I’m excited to announce the first alpha preview of this project that I’ve been working on for the past 4 months. I’m initially posting about this in a few small communities, and hoping to get some input from early adopters and beta testers.
What is a DHT crawler?
The DHT crawler is Bitmagnet’s killer feature that (currently) makes it unique. Well, almost unique, read on…
So what is it? You might be aware that you can enable DHT in your BitTorrent client, and that this allows you find peers who are announcing a torrent’s hash to a Distributed Hash Table (DHT), rather than to a centralized tracker. DHT’s lesser known feature is that it allows you to crawl the info hashes it knows about. This is how Bitmagnet’s DHT crawler works works - it crawls the DHT network, requesting metadata about each info hash it discovers. It then further enriches this metadata by attempting to classify it and associate it with known pieces of content, such as movies and TV shows. It then allows you to search everything it has indexed.
This means that Bitmagnet is not reliant on any external trackers or torrent indexers. It’s a self-contained, self-hosted torrent indexer, connected via the DHT to a global network of peers and constantly discovering new content.
The DHT crawler is not quite unique to Bitmagnet; another open-source project, magnetico was first (as far as I know) to implement a usable DHT crawler, and was a crucial reference point for implementing this feature. However that project is no longer maintained, and does not provide the other features such as content classification, and integration with other software in the ecosystem, that greatly improve usability.
Currently implemented features of Bitmagnet:
- A DHT crawler
- A generic BitTorrent indexer: Bitmagnet can index torrents from any source, not only the DHT network - currently this is only possible via the /import endpoint; more user-friendly methods are in the pipeline
- A content classifier that can currently identify movie and television content, along with key related attributes such as language, resolution, source (BluRay, webrip etc.) and enriches this with data from The Movie Database
- An import facility for ingesting torrents from any source, for example the RARBG backup
- A torrent search engine
- A GraphQL API: currently this provides a single search query; there is also an embedded GraphQL playground at /graphql
- A web user interface implemented in Angular: currently this is a simple single-page application providing a user interface for search queries via the GraphQL API
- A Torznab-compatible endpoint for integration with the Serverr stack
Interested?
If this project interests you then I’d really appreciate your input:
- How did you get along with following the documentation and installation instructions? Were there any pain points?
- There’s a roadmap of high-priority features on the website - what do you see as the highest priority for near-term development?
- If you’re a developer, are you interested in contributing to the project?
Thanks for your attention. If you’re interested in this project and would like to help it gain momentum then please give it a star on GitHub, and expect further updates soon!
This looks really cool! How resource intensive is this? What sort of storage requirements are there for this to be a reasonably reliable method of acquiring media? I’m probably just gonna find out myself. I’ve recently fully switched over to usenet, but this could make torrents pretty compelling again.
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_filestofalse. In fact I may set this tofalseby 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.
As someone interested in Usenet, what’s the best provider and client to start with in your opinion?
I’ve been using easynews/nzbgeek/nzbget with an arr stack on debian and it’s worked well for me. I’m fairly new to usenet, so take this with a giant grain of salt.
Running for 6 days, save_pieces: false
My database is currently 184 GB