dbprocedures 0.1.2

Fetch and execute MySQL queries by proc_name, returning a pandas DataFrame.

dbprocedures

A lightweight Python library that fetches a registered SQL query by name from a MySQL table and returns its result as a pandas DataFrame.

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Installation

From a local folder

pip install /path/to/dbprocedures/

From a Git repository (internal)

pip install git+https://your-internal-git-repo/dbprocedures.git

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Prerequisites

1. Environment Variables

Set the following environment variables before using the library:

Variable	Description	Example
DB_HOST	MySQL server hostname	your-db-host.example.com
DB_PORT	MySQL server port	3306
DB_NAME	Database name	your_database_name
DB_USER	MySQL username	your_username
DB_PASSWORD	MySQL password	yourpassword

Linux / macOS:

export DB_HOST=your-db-host.example.com
export DB_PORT=3306
export DB_NAME=your_database_name
export DB_USER=your_username
export DB_PASSWORD=yourpassword

Windows (PowerShell):

$env:DB_HOST = "your-db-host.example.com"
$env:DB_PORT = "3306"
$env:DB_NAME = "your_database_name"
$env:DB_USER = "your_username"
$env:DB_PASSWORD = "yourpassword"

2. Registry Table

The MySQL database must have a table (default name: proc_query_registry) with these columns:

Column	Type	Description
proc_name	VARCHAR	Unique name for the query
query	TEXT	The SQL query to execute

Example:

CREATE TABLE proc_query_registry (
    proc_name VARCHAR(255) PRIMARY KEY,
    query     TEXT NOT NULL
);

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Usage

from dbprocedures import run_proc

df = run_proc("my_proc_name")
print(df.head())

Custom registry table name

df = run_proc("my_proc_name", registry_table="my_custom_table")

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How It Works

1. Reads DB credentials from environment variables.
2. Connects to MySQL using pymysql.
3. Looks up the SQL query in proc_query_registry where proc_name matches.
4. Executes that query on the same DB connection.
5. Returns the result as a pandas.DataFrame.