fast-pysqlparse 0.5.0

A fast SQL parser with Python wrapper and C++ core

fast-pysqlparse: High-Performance SQL Parsing Library

README.md (Chinese)

A high-performance, cross-platform SQL parsing library, designed to handle the most complex SQL queries with ease.

Overview

This library provides a robust set of tools for parsing and analyzing SQL statements. Built with a core engine in C++17 for maximum performance, it offers native Python bindings, making it the ideal choice for data-intensive applications where speed and accuracy are critical.

It excels at parsing extremely long SQL statements and queries with deeply nested subqueries, delivering performance far superior to pure-Python alternatives.

Features

• Fast SQL Parsing: Leverages a high-performance C++17 core to parse SQL statements rapidly
• Cross-Platform: Compiled into native extensions (.pyd for Windows, .so for Linux)
• Comprehensive SQL Support: Supports a wide range of SQL statements, including:
  • SELECT (with complex JOIN, WHERE, GROUP BY, subqueries, etc.)
  • INSERT
  • Data Definition Language (CREATE)
  • VIEW
  • DELETE
  • UPDATE
  • Common Table Expressions (CTEs), including nested CTEs
• Abstract Syntax Tree (AST): Generates a detailed JSON representation of the parsed SQL AST for easy traversal and analysis
• SQL Formatting: Automatically reformats messy SQL into a clean, readable structure
• Table Lineage Parsing: Automatically traces and reveals the source-to-target relationships between tables (data lineage)
• Tokenization: Breaks down SQL statements into their fundamental tokens for lexical analysis
• Python API: A clean and intuitive Python library built around the high-speed native extension

Performance

This library is engineered for speed. By moving the computationally intensive parsing work to a native C++ layer, it significantly outperforms pure-Python parsing libraries, especially when dealing with large, complex SQL scripts.

Benchmark Results

Test 1: Comparison with sqlparse/sqlglot (1359 char SQL, 100 iterations)

Parser	Total Time	Avg per Parse	Speedup
fast-pysqlparse	0.0170s	0.17ms	1.0x (baseline)
sqlparse	1.3040s	13.04ms	76.75x faster
sqlglot	0.4283s	4.28ms	25.21x faster

Test 2: 5000 Iterations

• SQL Length: 639 characters
• Total Time: 0.6084s
• PPS (Parses Per Second): 8218.88
• Average per parse: 0.1217ms

Test 3: 10 Million Character SQL

• SQL Length: 10,500,998 characters
• Total Time: 1.4085s
• CPS (Characters Per Second): 7,455,540
• Parse successful!

Installation

pip install fast-pysqlparse

From Source:

git clone https://github.com/Nohaltsail/fast-pysqlparse.git
cd fast-pysqlparse
pip install build
python -m build
cd dist
pip install fast_pysqlparse-*.whl

Quick Start

from fastsqlparse import Parsed
from fastsqlparse.statement import ParsedQuery

if __name__ == '__main__':
    sql = """

-- main query
SELECT 
    'Monthly Sales Report' AS report_type,
    ms.year,
    ms.month,
    ms.region,
    ms.customer_segment,
    ms.unique_customers,
    ms.total_orders,
    ms.gross_sales,
    ms.avg_order_value,
    ms.cancelled_orders,
    (SELECT SUM(gross_sales) FROM sub_monthly_sales WHERE year = ms.year AND month = ms.month) AS total_monthly_sales,
    ms.gross_sales / NULLIF((SELECT SUM(gross_sales) FROM monthly_sales WHERE year = ms.year AND month = ms.month), 0) * 100 AS sales_percentage,
    (SELECT AVG(avg_order_value) FROM monthly_sales WHERE year = ms.year AND month = ms.month) AS overall_avg_order_value
FROM monthly_sales ms

UNION ALL

SELECT 
    'Category Performance' AS report_type,
    cs.year,
    cs.month,
    NULL AS region,
    cs.category_name AS customer_segment,
    cs.unique_buyers AS unique_customers,
    cs.order_count AS total_orders,
    cs.total_sales AS gross_sales,
    cs.total_sales / NULLIF(cs.order_count, 0) AS avg_order_value,
    NULL AS cancelled_orders,
    (SELECT SUM(total_sales) FROM sub_category_sales WHERE year = cs.year AND month = cs.month) AS total_monthly_sales,
    cs.total_sales / NULLIF((SELECT SUM(total_sales) FROM category_sales WHERE year = cs.year AND month = cs.month), 0) * 100 AS sales_percentage,
    NULL AS overall_avg_order_value
FROM category_sales cs
LIMIT 50, 100"""

    sql_len = len(sql)
    print("sql length: ", sql_len)

    # parse sql statements to SQL object
    sql_stmt = Parsed(sql)
    # Format and print the SQL statement with proper indentation
    print(sql_stmt.format())  # Output formatted SQL statement

    # Tokenization - returns list of tuples containing (value, type, position)
    tokens = ParsedQuery.tokenize(sql)  # Get tuple list of token information (value, type, position)

    # Alternative tokenization - returns list of token objects with attributes
    token_obj_list = sql_stmt.tokens()  # Get object list of token information

    # Generate and print Abstract Syntax Tree (AST) in JSON format
    print(sql_stmt.AST())  # Get JSON structure of the SQL statement

    # Extract table lineage/dependencies from the query
    src_tables = ParsedQuery.parse_dependence(sql)  # Get source tables (dependencies) of the query

When to Use Which Parser

Scenario	Parser to Use
SQL statement type is unknown or you don't want to specify the type	Parsed/ParsedOne
Multiple SQL statements separated by ; (script execution)	Parsed
SELECT / query statement	ParsedQuery
INSERT statement	ParsedInsert
DELETE statement	ParsedDelete
UPDATE statement	ParsedUpdate
CREATE TABLE statement	ParsedCreate
CREATE VIEW statement	ParsedView
CTE (WITH clause) statement	ParsedCTE

Note: If your SQL contains multiple statements separated by semicolons (e.g., a script with CREATE, INSERT, SELECT), you must use Parsed. The type-specific parsers are designed for single, known-type statements only.

Documentation

For complete API documentation, see: API_DOC.md

Contributing

Contributions are welcome! Please feel free to submit pull requests, report bugs, or suggest new features.

License

Apache-2.0