Jackett, a useful and straightforward solution for finding torrents, has garnered attention for its ease of use and efficiency. This article will delve into Jackett's capabilities and explore alternatives for both user interfaces and API (Application Programming Interface).
Overview
Previously, insights were shared about a small project designed to manage torrent clients and provide access to the Kinozal tracker interface via a Telegram bot. This solution, integrated with the TMDB database, serves as a commendable alternative to the servarr stack. Feedback indicates that many users found the bot's setup simpler. The primary goal is to consolidate all interfaces into one, enabling remote search and management of content downloads from phones. Despite supporting only one tracker for media content searches, this limitation is often sufficient.
Recently, instructions for running the bot in a Docker container were added. This enhancement simplifies deployment and management.
Servarr Stack
The servarr stack adopts a fundamentally different architectural approach. For instance, Sonarr automates the process of searching for and downloading series by tracking new episode releases and automatically downloading based on specified criteria. However, users still need to interact with the interface periodically. A significant challenge encountered was the lack of content search with Russian localization, a problem effectively addressed by Jackett.
Jackett
Jackett serves as an API (essentially a remote management interface for utilizing its functions in other applications) for a vast number of providers, facilitating centralized and simultaneous (parallel) searches across multiple trackers. Beyond its API, the user interface is particularly beneficial, allowing users to search for torrents without needing to navigate various websites. After familiarizing yourself with this interface, you might find yourself deleting all your tracker bookmarks from your browser. It’s impressive that everything is available out of the box, without requiring additional plugins or modules (unlike plugins for qBittorrent).
You can launch Jackett either through an executable file or as a service on Windows (available for download in the GitHub repository) or, more conveniently, in a Docker container. Here’s an example of launching the container with a single command:
# Create a directory for Jackett data
sudo mkdir /jackett
# Run the Jackett container
sudo docker run -d
--name=jackett
-e PUID=1000
-e PGID=1000
-e TZ=Etc/UTC
-e AUTO_UPDATE=true `#optional`
-e RUN_OPTS= `#optional`
-p 9117:9117
-v /jackett/data:/config
-v /jackett/blackhole:/downloads
--restart unless-stopped
lscr.io/linuxserver/jackett:latest
Settings for all trackers (also known as indexers) are stored in the directory /jackett/data/Jackett/Indexers/ (on Windows: C:ProgramDataJackettIndexers), while the server configuration can be found at /jackett/data/Jackett/ServerConfig.json (on Windows: C:ProgramDataJackettServerConfig.json). Transitioning from Windows to Linux (directly to the container) is seamless, requiring only the replacement of files in the specified directories.
The interface is designed to be maximally simple yet convenient. All settings are located on the main and only page in the browser, and adding trackers for indexing requests is done through a modal window in the interface.
