* fix: Scheduler preserve forced_targets across config reloads.
hot reload could cause loosing scheduler task targets.
warn logs for:
- process_source: warns when all inputs are disabled for given targets
- process_sources: warns when no sources were processed at all
* Fix: Logger initialization
* Fix: TCP USER TIMEOUT for linux. If a client disrupts the connection, the tcp layer will hold it approx for 5-15 minutes. With this setting we can close in 30 seconds under linux
* Fixed issues with probe and resolve tasks
* hls and catchup session fixes
* fixed problem with grace and shared stream
- added fix for missing episode title
* No template file not found error message
* Setup wizard improvmenets
* path improvements
* staged input fix
* path improvements
* Provider dns resolved ips now writing to a file instead of source.yml
* Playlist update hard kill after 1h
* matadata update tmdb fix
* Web UI fixes
* GeoIp update schedule
* batch:// prefix for aliases
Fixed m3u playlist with provider_config persistence as text file
Added probe_delay input option (default 2s); stream probing renamed “Stream Probing” and now covers live, VOD and series as a low‑priority background task.
**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.
* Fixed series sort issues
* Playlist explorer copy to clipboard functons implemented
* Epg time fix
* grace period hold stream option added.
* Fixed group and mapper normalization
* Image cache validity for browsers set to 4 hours. Responding now last modified and cache header.
* New Features
Added for_each DSL construct for iterating over playlist item fields, with split() function support.
Added "genre" as a queryable and editable playlist item field.
Bug Fixes
Improved short EPG request handling with automatic default limits.
Fixed EPG timestamp formatting to use UTC consistently.
* New Features
Added series information display with episodes, cast, and metadata in playlist explorer
Introduced multi-cluster playlist endpoints (live, VOD, series) for flexible content retrieval
Added series info lookup and filtering capabilities
Implemented sidebar navigation for source editor with dedicated pages (Main, Options, Staged, Advanced, Alias)
Added filter state controls (All, Selected, Deselected) for playlist category management
Enabled Trakt configuration toggle to control integration
Performance Improvements
Optimized B+Tree indexing for faster queries
Reduced startup latency with disk-based operations and optional memory caching
Improved string memory handling through efficient reference-counted strings
UI Enhancements
Enhanced playlist explorer with series details and episode grouping by season
Added new UI icons (Options, Advanced, Staging, Alias, Trakt)
Improved form layouts with toolbar controls
Enhanced radio button group styling for better visual hierarchy
* New Features
Added series information display with episodes, cast, and metadata in playlist explorer
Introduced multi-cluster playlist endpoints (live, VOD, series) for flexible content retrieval
Added series info lookup and filtering capabilities
Implemented sidebar navigation for source editor with dedicated pages (Main, Options, Staged, Advanced, Alias)
Added filter state controls (All, Selected, Deselected) for playlist category management
Enabled Trakt configuration toggle to control integration
Performance Improvements
Optimized B+Tree indexing for faster queries
Reduced startup latency with disk-based operations and optional memory caching
Improved string memory handling through efficient reference-counted strings
UI Enhancements
Enhanced playlist explorer with series details and episode grouping by season
Added new UI icons (Options, Advanced, Staging, Alias, Trakt)
Improved form layouts with toolbar controls
Enhanced radio button group styling for better visual hierarchy
* New Features
Added series information display with episodes, cast, and metadata in playlist explorer
Introduced multi-cluster playlist endpoints (live, VOD, series) for flexible content retrieval
Added series info lookup and filtering capabilities
Implemented sidebar navigation for source editor with dedicated pages (Main, Options, Staged, Advanced, Alias)
Added filter state controls (All, Selected, Deselected) for playlist category management
Enabled Trakt configuration toggle to control integration
Performance Improvements
Optimized B+Tree indexing for faster queries
Reduced startup latency with disk-based operations and optional memory caching
Improved string memory handling through efficient reference-counted strings
UI Enhancements
Enhanced playlist explorer with series details and episode grouping by season
Added new UI icons (Options, Advanced, Staging, Alias, Trakt)
Improved form layouts with toolbar controls
Enhanced radio button group styling for better visual hierarchy
* New Features
Added series information display with episodes, cast, and metadata in playlist explorer
Introduced multi-cluster playlist endpoints (live, VOD, series) for flexible content retrieval
Added series info lookup and filtering capabilities
Implemented sidebar navigation for source editor with dedicated pages (Main, Options, Staged, Advanced, Alias)
Added filter state controls (All, Selected, Deselected) for playlist category management
Enabled Trakt configuration toggle to control integration
Performance Improvements
Optimized B+Tree indexing for faster queries
Reduced startup latency with disk-based operations and optional memory caching
Improved string memory handling through efficient reference-counted strings
UI Enhancements
Enhanced playlist explorer with series details and episode grouping by season
Added new UI icons (Options, Advanced, Staging, Alias, Trakt)
Improved form layouts with toolbar controls
Enhanced radio button group styling for better visual hierarchy
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
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
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
