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@@ -111,15 +111,15 @@ The server connects to a Postgres database to build an in-memory schema cache co
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### Statement Processing Flow
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1. Input source code is split into individual SQL statements
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2. Each statement is parsed using libpg_query (via `pgt_query`)
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2. Each statement is parsed using libpg_query (via `pgls_query`)
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3. Statements are analyzed against the schema cache
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4. Results are cached and updated incrementally on file changes
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## Testing
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### Test Data Location
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- SQL test cases: `crates/pgt_statement_splitter/tests/data/`
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- Analyzer test specs: `crates/pgt_analyser/tests/specs/`
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- SQL test cases: `crates/pgls_statement_splitter/tests/data/`
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- Analyzer test specs: `crates/pgls_analyser/tests/specs/`
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- Example SQL files: `example/`, `test.sql`
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### Snapshot Testing
@@ -145,10 +145,10 @@ cargo insta review
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## Development Notes
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### Code Generation
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Many parser structures are generated from PostgreSQL's protobuf definitions using procedural macros in `pgt_query_macros`. Run `just gen-lint` after modifying analyzer rules or configurations.
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Many parser structures are generated from PostgreSQL's protobuf definitions using procedural macros in `pgls_query_macros`. Run `just gen-lint` after modifying analyzer rules or configurations.
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### Database Schema
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The `pgt_schema_cache` crate contains SQL queries in `src/queries/` that introspect the database schema to build the in-memory cache.
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The `pgls_schema_cache` crate contains SQL queries in `src/queries/` that introspect the database schema to build the in-memory cache.
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### Multi-Platform Support
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The project includes platform-specific allocators and build configurations for Windows, macOS, and Linux.
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