spotify-automation-playlist-analytics

This project is a production-grade Spotify automation system for legitimate playlist management and music analytics. It automates playlist curation, scheduled updates, and performance tracking using the Spotify Web API with compliant rate limiting and secure OAuth sessions.

  

           

Created by Appilot, built to showcase our approach to Automation!
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Introduction

Managing playlists and tracking performance across releases is repetitive and easy to neglect when you’re running campaigns or multiple playlists. This system automates playlist operations (create/update/reorder), keeps your catalogue organised, and provides a lightweight dashboard to monitor playlist health and engagement trends—without relying on unsafe “botting” behaviors.

Why Spotify Automation Matters

• Keeps playlists fresh with scheduled curation updates
• Tracks performance signals in one place for faster decisions
• Reduces manual work for artists, labels, and curators
• Operates safely using OAuth, quotas, and API limits

Core Features

Feature	Description
Playlist Automation	Create, update, reorder, and maintain playlists based on rules (new releases, genre buckets, mood tags).
Scheduled Playlist Updates	Runs timed jobs to rotate tracks, refresh ordering, and maintain consistent playlist cadence.
Track & Artist Insights	Collects track, artist, and playlist metadata for reporting and trend analysis.
Audience-Friendly Dashboards	Displays playlist growth signals and change history in a simple UI.
OAuth Session Management	Secure token handling with refresh workflows and scoped permissions.
Rate Limiting & Backoff	Implements request pacing and automatic retries to respect API thresholds.
Audit Logs	Logs all playlist changes with who/what/when for traceability.

How It Works

Trigger / Input	Core Automation Logic	Output / Action	Safety Controls
Playlist rules	Parse rules for each playlist (sources, filters, rotation)	Playlist plan generated	Rule validation
Scheduled run	Execute update jobs on a schedule	Playlist updated	Rate limiting, backoff
Metadata sync	Pull track/playlist stats	Dashboard data refreshed	API quota safeguards
Manual override	Admin edits a playlist plan	Updated configuration applied	Access control
Logging	Record every change	Audit trail stored	Immutable logs

Tech Stack

• Backend: Python (FastAPI)
• Spotify Integration: Spotify Web API (OAuth 2.0)
• Scheduler: Celery + Redis
• Database: PostgreSQL
• Dashboard: React (admin panel + charts)
• Deployment: Docker + CI/CD pipeline

Directory Structure Tree

spotify-automation/
    api/
        routes.py
        auth.py
        spotify_client.py
    automation/
        playlist_rules.py
        playlist_updater.py
        scheduler.py
        rate_limiter.py
    analytics/
        metrics_collector.py
        aggregations.py
    dashboard/
        app.py
        components/
            PlaylistOverview.js
            ChangeHistory.js
            TrendsPanel.js
    config/
        settings.yaml
        playlist_rules.yaml
    data/
        logs/
            playlist_changes.csv
    scripts/
        run_worker.py
        run_scheduler.py
    requirements.txt

Use Cases

• Curators use it to refresh playlists automatically, so they can keep content current without daily manual work.
• Artists use it to track playlist placement and trends, so they can adjust release promotion decisions.
• Labels use it to manage multiple playlists and catalogue buckets, so they can standardise curation across teams.
• Marketing teams use it to schedule playlist updates around campaigns, so they can align timing with launches.

FAQs

Q: Does this automate streaming or inflate plays?
No. It only automates playlist management and analytics via the official Spotify Web API.

Q: What permissions are required?
OAuth scopes depend on features (e.g., playlist-modify-private/public, playlist-read-private).

Q: Can it manage multiple Spotify accounts?
Yes, with separate OAuth tokens and per-account rate limits.

Q: How are API limits handled?
The system uses pacing, exponential backoff, and job queues to stay within thresholds.

Performance & Reliability Benchmarks

• Playlist update latency: 30–120 seconds per playlist run (rule complexity dependent)
• Success rate: 93–95% per scheduled run (network/API variance)
• Scalability: 1,000+ playlists/day per node with queued workers
• Resource usage: ~150–350 MB RAM per worker, low CPU outside sync windows
• Recovery behavior: automatic retries with backoff, idempotent updates, and resumable jobs