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feat(server): library refresh go brrr #14456
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Nice start! I think there are still a lot of untapped potential improvements here.
The update to |
Thanks for your comments @mertalev ! I'll first attempt to do the import path and exclusion pattern checks in SQL and then move to your suggestions |
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.where({ isOffline: false }) | ||
.andWhere( | ||
new Brackets((qb) => { | ||
qb.where('originalPath NOT SIMILAR TO :paths', { |
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Use LIKE
instead of SIMILAR TO
.
The exclusions and import paths are also specific to a particular library, right? So you need to specify the library in the query.
Also, can you generate SQL for this and confirm with EXPLAIN ANALYZE
that it uses an index?
.update() | ||
.set({ | ||
isOffline: true, | ||
deletedAt: new Date(), |
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The status
also needs to be set. This is why I don't really like the status field. The same info is stored in multiple places so it's so easy for it to go out of sync like this.
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Never thought of that, I've implemented your suggestion. I'm also considering changing the initial import code to ignore file mtime, this allows us to not do any file system calls except for the crawl. Metadata extraction will have to do the heavy lifting instead |
Would that mean you queue them for metadata extraction even if they're unchanged? You can test it but I think it'd be more overhead than the stat calls. Edit: also if you do this with the source set to |
I was referring to new imports, files that are new to immich. I hoped to improve the ingest performance by removing the stat call. After testing, there are two issues:
If we can mitigate the two issues above, I can rewrite the library import feature and do that in batches as well! |
I don't see why fileModifiedAt needs a non-null constraint in the DB. Might just be an oversight that didn't matter because it didn't affect our usage. I think you can change the asset entity and generate a migration to remove that constraint. For sidecar files, maybe you could add |
This PR significantly improves library scanning performance. Wherever suitable, we are doing jobs in batches, and many looped database interactions are replaced with SQL queries.
A library scan with 22k items where nothing has changed since the last scan used to take 1m 22s, now it's below 10 seconds, an improvement of 87 percent!
Highlights:
Bonus: