Stationarr v1.1.0 — Major Performance Update

Stationarr v1.1.0 is a major performance-focused release for large playlists and large XMLTV/EPG datasets.

This release makes Stationarr much more usable with very large channel lists by moving heavy editor and Guide work to server-side queries, background indexing, and lazy loading. It also adds new branding assets, favicon updates, and refreshed README documentation.

Highlights

- Major large-playlist editor performance improvements
- Server-side paginated channel editor
- Server-side channel search, group, and enabled/disabled filtering
- SQLite-backed TV Guide programme index
- Background Guide indexing after EPG updates
- Lazy-loaded Guide rows with automatic load-more on scroll
- Manual Load more fallback and batch-size controls
- 12h / 24h visible Guide windows
- Updated Stationarr branding assets and favicon
- Refreshed README documentation

Added

Large playlist editor performance

• Added server-side paginated channel loading for large playlists.
• Added page-size controls for the channel editor.
• Added server-side channel search.
• Added server-side group filtering.
• Added server-side enabled/disabled filtering.
• Added full-group enable/disable actions from the group sidebar.
• Added selected-row bulk operations that work cleanly with paginated views.

TV Guide performance

• Added a SQLite-backed Guide programme index.
• Added background Guide indexing after EPG fetch, upload, and refresh.
• Added startup backfill for existing cached EPG sources.
• Added Guide index status messaging for building and failed index states.
• Added server-side Guide search, group, and time-window queries.
• Added lazy-loaded Guide rows.
• Added automatic load-more on scroll.
• Added manual Load more fallback.
• Added Guide batch-size controls.
• Added 12h / 24h visible Guide windows.
• Added a cleaner two-bar Guide layout separating app navigation from Guide controls.

Branding and documentation

• Added Stationarr branding assets.
• Added downloadable logo assets.
• Updated favicon references to use the new branding package.
• Updated README hero branding.
• Added README documentation for the branding package.
• Updated README documentation for the new large-playlist editor and Guide performance features.

Improved

• Improved channel editor responsiveness by avoiding loading all channels into frontend state at once.
• Improved large playlist workflows with server-side filtering and pagination.
• Improved TV Guide responsiveness by querying indexed programme data instead of reparsing XMLTV files during Guide navigation.
• Improved Guide usability for large EPG datasets with lazy row loading and automatic load-more.
• Improved Guide layout clarity with separate app and Guide control bars.
• Improved documentation for large playlist workflows, Guide indexing, and branding assets.
• Improved app polish with official Stationarr branding assets.

Fixed

• Fixed scraper XMLTV mapping normalization issues where variant suffixes such as @SD could prevent programme data from loading.
• Fixed Guide sluggishness caused by large XMLTV/EPG datasets being too heavy to load and render all at once.
• Fixed Guide usability issues around loading large numbers of channel rows.
• Fixed outdated README references to older editor and Guide behavior.

Notes for existing users

The first Guide load after updating may take longer while Stationarr builds the new background Guide programme index from existing cached EPG sources.

Once indexing is complete, Guide browsing should be much faster.

You may see a Guide index status message while indexing is building or if an index fails. Check the in-app logs page for details.

Upgrade notes

If using Docker Compose:

docker compose pull
docker compose down
docker compose up -d

If using Docker directly:

docker pull rroy676/stationarr:1.1.0

Or use:

docker pull rroy676/stationarr:latest

Related work

- Large playlist editor performance
- Guide performance and indexing
- Branding package and favicon update
- README documentation refresh

Thank you

Thanks to the users testing large playlists and large EPG files. This release was shaped by real-world feedback around sluggish editor and Guide behavior with large datasets.