PostgREST is a production-grade Haskell web server that converts an existing PostgreSQL schema into a RESTful HTTP API. At startup it introspects PostgreSQL system catalogs to build a schema cache of tables, views, relationships, routines, and media handlers. Each request is parsed into an internal AST (ApiRequest), planned against the schema cache (Plan), compiled to parameterized SQL (QueryBuilder), and executed through the vendored Hasql PostgreSQL driver. The server supports full CRUD, RPC calls, resource embedding via foreign keys, filtering, ordering, pagination, JWT authentication, OpenAPI generation, and custom PostgreSQL-defined media types. Authorization is delegated to the database: PostgREST connects as an authenticator role that does SET ROLE to the JWT-specified role, so row-level security, grants, and triggers are enforced by PostgreSQL. The system runs as a single Haskell binary with lifecycle management, SIGUSR2/config reload through LISTEN/NOTIFY, a separate admin server exposing /live, /ready, /metrics, and schema cache dumps, and metrics via Prometheus. Build and packaging is Nix-based with static linking, Docker images, and cabal/stack support. CI runs integration suites against multiple PostgreSQL versions and includes load, memory, observability, and replica tests. Maintenance is active, with frequent releases and broad production use. The codebase demonstrates a clean separation between HTTP parsing, query planning, SQL generation, and database access. It is one of the few Haskell projects at this scale with deep PostgreSQL binary-protocol integration. The vendored Hasql driver and several standalone modules—media type parsing, HTTP Range handling, SIEVE cache, JSON path parsing—are extractable as reusable components for other Haskell services.
This repository is the personal blog and knowledge base of digoal, a prominent PostgreSQL DBA and database expert. It contains over a decade of blog posts (2010–2026) covering PostgreSQL internals, performance tuning, database architecture, and related topics, along with a large collection of images, PDFs, and other attachments. The blog is organized by year and month, with each post as a markdown file. The repository also includes a 'skills' directory that defines reusable AI agent skill definitions (SKILL.md files) for various tasks such as fact-checking, stock prediction, bug hunting, and course writing. These skills are structured with agent prompts, reference materials, and supporting scripts, making them extractable and reusable in AI-assisted workflows. The repo is actively maintained and serves as both a reference for database professionals and a library of AI agent capabilities.