PostHog

# MISSION: Scaffold "PostHog" Production Architecture
You are an expert Senior Staff Software Architect and Full-Stack Engineer. Your mission is to scaffold and implement a production-grade, highly reliable, and modular codebase following the proven architecture of **PostHog**.
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## 1. PROJECT SPECIFICATIONS & BENCHMARK
- **Reference Architecture**: PostHog
- **What It Does**: PostHog is an open-source product engineering platform combining web analytics, session replay, feature flags, A/B testing, and AI observability.
- **Domain & Category**: Product Analytics Platform
- **Production Scale**: 20+ people
- **Development Mode**: HYBRID
- **Architectural Rationale**: Pairing Apache Kafka with ClickHouse columnar storage enables PostHog to ingest billions of monthly events while maintaining sub-second query response times.
- **Live Website Reference**: https://posthog.com
- **Source Repository**: https://github.com/PostHog/posthog
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## 2. PRODUCTION TECH STACK
- **Full Stack Array**: Django, Python, React, TypeScript, ClickHouse, PostgreSQL, Kafka, Redis, Docker, Rust, Temporal
- **Primary Language(s)**: Python, TypeScript, Rust
- **License of the reference repo**: NOASSERTION
- **Frontend**: React — Powers the analytics dashboards, session replay player, and experimentation UI.; TypeScript — Ensures strict type-checking across frontend apps, plugins, and shared schema contracts.; Kea — Redux-based state management framework developed by PostHog for orchestrating complex UI logic.
- **Backend & APIs**: Django — Python REST API framework handling authentication, organization management, and billing.; Python — Primary language for the core API, background workers, and analytical query compilers.; Rust — High-throughput event ingestion pipelines and parsing services compiled in rust/Cargo.toml.; Temporal — Coordinates long-running background tasks, data warehouse syncing, and migration jobs.
- **Data & persistence**: ClickHouse — Columnar database storing billions of events, session recordings, and HogQL telemetry.; PostgreSQL — Relational database storing teams, feature flag configurations, user roles, and dashboard metadata.; Kafka — Distributed messaging log buffering incoming event streams before batching into ClickHouse.; Redis — In-memory caching layer, rate limiter, and Celery task broker.
- **Infrastructure & deploy**: Docker — Multi-stage Dockerfile packaging the web application, workers, and background processors.
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## 3. KEY ARCHITECTURAL DECISIONS (audited from https://github.com/PostHog/posthog @ ceedeea)
1. **Ingestion decoupled from persistence via Kafka and Rust capture workers**: Incoming event batches are captured by high-throughput Rust services and pushed to Apache Kafka topics, isolating web-tier ingestion spikes from database write pressure.
2. **Bifurcated storage model: ClickHouse for telemetry, PostgreSQL for metadata**: PostgreSQL manages tenant accounts, feature flag toggles, team memberships, and dashboard layouts, while ClickHouse executes columnar aggregations over billions of events.
3. **Custom SQL dialect (HogQL) compiled directly to ClickHouse SQL**: PostHog created HogQL (common/hogql_parser/), a domain-specific SQL dialect with AST validation that translates safe user-defined queries directly into optimized ClickHouse SQL.
4. **Temporal workflow engine for complex multi-step background orchestration**: Long-running batch workflows, data warehouse synchronization, and cohort recalculations are orchestrated via Temporal rather than unbounded Celery worker chains.
5. **Kea state management for declarative Redux architecture**: The frontend in frontend/ uses Kea to organize business logic into typed state "logics" that encapsulate actions, reducers, and side effects without boilerplate Redux plumbing.
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## 4. NON-NEGOTIABLE ARCHITECTURAL GUARDRAILS
1. **Monorepo & Modular Separation**:
   - Structure as a Turborepo monorepo with strict package boundaries:
     - `apps/web`: Application UI, routing, layouts, and server endpoints.
     - `packages/ui`: Shared design tokens, CSS variables, and Radix UI primitive components.
     - `packages/db`: Database schemas, client singleton, declarative migrations, and seed scripts.
     - `packages/config`: Shared TypeScript, ESLint, and build configurations.
2. **Strict Type Safety & Zero `any` Policy**:
   - Enable `strict: true`, `noImplicitAny: true`, and `strictNullChecks: true`.
   - Validate ALL external inputs, API request bodies, and query parameters with **Zod** schemas before execution.
3. **Frontend & Rendering Guidelines**:
   - Isolate interactive UI state to leaf components. Keep core pages lightweight and performant.
4. **Design System & Aesthetics**:
   - Keep every color, radius, shadow and font in a single token file (CSS variables) and consume tokens everywhere; never hardcode hex values in components.
   - Prefer crisp 1px borders and one subtle shadow scale over blurry default shadows. Pair one sans-serif for body/headings with one monospace for tags, badges, metrics, and code.
5. **Data Layer & Reliability**:
   - Write declarative schema definitions with foreign keys, composite indexes on queried filters, and automated timestamp triggers.
   - Use connection pooling and prepared statements for serverless database execution.
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## 5. STEP-BY-STEP SCAFFOLDING ROADMAP
- **Phase 1: Workspace & Root Config**: Initialize package manager, monorepo configuration (`turbo.json`, `tsconfig.base.json`, `package.json`).
- **Phase 2: Database Schema & Client**: Set up the data layer (ClickHouse, PostgreSQL, Kafka, Redis): client, connection pool, models, and migration scripts.
- **Phase 3: Design Tokens & UI Primitives**: Build accessible `Button`, `Input`, `Card`, `Badge`, and layout wrappers inside `packages/ui`.
- **Phase 4: Core Application Routes & Handlers**: Implement primary authentication, user session handling, and application routes.
- **Phase 5: Quality Assurance & Build Verification**: Run `tsc --noEmit`, ESLint, Prettier, and smoke test suites to ensure zero compilation or runtime errors.
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## 6. EXECUTION INSTRUCTIONS
1. Review all specifications, architectural guardrails, and stack choices above.
2. Present the full monorepo directory tree structure.
3. Systematically generate the complete, production-ready codebase according to the 5-phase roadmap above — starting with the root workspace setup, followed by the database schema, UI design system package, and full-stack application routes until the repository is fully scaffolded and ready to run.