• 3 Posts
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Joined 1 year ago
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Cake day: June 12th, 2023

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  • Hi, and thanks!

    As a priority I’d like to gather some more rigorous performance benchmarks, but I can give you some hand-wavey stats now: Bitmagnet is currently fluctuating between 2-10% CPU usage on my M2 Mac Mini, and is using ~120MB of memory having currently been running for around 48 hours. Overall, the GoLang implementation seems pretty efficient to me considering how much I know is going on in the background.

    Disk space usage of the database- this will be highly dependent on 2 configuration options, the first of which I’ve only just added in the just-released version. Copied from the configuration page of the website:

    • dht_crawler.save_files (default: true): If true, file metadata from the DHT crawler will be saved to the database. This provides more rich information about a torrent, but will use a lot more disk space. If disk space is at a premium you may want to consider disabling this.
    • dht_crawler.save_pieces (default: false): If true, the DHT crawler will save the pieces bytes from the torrent metadata. The pieces take up quite a lot of space, and aren’t currently very useful, but they may be used by future features.

    For me, 24 hours of crawling uses ~2.5GB of database disk space for metadata on the ~120k torrents it has discovered. Yep, that sounds like a lot, however 90% of that is taken up with the files metadata, and could have been saved by setting dht_crawler.save_files to false. In fact I may set this to false by default and allow users to opt-in to the full-fat torrent info.

    I’ve also imported the entire RARBG backup (the SQLite one, see tutorial on the Bitmagnet website). This, along with all the associated metadata from TMDB, took around 4GB of database space, which seems quite acceptable considering it’s basically every movie and TV show. Note that this does NOT include the metadata on individual files as I described above.

    A priority feature for me (detailed on website) is smart deletion - this would allow you to automatically discard a lot of data that can be automatically determined of no interest and therefore greatly reduce disk space demands.



  • Hi, this is a great point and one that I’ve already given consideration to. I’ll address separately the issue of the primary datastore ,i.e. Postgres, and the Redis dependency:

    Postgres as the only option for the data store

    There are 2 reasons for this:

    • Performance: while SQLite could offer a simpler/embedded data store, it simply doesn’t have the performance and features of Postgres. Bitmagnet has a faceted search engine and is write-intensive (it will be discovering ~5k torrents per hour and writing these to the database along with associated metadata). As such, its database may not be suitable for running on older hardware. A SQLite adapter, if it was developed, may simply not be up to the job (although as I haven’t attempted this I can’t say what the performance would be like). That said, Bitmagnet itself is not especially resource intensive, you could probably run it on a Raspberry PI but point it to a Postgres instance on some more powerful hardware. At this stage I’ve only been running it on a M2 Mac Mini with Postgres located on its SSD and so would be interested to know people’s mileage on other hardware.
    • Development, support and maintenance overhead: I’m a lone developer and this project is already too big for one person. A SQLite adapter, if feasible performance-wise, I think could only happen if other contributors joined the project as my to-do list is already pretty long. It would have to achieve feature parity with the Postgres implementation which makes use of several Postgres-specific features and extensions. It would also mean a longer testing cycle and therefore probably a slower release cadence. That said, if there was enough demand and assistance then I’d be open to looking into the feasibility of this once the rest of the application is a little more mature and the current database schema more finalised.

    Redis dependency

    Redis is currently used only for the asynchronous task queue. I would like to have put this in Postgres, but there simply is not a good out-of-the-box solution that works well with Postgres and GoLang, and is actively maintained. I looked at quite a few queuing libraries and eventually settled on asynq (https://github.com/hibiken/asynq), which is a great library and does the job well - but could really do with support for non-Redis backends.

    Using Redis here was a pragmatic decision that allowed me to make progress, rather than an optimal one. I guess I could have built a simple Postgres-based queue myself but that would have been a distraction and probably sub-optimal compared with a mature/separately developed library. It remains an option. Since I looked into this a new project has sprung up which I’m keeping an eye on - https://www.tork.run/ - it has a Postgres backend and looks like it might be up to the job, but is very new.

    So yes, I’m very aware that the additional Redis dependency is not ideal and it may well disappear at some point.