Aruvi — Telegram Media Streaming Platform

Stream your Telegram media files (videos, audio) to any browser or Android TV using multi-bot parallel streaming with intelligent caching.

Architecture

Browser/TV App → Cloudflare Tunnel → FastAPI (uvicorn) → PyroTGFork clients → Telegram MTProto
                                    ↕
                         Sliding Window Cache (500MB RAM global)
                                    ↕
                          NVMe Disk Cache (13GB, 3h TTL)

Key Components

Component	Description
FastAPI backend	REST API for auth, file listing, streaming
14 Telegram bots	1 main bot + 13 helper bots for parallel chunk fetching
StreamCache	Position-aware sliding window: 300MB fwd + 100MB back per stream
CacheManager	Global 500MB RAM limit across all streams, spills to NVMe
Disk cache	All chunks persisted to NVMe (`data/chunks/`), 3h TTL, 13GB max
Status monitor	Live dashboard at `monitor.aaruvi.space`

Streaming Pipeline

All-bot warmup — all 14 bots fetch messages 1-20 at startup
Fast-start — first 13 chunks as 1-chunk batches across all 13 helpers
Batch fetch — remaining chunks in parallel (BATCH_SIZE=5 per bot)
Sliding window — 300MB ahead / 100MB behind stays in RAM; rest to NVMe
100MB lookahead — maintains cushion against Telegram latency spikes
Global OOM guard — evicts farthest chunks across all streams at 500MB

Deployments

HidenCloud (current)

3GB ARM64, 15GB NVMe. Accessible via Cloudflare Tunnel:

Domain	Service
`REDACTED_DOMAIN`	TelePlay web player
`REDACTED_DOMAIN`	opencode Web UI (debug)
`monitor.aaruvi.space`	Status dashboard

# HidenCloud runs: python /home/container/run.py
# Fresh git clone on every restart:
git clone https://github.com/Thirupathi-pirate/Aruvi-backend.git code/repo

.env:

TELEGRAM_API_ID=
TELEGRAM_API_HASH=
TELEGRAM_BOT_TOKEN=
TELEGRAM_STORAGE_CHANNEL_ID=
TELEGRAM_HELPER_BOT_TOKENS=token2,token3,...,token14
DATABASE_URL=postgresql+asyncpg://...
TUNNEL_TOKEN=

Daily exit at 3:30 AM IST — fresh IP on restart.

Hugging Face Spaces (migrating)

Deployed via Docker:

docker build -t aruvi-backend .
docker run -p 7860:7860 \
  -e TELEGRAM_API_ID=... \
  -e TELEGRAM_API_HASH=... \
  -e TELEGRAM_BOT_TOKEN=... \
  -e TELEGRAM_STORAGE_CHANNEL_ID=... \
  aruvi-backend

Env vars specific to HF Spaces:

Variable	Required	Notes
`CLOUDFLARE_WORKERS_TOKEN`	no	Auto-deploys CF Worker proxy for `api.telegram.org`
`CLOUDFLARE_PROXY_URL`	no	Pre-existing proxy URL (skip auto-setup)
`CLOUDFLARE_PROXY_SECRET`	no	Shared secret for proxy auth
`APP_START_CMD`	no	Default: `uvicorn app.main:app --host 0.0.0.0 --port 7860`

Key Design Decisions

BATCH_SIZE=5 — 5 × 1MB chunks = 5MB per bot batch; reduced from 10 for faster first-byte
Global 500MB RAM — not per-stream; prevents OOM with multiple concurrent streams
All chunks to NVMe — both ahead and behind chunks persist; enables instant rewind without Telegram refetch
Per-bot fresh Message — each worker fetches its own get_messages() to avoid cross-bot FILE_REFERENCE_INVALID
Sentinels for shutdown — concurrency None tuples on task queue signal workers to stop
reconnect_client uses start() — gets new auth key on AuthKeyUnregistered, not just connect()

Tech Stack

Backend: Python 3.11, FastAPI, SQLAlchemy async, Kurigram (Pyrogram fork)
Database: PostgreSQL (Supabase) or SQLite
Cache: In-memory + NVMe disk with 3h TTL
Tunnel: Cloudflare Tunnel (cloudflared) + Cloudflare Workers proxy
Frontend: React (pre-built, served as static files)
Platform: HidenCloud ARM64 container / Hugging Face Spaces

License

MIT

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