannot display those photos because their placement on the map cannot be determined.

Could you please verify in Immich if the expected photo has its location in the metadata? If it is available there, then the issue might lie in how Reitti is parsing that specific data.

That's good, but I still question why it is so slow. If you receive these timeout exceptions more often, at some point the data will cease to be analyzed.

I just re-tested it with multiple concurrent imports into a clean DB, and the stay-detection-queue completed in 10 minutes. It's not normal for it to take that long for you. The component that should take the most time is actually the merge-visit-queue because this creates a lot of stress for the DB. This test was conducted on my laptop, equipped with an AMD Ryzen™ 7 PRO 8840U and 32GB of RAM.

Thanks for getting back to me. I can look into it. I don't think it's connected, but you never know.

The data goes the same way, first to RabbitMQ and then the database. So it shouldn't matter, it's just another message or a bunch of them in the queue.

It is actually awesome if you have some old photos with the geodata attached and scim through Reitti and suddenly one of them shows up :)

Hmm, I had hoped you say something like a Raspberry PI :D

But this should be enough to have it processed in a reasonable time. What I do not understand in the moment is, that the filesize should not affect it in any way. When importing it 100 Geopoints are bundled, send to RabbitMQ. From there we retrieve them, do some filtering and save them in the database. Then actually nothing happens anymore until the next processing run is triggered.

But this than works with the PostGis DB and not with the file anymore. So the culprit should be there somewhere. I will try to insert some fake data into mine and see how long it takes if i double my location points.

Thanks for the information. I will try to recreate it locally. In my testing I used a 600MB file and this took maybe 2 hours to process on my server. It is one of these ryzen 7 5825U. Since Reitti tries to do these analysis on multiple cores we start it with 4 to 16 Threads when processing. But the stay detection breaks when doing it that way, so it is locking per user to handle that. If now one of them takes a long time the others will break eventually. They will get resheduled 3 times until rabbitmq gives up.

On what type of system do you run it?

I will add some switches so it is configurable how many threads are opened and add some log statements to print out the duration it took for a single step.

It was not intentional but after bothering not about it because i had other things on my mind i got used to it and now like it the way it is.

But for everyone who is bothered by that. If Reitti reaches 1k stars on Github I will add a switch to use a centered one 😊

Congratulations 😆

To help with that I would need some information:

• does it show anything in the logs?
• what do you mean by several years or how big was the Records.json?

Thank you for testing 🙂