Plausible Analytics is a production-grade, privacy-first web analytics platform implemented as a full-stack SaaS application. The backend is built with Elixir/Phoenix (LiveView, Ecto, Oban) and uses ClickHouse as the primary analytics data store alongside PostgreSQL for transactional metadata. The frontend is a React/TypeScript single-page application with Tailwind CSS, and a separate JavaScript tracker script is distributed via npm and self-hosted CDN. The system handles event ingestion, session management, real-time dashboards, goal/funnel tracking, billing/subscriptions via Paddle, team collaboration, SSO/SAML authentication, data imports from Google Analytics, and a public Plugins API. The codebase is dual-licensed (Community Edition and Enterprise Edition) with conditional compilation for EE features like consolidated views, customer support tools, and audit logging. The architecture is modular with clear contexts (stats, ingestion, billing, teams, auth, shields) and extensive use of Oban workers for background jobs, ConCache for distributed caching, and OpenTelemetry for observability. The repository demonstrates high engineering craftsmanship with comprehensive test coverage (ExUnit, Playwright, k6 load tests), well-structured abstractions (cache behaviour, rate limiter, write buffers, session balancer), and a clear separation of concerns between the Phoenix web layer, LiveView components, and the React dashboard.
This repository hosts the documentation website for the Home Assistant Community Store (HACS), built with mkdocs-material. It is a static site generator project, not a runtime application. The site is built from Markdown files with custom Python hooks for HTML tag modification, Plausible analytics integration, and shortcode processing. It includes a CI pipeline for linting and building, and uses dependabot for dependency updates. The codebase is well-structured for a documentation project, with clean, reusable hooks and thoughtful theme overrides. While it does not contain application logic or data pipelines, it demonstrates good software engineering practices in the context of documentation generation.