SQLParserDataPipeline 0.4

The SQLParserDataPipeline Library is a powerful Python package designed for parsing and interpreting complex SQL queries. It was developed with a focus on BigQuery but is adaptable to other SQL dialects due to its flexible parsing strategy that doesn't consider the function itself but the most inner parentheses.

SQL Query Tools Library

Overview

The SQL Query Tools Library is a powerful Python package designed for parsing and interpreting complex SQL queries. It was developed with a focus on BigQuery but is adaptable to other SQL dialects due to its flexible parsing strategy that doesn't consider the function itself but the most inner parentheses.. This Parser is specifically tuned to handle intricate query structures that go beyond the capabilities of standard SQL parsers.

Features

• Select Clause Parsing: Handles a wide range of SELECT statements, from simple queries to those with nested statements, functions, and placeholders.
• From Clause Analysis: Identifies table names and associated aliases in medium complexity SQL queries, suitable for LeetCode-level challenges.
• Unnest Transformations: Extracts details from UNNEST operations, such as the type of join, aliases, and unique values, which are crucial for building data pipelines.

Capabilities

Select Function

The select function outperforms typical SQL parsers by accurately parsing column names in queries that include:

• Nested SELECT statements
• Functions within columns
• Use of placeholders and complex syntax

From Function

The from function is optimized for medium complexity queries. It can accurately identify table names and their aliases within a query. Future updates aim to extend its capabilities to handle more complex scenarios.

Unnest Function

The unnest function is crucial for understanding complex joins in queries. It returns:

• The type of join used
• The alias of the join, for easy reference in SELECT statements
• Unique values of columns involved in the UNNEST operation

This function is particularly useful for those developing data pipelines where understanding the flow of data transformation is critical.

Getting Started

To get started with SQL Query Tools, install the package using pip:

pip SQLParserDataPipeline

Usage

An example on how to call the functions and use the library is provided on Usage.py , feel free to use it with your queries

Queries Examples

On Example_Query.SQL a few examples were provided to demostrate how our library perform on gradually more complex queries. In each example we experiment a potential issue that other parser can't deal with.