* docs(plans): root-cause analysis for endpoints still slow after PR #292 Five endpoint groups stayed slow after the home/Continue Watching/Latest latency work shipped: Resume (110s p95), NextUp (17s p95), Latest (17s), /Items, and the home sections routes. The caps and caches from PR #292 are live in the deployed binary; they bounded how many rows the loops touch but not what each underlying query costs. Documents the four confirmed root causes (4.3M stale completed-with-position progress rows + missing resume index, unbounded next-up anchor scan, per-episode series rollup fanout, two index-starved history/scanner paths) with live EXPLAIN ANALYZE measurements and the fix plan implemented by the follow-up commits. AI-use disclosure: analysis and doc produced with AI (Claude) assistance. * perf(catalog): bound the global next-up anchor scan to recent completions The completed_episodes CTE in buildListNextUpQuery derived per-series anchors from the profile's ENTIRE completed history — DISTINCT ON over 233k rows joined to episodes for the worst bulk-import profile, then a per-series LATERAL that scans every episode of a fully-watched series before yielding nothing. 648 slow executions in a 19h window, 44.7s worst; this drove /Shows/NextUp (17.1s p95) and the next-up injection on the native home sections aggregate. Global queries now derive anchors from the profile's nextUpAnchorMaxRows (500) most recent completed rows — an ordered index walk on idx_uwp_profile_completed, with the hidden-items exclusion and date cutoff applied inside the bounded scan so hidden/old rows never consume the anchor budget. A next-up rail surfaces ~24 series; the 500 most recent completions cover every series that can realistically rank on it. Series-scoped calls (the show-detail tile) keep the unbounded shape: they must anchor on the series' last completed episode no matter how long ago it was watched, and are naturally bounded by one series. Measured on the live worst-case profile with the exact generated SQL: 44.7s worst / ~2.6s avg before; 10ms after (together with the one-time stale-resume-point data repair applied directly to the deployment DB — see docs/superpowers/plans/2026-07-06-slow-endpoint-root-causes.md). AI-use disclosure: implemented with AI (Claude) assistance. * perf(jellycompat,userstore): aggregate series watch-state rollup in SQL The series Played/UnplayedItemCount badge on list rails (per-library Latest, library browse, search results) and series detail pages was computed by materializing EVERY episode of every series on the page (episodeRepo.ListBySeriesIDs) and then batching per-episode progress+history lookups in 500-id chunks. A 50-series page of an episode-heavy library (Sports) expanded to 32,467 episode rows and ~65 sequential queries — measured 17-18s per /Items/Latest request, and PR #292's cached Latest fast path pays it on every response for series libraries. The same fanout made /Items?searchTerm=... slow whenever the result set was mostly series (Meilisearch itself answers in milliseconds). New optional store capability userstore.SeriesEpisodeRollupStore, implemented by PostgresUserStore as one GROUP BY e.series_id aggregate with semantics identical to the chunked path (episode availability via episode_libraries, hidden-items visibility on progress rows, completed-history fold, in-progress = not watched with position > 0 — verified value-for-value against the old semantics on a real 1,586-episode series). enrichSeriesListUserData and enrichDetailUserData use it when present; SQLite-backed stores and rollup query failures keep the existing chunked path as fallback. catalog.SeasonUserDataFromCounts pins the counts-to-DTO mapping to EpisodeRollupUserData. Measured on the live worst-case profile against the real 50-series Sports Latest page: ~17s of chunked round-trips before, 119ms in one query after. Part of docs/superpowers/plans/2026-07-06-slow-endpoint-root-causes.md. AI-use disclosure: implemented with AI (Claude) assistance. * perf(catalog): bound superseded-episode completed walk to recent history The Resume / Continue Watching superseded-episode filter loaded a profile's *entire* completed history into memory on every request that contained an in-progress episode: CompletedProgressSnapshots paged user_watch_progress WHERE completed=TRUE with no upper bound. The 2026-07-06 slow-query comparison showed this surviving as a 60-116s Resume tail even after the in-progress index landed live, because the 4.3M zeroed Plex-import rows are still completed=TRUE and were re-walked every load. A completed episode can only supersede an in-progress one it was finished more recently than (the query gates on done_progress.updated_at > ip_progress.updated_at), so only completed rows newer than the oldest in-progress entry can matter. Compute that cutoff in SupersededEpisodeProgressIDs and pass it to CompletedProgressSnapshots, which — since the completed listing is ordered updated_at DESC — stops paging as soon as it crosses the cutoff. Import-heavy profiles whose back-catalogue predates their current in-progress items now stop on the first page instead of paging hundreds of thousands of irrelevant rows. Correctness is unchanged: no relevant superseding row is excluded. * perf(catalog): hard-cap superseded-episode completed walk at 5 pages The updated_at cutoff added in the previous commit bounds the completed walk on the relevance axis, but a very old in-progress entry sitting behind a large volume of newer completions could still page deep. Add a 5-page (2,500-row) hard backstop on top of the cutoff: normal profiles still stop on page one via the cutoff, and only the adversarial tail hits the cap. When it engages the tail of the completed set goes unscanned, so a superseded episode could momentarily survive on Continue Watching — we log a warning when that happens (with profile_id + rows scanned) rather than mis-filter silently, and it self-corrects once the stale in-progress entry ages out of the scanned window. * perf(playback): extract subtitle fonts in a single ffmpeg pass Embedded ASS/SSA font extraction spawned one ffmpeg process per font attachment, each re-opening the (usually CephFS-backed) media file. Anime releases carry 15-47 fonts, so the per-spawn file-open cost dominated and pushed GET /api/v1/stream/{sid}/subtitles/{track}/fonts to a 17-60 s plateau (p95 ~33 s in the live logs). Collapse the N spawns into one ffmpeg invocation that dumps every attachment to a temp dir (-dump_attachment:idx path ... -i file -map 0:t? -c copy), then read the files back. The file is opened once instead of N times, taking p95 from ~30 s to ~1-2 s with no change to output. Safety is preserved. The 32-attachment / 32 MiB caps still apply: attachment size is stat'd before read so an over-limit font never enters memory, and a watchdog polls the dump dir and kills ffmpeg if its on-disk output crosses the cap -- restoring the hard bound the old pipe-per-attachment reader enforced by killing at maxBytes+1, so a container with oversized "font" attachments can't fill the disk. Part of the slow-endpoint follow-up; see slow-query-analysis/subtitle-fonts-extraction-findings.md. * fix(review): report enforced font-byte cap; correct doc subtitle scope Address PR #350 review: - dumpFontAttachments reported the maxSubtitleFontBytes package constant in both over-limit errors instead of the maxBytes argument the caller passed, so the message misstated the enforced bound whenever a different cap was in effect (as the tests use). Interpolate maxBytes in both messages. - The root-cause plan claimed subtitle extraction was 'out of scope' while the branch actually optimizes /subtitles/{track}/fonts. Scope the out-of-scope note to subtitle *track* conversion and record the fonts single-pass work as deliverable 5.
Silo
Silo is a self-hosted media streaming server for your movies, shows, music, and books. Point it at your media folders and stream to your devices — at home or away — with direct play, remuxing, and hardware-accelerated transcoding handled automatically.
Join the community on Discord. If Silo is useful to you, consider sponsoring the project — see Supporting Silo.
Highlights
- Plays your media, your way — direct play when the device supports it, remux or hardware-accelerated transcode (including NVENC) when it doesn't.
- Web app included — a full-featured web client and admin interface ship with the server.
- Works with apps you already use — optional Jellyfin/Emby-compatible API supports clients such as VidHub, Findroid, and Infuse.
- Household profiles — multiple profiles per account, with per-profile watch state and parental controls.
- Plugin-driven metadata — match and enrich your libraries with providers like TMDB and TVDB, installed as plugins.
- Fast setup — one
docker compose up -dbrings up the whole stack; everything else is configured in the admin UI.
Deploy with Docker (recommended)
The easiest way to run Silo is with Docker Compose. The default stack assumes you do not already have PostgreSQL and Redis available, so it bundles PostgreSQL, Redis, FFmpeg, and the application for a one-command start.
-
Create a
.envfilecp .env.example .env -
Set your media path
Edit
.envand set:MEDIA_ROOT=/path/to/your/mediaMEDIA_ROOTis the one value most users need to change. You can also overrideSILO_DATA_ROOTif you do not want bind mounts under/opt/silo, and change ports if the defaults conflict with something else on the host. -
Start the default integrated stack
docker compose up -dThis starts PostgreSQL, Redis, and the integrated Silo server. The app is available at
http://localhost:8090. Jellyfin-compatible app support is disabled until an administrator enables it in onboarding or admin settings.If you already have PostgreSQL and Redis available, omit those bundled service examples from compose and point Silo at your existing
DATABASE_URLandREDIS_URLinstead.Optional NVIDIA/NVENC
GPU support is kept out of the default compose file so hosts without NVIDIA drivers work unchanged.
Install the NVIDIA Container Toolkit and use a Docker Compose version with GPU reservation support before enabling this override.
Use the optional override file when you want NVENC:
docker compose -f docker-compose.yml -f docker-compose.nvidia.yml up -dIf you want this controlled from
.env, setCOMPOSE_FILE:COMPOSE_FILE=docker-compose.yml:docker-compose.nvidia.yml NVIDIA_GPU_COUNT=1Windows uses
;instead of:between compose files.Then
docker compose up -dwill include the NVIDIA override automatically. -
Configure through the admin UI
Add libraries, users, metadata providers, and playback settings from the web interface.
Bind Mount Layout
The deploy-oriented compose files use host folder mappings rather than Docker-managed volumes.
By default, data is stored under /opt/silo:
/opt/silo/postgres/opt/silo/redis/opt/silo/transcode/opt/silo/catalog-seeds
Media is mounted into the container at /mnt/media from the host path you set in MEDIA_ROOT.
Optional Profiles
The main compose file is integrated-first. These profiles exist for operators testing distributed mode or mirroring a split deployment shape. Most single-host installs should stay on the default integrated service, because it already includes proxying and transcoding.
| Profile | Command | Description |
|---|---|---|
| default | docker compose up -d |
Integrated server plus bundled PostgreSQL and Redis |
proxy |
docker compose --profile proxy up -d |
Start a standalone proxy service for distributed-mode testing |
transcode |
docker compose --profile transcode up -d |
Start a standalone transcode service for distributed-mode testing |
You can enable both optional examples together:
docker compose --profile proxy --profile transcode up -d
If you are splitting workers across multiple hosts, use the separate remote worker example instead of trying to stretch the main compose file across machines.
Advanced Remote Node Example
For a dedicated remote transcode worker, use docker-compose.remote-transcode.yml. That file is intended for a separate worker host that connects back to an existing Silo deployment using shared PostgreSQL and Redis.
Deployment Notes
The default compose stack intentionally bundles PostgreSQL and Redis for ease of setup and assumes a fresh install without those services already available. If you already operate PostgreSQL and Redis, omit those examples from compose and point Silo at your existing infrastructure instead. For serious installs, PostgreSQL is better on a separate VM or a managed service so upgrades, tuning, and backups are isolated from the app host. Redis can stay local for many installs, but externalizing it is also reasonable if you already operate shared infrastructure.
Silo is externally stateful by default rather than fully stateless. Durable application state lives in PostgreSQL. Redis only stores coordination and cache-style data. Silo still writes transient transcode output locally under /tmp/silo-transcode. If you switch userdb.backend=sqlite, Silo also becomes locally stateful at /var/lib/silo/userdb.
Migrating an existing Continuum Docker install should be done with the preflight helper and cutover guide in docs/continuum-to-silo-docker-migration.md.
Configuration
Silo requires only a DATABASE_URL when running from source or against external infrastructure. In the default Docker Compose path, the stack wires the database and Redis URLs for you. All other settings — libraries, metadata providers, transcoding, users — are managed through the admin UI after first launch.
Server Modes
| Mode | Description |
|---|---|
integrated |
Full server: API + frontend + scanner + transcode (default) |
api |
API server only, no local transcoding |
proxy |
Stream proxy node that connects to the shared deployment database and Redis |
transcode |
HLS transcode worker node that connects to the shared deployment database and Redis |
PostgreSQL Auto-Tuning
The default Docker Compose stack does not require a checked-in postgresql.conf.
It enables Silo's pgtune-style OLTP tuning
by default:
POSTGRES_TUNE: auto
When enabled, Silo connects with DATABASE_URL and applies recommendations with
ALTER SYSTEM, which writes to PostgreSQL's postgresql.auto.conf inside the
database data directory. Reloadable settings are applied immediately with
pg_reload_conf(). Settings that PostgreSQL marks as restart-only are written
too, and Silo logs the setting names so you can restart PostgreSQL once:
docker compose restart postgres
The default Compose database user has the required PostgreSQL permissions. If
you use an external PostgreSQL server, make sure the configured DATABASE_URL
user can run ALTER SYSTEM, or set POSTGRES_TUNE=off and manage
PostgreSQL yourself.
For POSTGRES_TUNE_MEMORY=auto, Silo uses the first trustworthy memory source:
a finite Docker cgroup limit, the read-only /host/proc/meminfo mount supplied
by the bundled Compose file, then /proc/meminfo with container safety guards.
Auto-detected memory is treated as a PostgreSQL budget, defaulting to 75% of
detected RAM so Silo, Redis, plugins, transcodes, and the OS retain headroom.
POSTGRES_TUNE_DB_SIZE=auto queries pg_database_size(current_database()) and
classifies the workload by comparing the database size to that memory budget.
Optional tuning overrides:
| Variable | Default | Description |
|---|---|---|
POSTGRES_TUNE_PROFILE |
oltp |
Tuning profile. Only oltp is currently supported. |
POSTGRES_TUNE_MEMORY |
auto |
Server/container RAM, such as 8GB or 32GB; explicit values are used as-is. |
POSTGRES_TUNE_MEMORY_BUDGET_PERCENT |
75 |
Percent of auto-detected RAM used for PostgreSQL recommendations. |
POSTGRES_TUNE_CPUS |
auto |
CPU count used for worker recommendations. |
POSTGRES_TUNE_STORAGE |
ssd |
One of hdd, ssd, san, or nvme. |
POSTGRES_TUNE_DB_SIZE |
auto |
Use less_ram when the database comfortably fits in RAM, mid_ram, or greater_ram for very large databases. |
POSTGRES_TUNE_CONNECTIONS |
100 |
PostgreSQL max_connections; automatically raised if Silo's app pool is configured higher. |
POSTGRES_SHM_SIZE |
8gb |
Docker /dev/shm size for the bundled PostgreSQL container. |
Advanced operators can still supply their own PostgreSQL configuration or
override these env vars. Set POSTGRES_TUNE=off when you do not want Silo to
change PostgreSQL server settings. Settings already written with ALTER SYSTEM
remain in postgresql.auto.conf; reset those PostgreSQL parameters if you later
move fully to a custom postgresql.conf.
Build from Source
If you prefer running Silo without Docker:
-
Install prerequisites: Go 1.24+, Bun 1.0+, PostgreSQL 18+, and FFmpeg.
-
Start PostgreSQL and Redis (skip if you already have them running)
docker compose up -d postgres redisThe main compose file still expects
MEDIA_ROOTto be set even if you only want the bundled PostgreSQL and Redis services, so set that in.envfirst. -
Configure the database connection
cp .env.example .envEdit
.envand setDATABASE_URLto point to your PostgreSQL instance. -
Build and run
make build ./siloThe server starts at
http://localhost:8080by default. All other settings are configured through the admin UI.
Reporting Issues
If you are reporting a bug, install problem, or performance issue, start with the admin workflow and reproduction steps, not Claude/Codex analysis.
Please include:
- What you were trying to do
- Exact steps you took
- What you expected to happen
- What actually happened
- What exact action is slow or broken (
save,scan,browse,import,playback, etc.) - Whether it happens every time or only sometimes
- The library, media type, filter, setting, or value involved
- Version, branch, commit, and deployment details if you know them
- Screenshots, recordings, or log snippets if relevant
If you used Claude/Codex for debugging, put that under Technical notes at the end. Suspected files, SQL output, stack traces, and root-cause theories can be helpful, but only after the workflow and repro steps are clear.
Use this template:
Goal:
Steps:
Expected:
Actual:
What is slow/broken:
Scope:
Version/branch:
Deployment:
Technical notes:
Contributing & Development
Silo is open source and contributions are welcome. See DEVELOPMENT.md for building from source in a dev workflow, running tests, database migrations, and project layout, and CONTRIBUTING.md for contribution expectations, merge request guidance, and the policy for AI-assisted submissions.
Supporting Silo
Silo is an open-source hobby project, developed in spare time and funded out of pocket. If you'd like to support development, you can sponsor via GitHub Sponsors.
Donations go directly toward the costs of building and running the project:
- AI development tooling subscriptions (Claude, Codex) used to build and maintain Silo
- Push notification relay infrastructure
- Future development costs
Sponsoring is entirely optional — Silo is and will remain free and open source. Bug reports, contributions, and feedback are just as valuable.
License & Trademarks
Silo's source code is licensed under the GNU Affero General Public License
v3.0 or later (AGPL-3.0-or-later) — see LICENSE.
The Silo name, logo, and wordmark are trademarks of Silo Media L.L.C. and are not covered by the AGPL. You're free to fork and redistribute the code, but forks and redistributions must not use the Silo brand as their identity and must remove or replace the brand assets. Publishing a Silo-branded app to an app store requires written permission. See TRADEMARK.md for what's permitted — including referential use like "compatible with Silo."