**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.
New Features
- Automatic provider failover & rotation across multiple provider URLs with a provider:// scheme and capped retries
- Configurable failover redirect patterns to influence retry/failover behavior
UI
- New interface to view and edit failover redirect patterns in reverse proxy configuration
- Expanded docs covering Provider Failover & Rotation and enriched messaging templating
Reliability
- Improved URL resolution, error reporting and logging for more robust provider handling
B+ tree now supports a multi-block layout to handle records that exceed a single block.
Additionally, a larger safety factor was added to the overhead calculation to reduce the risk of data overflow.
Added in-place update for values if they fit in the block without rewriting the whole tree structure.
Implemented an LRU Block Cache (Node Cache) for the query engine.
Implemented zero copy key lookup
Added --dbv for printing id_mapping.db files
Increased initial load time with mmap
* Season Order fix
* fixed episode id problem
* fixed path prefix for user bouquet editor
* fixed css styling for mobile size
* fixed disabled input config
* cargo clippy fixes
* 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
Aliases can now be individually enabled or disabled to manage active sources.
Bug Fixes
Improved channel identification accuracy in XMLTV outputs.
Style
Disabled aliases display with strikethrough text for better visibility.
fixed some logs, added input name
epg short response hos now no errors field
added missing fields for reverse_proxy and library config in ui
fixed button styling
* 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
