fast-pysqlparse 0.3.1

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

SQL Parser Library:

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, sub query, 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.

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

pip install fast-pysqlparse

from fastsqlparse import ParsedSQL
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 = ParsedSQL(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	parser.ParsedSQL
Multiple SQL statements separated by ; (script execution)	parser.ParsedSQL
SELECT / query statement	statement.ParsedQuery
INSERT statement	statement.ParsedInsert
DELETE statement	statement.ParsedDelete
UPDATE statement	statement.ParsedUpdate
CREATE TABLE statement	statement.ParsedCreate
CREATE VIEW statement	statement.ParsedView
CTE (WITH clause) statement	statement.ParsedCTE

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

Detail DOC

zh_CN

Contributing:

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