About SQL Parser

Parse SQL queries and extract structure (tables, columns, joins). Part of the DevTools Surf developer suite. Browse more tools in the Developer Utilities collection.

Use Cases

• Extract all table names referenced in a query to build a data lineage graph.
• Identify JOIN conditions to verify that all joins have proper constraints.
• Parse legacy stored procedures to understand their structure before migration.
• Validate that dynamically generated SQL conforms to expected structural patterns.

Tips

• Use the parsed AST to extract table and column references for dependency mapping before running a schema migration.
• Detect CROSS JOINs and unconstrained subqueries from the parse tree — they are common sources of unintentional Cartesian products.
• Compare parsed structure between two logically equivalent queries to verify rewrites are semantically identical.

Fun Facts

• SQL (Structured Query Language) was developed at IBM by Donald Chamberlin and Raymond Boyce in the early 1970s, based on Edgar Codd's relational model paper from 1970.
• SQL was first standardized by ANSI in 1986 (SQL-86) and has been revised in 1989, 1992, 1999, 2003, 2008, 2011, 2016, and 2023 — making it one of the longest-running living standards in computing.
• Despite 50 years of standardization efforts, SQL dialects diverge significantly across databases. A 2020 study found that PostgreSQL and MySQL share only about 70% of their SQL syntax for common operations.

FAQ

What information can a SQL parser extract?

From the AST: table names (FROM, JOIN), columns referenced, WHERE conditions, aggregate functions, GROUP BY/ORDER BY columns, subqueries, and CTEs. Full semantic analysis (type checking, constraint validation) requires a connected database.

Can the parser handle all SQL dialects?

SQL parsers are typically dialect-specific or implement a common subset. PostgreSQL-specific syntax (LATERAL, array functions) will not parse in a MySQL parser. Specify the target database dialect for accurate results.