Open-source comparison

GSP vs sqlglot: which SQL parser fits your workload?

sqlglot is a first-class open-source Python parser and transpiler — if you work in Python and need to convert SQL between dialects, use it. General SQL Parser (GSP) is a commercial Java/.NET SDK for the workloads sqlglot doesn't target: fully parsed stored procedures, dynamic SQL evaluation, 38 dedicated dialect grammars, and column-level lineage across entire enterprise codebases.

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Short Answer

Choose sqlglot when you are in Python and your SQL is queries: parsing, rewriting, and especially transpiling between dialects. Choose GSP when your product is Java or .NET, when your SQL includes stored procedures or dynamic SQL, when you need dialects sqlglot doesn't cover (Db2, Informix, Netezza, Sybase, HANA, Vertica, Dameng, OceanBase, Power Query M), or when lineage accuracy across procedural code is a requirement with a commercial support path behind it.

Comparison table

CapabilitysqlglotGSP
Language / runtimePython (optional Rust tokenizer)Java (JDK 8+) and .NET (.NET Standard 2.0)
LicenseMIT open sourceCommercial with support SLA and 90-day free trial
Dialect-to-dialect transpilationYes — the headline feature (e.g. Snowflake → DuckDB)No. GSP parses, validates, formats, rewrites, and analyzes — it is not a transpiler
Dialects~30 dialects, one shared grammar with per-dialect overrides38 dialects, each with its own dedicated grammar — including IBM Db2, Informix, Netezza, Sybase, SAP HANA, Vertica, Couchbase N1QL, Salesforce SOQL, GaussDB, Dameng, OceanBase, and Power Query M
Stored procedures / procedural SQLProcedural blocks (PL/SQL packages, T-SQL blocks, DB2 SQL PL) generally fall back to opaque Command nodesFully parsed ASTs: PL/SQL packages, types, and triggers; T-SQL blocks, TRY/CATCH, EXECUTE; DB2 SQL PL cursors; Teradata procedures and BTEQ
Dynamic SQLNot evaluatedEvaluated — GSP constant-folds EXEC('...') strings and parameter bindings to recover lineage from dynamic SQL
Column-level lineageLineage module for queries; used by DataHub for query-based lineageLineage engine covering queries, stored procedures, dynamic SQL, and multi-statement scripts, with impact analysis and call graphs (the engine behind Gudu SQLFlow)
Syntax validationBest-effort (tolerant parser by design)Offline validation with detailed error positions, no database connection required
SQL formattingpretty output flagDedicated formatter with 72+ configurable style options
Tests / releasesActive open-source project, frequent releases12,000+ unit tests, releases roughly weekly, 20+ years of development
Support for edge casesGitHub issues, community-drivenCommercial support: send failing SQL, get a fix in a scheduled release

When sqlglot is the right choice

  • Your codebase is Python and you want an in-process library with no license cost.
  • You need to transpile SQL between dialects — sqlglot's core strength.
  • Your SQL is mostly queries (SELECT/INSERT/CTAS), not procedural code.
  • Your dialects are in its supported set (Snowflake, BigQuery, Spark, DuckDB, Postgres, T-SQL, and more).

When GSP is the better fit

  • Your product runs on the JVM or .NET and needs an in-process parser.
  • Stored procedures, packages, triggers, or dynamic SQL carry your transformation logic.
  • You need Db2, Informix, Netezza, Sybase, HANA, Vertica, Teradata BTEQ, N1QL, SOQL, GaussDB, Dameng, OceanBase, or Power BI's M language.
  • Column-level lineage must survive procedures and EXEC() strings, not just queries.
  • Enterprise edge cases need a vendor fix, not a GitHub issue queue.

Evaluation checklist

Test both with the SQL that actually breaks parsers

  1. Pull 20–100 real statements from your target systems — include your longest stored procedures and any dynamic SQL.
  2. Parse them with sqlglot first: count how many procedure bodies come back as opaque commands or raise errors.
  3. Mark the outputs you need: AST detail, validation errors, table/column extraction, or column-level lineage.
  4. Send the failures and edge cases to Gudu — evaluation feedback on real SQL is part of the trial.
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Common questions

Is sqlglot a bad choice?

No — sqlglot is an excellent open-source project and the right default for Python teams, especially for transpiling SQL between dialects. This page is about the workloads where a specialized commercial parser is the better tool: stored procedures, dynamic SQL, Java/.NET products, and dialects sqlglot does not cover.

Does GSP transpile SQL between dialects like sqlglot does?

No. GSP is not a transpiler. If dialect-to-dialect conversion is your primary need, sqlglot or jOOQ are better starting points. GSP focuses on parsing depth: full ASTs for procedural code, validation, formatting, rewriting, and column-level lineage.

Can I use sqlglot from Java or C#?

Only by embedding a Python runtime or calling a service. sqlglot is a Python library. GSP ships native Java and .NET editions, so it runs in-process in JVM and .NET applications with no Python dependency.

Why do stored procedures matter for lineage?

In enterprise warehouses much of the transformation logic lives in stored procedures and dynamic SQL, not standalone queries. A parser that treats a procedure body as one opaque command cannot see the INSERT/SELECT statements inside it, so lineage extracted from procedures silently drops edges. GSP parses procedure bodies into full ASTs and evaluates dynamic SQL strings, which is why metadata platforms use it to fill exactly this gap.

Can GSP and sqlglot be used together?

Yes, and teams do this: keep sqlglot for Python-side transpilation and query-level work, and route stored procedures, unsupported dialects, or failed parses to GSP — for example as a lineage sidecar for DataHub or OpenMetadata.