Development [^1] of qbitmaid was over the course of several months. At first, the project was called qbit-clean and didn't have all the features the project has now. The issue was mainly with my download cache in unraid being filled with torrents I no longer needed to seed[^2]. When I would get a notification from the server that the download cache was 95% full I would have to manually go to [qbittorrent](https://www.qbittorrent.org/), sort the torrents by age and remove the ones older than two weeks avoiding torrents I wanted to keep.
This was tedious. Very tedious. So I went off to do more work just to avoid a little.`qbitmaid.py` is the main file that glues the project together. This was my first project where I heavily abstracted the design. More on this later. First, we'll connect to the API. I used an existing client [library](https://pypi.org/project/qbittorrent-api/2022.5.32/) that makes this process simpler than writing your own client.
Next, we "sift" out torrents to be deleted. This was created with a positive sieve meaning we specify positive scenarios. In other words, I know which torrents I want to keep as opposed to the torrents I don't want. Theres pros and cons to both scenarios however in the long term a positive sieve is less work.
> `qlist.py` has functions at the bottom of the file that are referenced in the conditions. This very method of programming made it easy to write unit tests as I went.
```python
if is_preme(torrent['seeding_time'], self.min_age):
continue
```
```python
def is_preme(seeding_time, minage):
if seeding_time <= minage:
return True
```
When it comes across an item that meets certain criteria it will skip it. For instance, the example above checks to see if it's too soon to remove a torrent. This is because some [trackers][private tracker] require a minimum seed time. If you were to remove a torrent sooner than they require, it could lead to getting kicked.
`qlist.py` Has a couple jobs:
- Tag torrents according to how they should be treated.
In this case the items I want to keep stays. `qlist.py` passes the data over to `qprocess.py`. This is done through about 2 layers of abstraction. Unfortunatley, this type of programming makes it difficult to follow.
`qprocess.py` has four jobs:
- Collect telemetry
- Delete torrents if needed
- Enable debugging if needed
`qprocess.py`
```python
def tor_processor(self):
"""Main logic to sort through both self.tracker_nonprotected_list and self.tracker_protected_list
If torrent meets criteria for deletion, its infohash_v1 will be appended to self.torrent_hash_delete_list
Then use [Drone][drone] to package this into a container. This pushes the container to an [OCI repo][oci] in gitea. The application is configured through a toml file:
Finally, this same container will run the test cases in `test_qbitmaid.py`. This is handled by drone. So eachtime I push new code to a development branch on gitea, it creates a container to test and tests the code. Once I see that it has passed, I can merge the code to the main branch.
## Final Notes
I have been using this for over 2 years. It was a huge learning experience and my coding practices have evolved over my newer projects. While I did make this for my use mainly, feel free to try it out! If you have any questions, you can open an issue [here](https://git.jonb.io/jblu/qbit-maid/issues/new).
