- Add Makefile to simplify installing required tools
- Add CONTRIBUTING.md welcome new contributors
- Add fmt, lint, test tasks to makefile and update CONTRIBUTING.md
- Add CI workflow
- Log with local date
**Description:**
This PR introduces a major overhaul of how streams are analyzed and connections are managed. It integrates `ffprobe` for deep stream analysis, adds a background task queue for metadata resolution to prevent UI blocking, introduces a connection priority system, and adds support for Live TV probing.
### 🚀 Key Features
* **FFprobe Integration & Live TV Probing**:
* Automatically probes streams (VOD/Series **and now Live TV**) to determine resolution, codecs (HEVC/AV1), dynamic range (HDR/DV), bit depth, and audio channels.
* **Safe Probing**: Probing strictly respects the provider's `max_connections` limit.
* Docker images now include static `ffmpeg/ffprobe` binaries.
* **Smart Connection Management (Priority System)**:
* Introduced a priority system for connections to manage provider limits intelligently.
* **Preemption**: Users with higher priority can preempt (kick) lower priority connections when provider slots are full.
* **Background Queue**: Metadata resolution and stream analysis run as background tasks (Priority 0). They only run when connection slots are idle and yield immediately if a real user needs to stream.
* **Enhanced Metadata Resolution**:
* **Fallback Logic**: If a provider is missing the TMDB ID or Release Date, the system resolves them via the TMDB API.
* **Smart Title Cleaning**: Implemented a centralized cleaner to strip common IPTV prefixes before attempting metadata matches or generating filenames, significantly improving match rates.
* **Improved Output**:
* **Quality Tags**: Filenames in STRM files now include detailed tags (e.g., `Movie - [4K HEVC HDR].strm`).
* **Flat Grouping**: `flat: true` now correctly groups multi-version movies into single folders based on TMDB ID.
* **Global Series Date**: Ensures consistent series folder naming even if episodes have different years.
An optimization has been introduced to reduce memory consumption during playlist updates.
Overview
Previously, provider playlists were fully loaded into memory during the update process. For large playlists (e.g. hundreds of thousands of entries or multiple providers), this could result in significant RAM usage.
With the new implementation, it is now possible to optionally read provider playlists directly from disk instead of keeping them entirely in memory.
How It Works
- users can configure whether:
- Provider playlists are loaded into memory (previous behavior), or
- Provider playlists are streamed to/from disk to minimize RAM usage.
Processing remains sequential and batch-based, ensuring identical functional behavior.
This approach significantly lowers peak memory usage, especially on systems with limited resources.
Benefits
- Reduced peak RAM consumption during playlist updates
- Better scalability for large playlists and multiple providers
- Full backward compatibility
Trade-offs / Drawbacks
- Increased processing time due to reduced in-memory caching
- Higher disk I/O usage, especially for large or fragmented playlists
- Performance depends more strongly on disk speed (Nvme SSD, HDD)
- Slightly increased CPU overhead due to repeated parsing and deserialization
- Not optimal for environments where fast updates are more important than memory usage
Recommendation
- Use in-memory mode for systems with sufficient RAM and a focus on update speed
- Use disk-based mode for large playlists, multiple providers, or memory-constrained environments
