$ show ash-project/spark
Spark is a sophisticated Elixir library designed for building compile-time domain-specific languages (DSLs). It provides a structured, metaprogramming-driven framework for defining DSL extensions, entities, sections, transformers, verifiers, and persisters, and it automatically generates documentation, formatter support, IDE autocomplete (via ElixirSense), and test helpers. Spark is the foundation for all DSLs in the Ash framework, making it a critical, battle-tested component of the Elixir ecosystem. The library is implemented as a pure, reusable library with no runtime service or data pipeline; its primary input is Elixir source code and its output is augmented modules, generated docs, and introspection metadata. Architecturally, Spark exposes a clear extension pipeline: declarations (`use Spark.Dsl.Extension`) define sections and entities, transformations and verifications run at compile time, and persisted state is stored via process-bound keys and metadata. The library also includes a builder API (`Spark.Builder`) for declarative DSL schemas, an options validation system (`Spark.Options`) forked from NimbleOptions with enhanced typespec and documentation generation, and utility modules for formatting, cheat sheet generation, and source annotation tracking. Key entry points include `Spark.Dsl.Extension`, `Spark.Dsl`, `Spark.InfoGenerator`, and the testing helpers in `Spark.Test`. External dependencies are minimal (ExDoc, ElixirSense, Sourceror), with conditional compilation to support version variations. Spark’s design emphasizes elegance and reusability: the DSL metacontract (entities, sections, transformers, verifiers) is a proven pattern for anyone building a domain-specific language in Elixir. The codebase is well‑structured, heavily tested, and accompanied by comprehensive documentation and tutorials. However, it relies on deep metaprogramming and a vendored options library, which introduces some complexity and drift risk. The project is actively maintained, with frequent releases and a low issue count, and the tooling it provides (formatter, autocomplete, test capture) is exceptionally useful for DSL authors. While not a data processing tool, it is an exemplary model of how to build a robust developer-facing library.
The LLM identified 4 files worth highlighting for reuse:
Defines the heart of Spark's DSL framework: the Extension behaviour and the compilation orchestration pipeline that runs transformers, persisters, and verifiers on a DSL module. It provides reusable macros and functions to define DSL sections, entities, and options, and handles module creation, metadata persistence, and error aggregation. This module is essential for anyone creating a DSL in Elixir and is highly reusable as a stand‑alone pattern for building compile‑time language extensions.
A self‑contained schema validation system (forked and enhanced from NimbleOptions) that supports complex types, defaults, required fields, and produces both human‑readable documentation and Elixir typespecs from the schema. It can be extracted independently to validate options in any Elixir library, demonstrating a clean pattern for runtime and compile‑time option handling with rich introspection support.
Implements an ElixirSense plugin that provides autocomplete suggestions for DSL entities and options while editing Elixir source. It uses Spark's metadata and source annotations to deliver context‑aware completion, and it is designed to be used by any Spark‑based DSL. This module showcases how to integrate a metaprogramming‑driven library with developer tooling, and the pattern is reusable for other IDE plugins or static analysis tools.
Provides testing utilities (`dsl_errors/1`, `assert_dsl_error/2`, `dsl_warnings/1`, `refute_dsl_errors/1`, etc.) that capture compile‑time DSL verifier output via a process dictionary flag, enabling deterministic testing without stderr interception. This module is a reusable pattern for testing any metaprogramming framework that performs compile‑time validation, and it adds a high‑quality, DX‑friendly testing layer to Spark.