SQL Parser Solutions and Use Cases

Use General SQL Parser (GSP) when your application must understand SQL as a language instead of treating it as text. GSP selects a database-specific grammar, builds a typed abstract syntax tree (AST), and exposes the statements, clauses, expressions, tables, columns, and dependencies your workflow needs.

This page helps you decide where that capability fits in your product. When you are ready to implement a task, follow the linked guide and run the Java or .NET source project.

Choose a solution

You need to...	GSP provides...	Start with...
Check or control SQL before it reaches a database	Offline parsing, diagnostics, statement inspection, and AST rewriting	Safer SQL execution
Add SQL intelligence to an editor, CI job, or developer platform	Dialect-aware validation, formatting, and structural metadata	Developer tooling
Build a catalog, lineage, governance, or impact-analysis product	Table, column, dependency, and source-to-target relationships	Data governance
Assess or automate a database migration	Source-dialect parsing, syntax inventories, AST transformation, and join conversion	Migration and modernization

Safer SQL execution

Customer-facing analytics, report builders, database consoles, and AI-generated SQL workflows often receive statements from outside the application trust boundary. Passing that SQL directly to a database makes syntax errors, unsafe statement types, missing tenant filters, and prohibited object access database problems instead of application decisions.

Place GSP between SQL submission and execution. Your application can:

1. parse with the dialect that produced the SQL;
2. reject invalid input and return parser diagnostics;
3. inspect statement types, tables, columns, and predicates;
4. apply application-specific allow or deny rules;
5. modify the AST when a safe rewrite is required;
6. regenerate and parse the result again before passing it onward.

The result is an explicit allow, reject, or rewritten SQL outcome that your application can log and test. Parsing does not replace database authorization, parameter binding, resource limits, or transaction controls; it adds a structural policy layer before those protections.

Start with offline SQL validation and safe AST modification. Both guides link to paired Java and .NET projects.

Developer tooling and CI

SQL editors, IDE extensions, linters, review bots, and CI pipelines need fast feedback without executing the submitted SQL. Text matching can find style patterns, but it cannot reliably distinguish a keyword from a quoted identifier, a table reference from a comment, or an expression in a nested query from one in the outer statement.

GSP can run in the editor process, a language service, a build step, or an API worker. The parser turns each file or statement into structured results that the surrounding tool can convert into:

• line, column, token, and statement-level syntax diagnostics;
• a pass/fail build result for the selected dialect;
• formatted SQL for review or generated-query logs;
• tables, columns, clauses, and functions for navigation or custom rules;
• a parse tree for visualization and debugging.

Use the offline validation guide for editor and CI diagnostics, the formatter guide for consistent output, or open SQL Playground to inspect a parse tree interactively.

Data governance and impact analysis

Catalogs and governance platforms need to answer two related questions: "Where did this data come from?" and "What can break if this object changes?" A list of matching names is not enough because aliases, expressions, CTEs, views, stored procedures, and INSERT ... SELECT statements define how data moves.

GSP first parses each source with the correct dialect, then its analysis layer resolves table and column relationships. A product can use the resulting model to:

• enrich a catalog with referenced and written database objects;
• connect target columns to source columns across transformations;
• find downstream reports, views, jobs, or data products before a schema change;
• review access to sensitive objects based on real SQL references;
• document ETL and analytics code that no longer has reliable metadata.

Start with table and column extraction for an inventory, then use column-level data lineage for source-to-target relationships. Review the validation reports to understand measured behavior and documented gaps on real SQL projects.

Database migration and SQL modernization

A file count does not show how difficult a migration will be. Teams need to know which source dialects, statement types, procedural blocks, proprietary joins, functions, and database-specific clauses are present before they can estimate or automate the work.

Run GSP over the legacy SQL with the source database selected. The resulting AST can drive an inventory, identify constructs that require a conversion rule, and provide stable targets for structured rewrites. After a transformation, parse the generated SQL with the intended target dialect and route failures or unsupported constructs for review.

This workflow supports Oracle-to-PostgreSQL assessments, SQL Server or Teradata modernization, stored-query remediation, and schema refactoring. It does not claim that parsing alone provides a complete semantic migration: database behavior, data types, functions, transaction semantics, and runtime plans still require target-system validation.

Use the SQL dialect reference to compare source coverage, the AST rewrite guide for structured transformations, and the join conversion examples for legacy outer-join notation.

How GSP fits into your architecture

flowchart LR
    A[SQL from users, files, tools, or repositories] --> B[Select the source SQL dialect]
    B --> C[GSP parser and typed AST]
    C --> D{Application goal}
    D --> E[Validate or enforce policy]
    D --> F[Extract metadata or lineage]
    D --> G[Format or transform SQL]
    E --> H[Diagnostics, allow, or reject]
    F --> I[Catalog, graph, or report]
    G --> J[Regenerated and revalidated SQL]

GSP owns the language-understanding step. Your product remains responsible for business policy, metadata context, permissions, user experience, and any database execution. That separation lets the same parser capability support a desktop tool, build pipeline, server API, or embedded product without coupling analysis to a live database.

Install GSP with the Quick Start Find a task guide and source code