datalog-dsl-design

To compile Datalog rules to SQL for static analysis, use a compiler pipeline that parses rules, resolves built-ins, type checks, stratifies, and emits SQL. This allows querying code metadata via a SQLite backend.

Embedding regex, AST, and file glob patterns in a Datalog DSL involves using built-in relations like match and file. These built-ins integrate foreign patterns directly into rules for querying source code metadata.

Yes, you can use a Souffle-like rule language with a SQLite backend by designing a DSL that translates Datalog rules into SQL. This setup supports incremental materialization using INSERT OR IGNORE strategies.

Incremental evaluation in a Datalog to SQL compiler works by using an INSERT OR IGNORE strategy during materialization. This efficiently updates derived relations without recomputing the entire dataset.

Analyzing code metadata with Datalog requires built-in relations such as file, match, link, and repo. These relations map to a SQL schema and support a modular extraction pipeline for source code patterns.