* 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
