Skip to main content

A Python package to execute SQL queries and procedures and manage backups.

Project description

SQLViaCode

Overview

SQLViaCode is a library for executing SQL queries, stored procedures, table backups, and reading queries from files, all with a clean and simple interface.


Features

  1. Query Execution: Execute SQL queries and fetch the results as a pandas DataFrame.
  2. Stored Procedure Execution: Run stored procedures with input parameters.
  3. Table Backup: Automatically back up a specified table's data before executing a query or procedure.
  4. Query from File: Read SQL queries from external files to improve maintainability.

Functions

get_query_from_db

Executes a SQL query and optionally backs up a table.

Parameters:

  • query (str): SQL query to execute.
  • table_to_backup (str or None): Table to back up. Pass None to skip the backup.
  • env_file_name (str, optional): Path to the .env file for database credentials (default is .env).
  • params (dict, optional): Query parameters.

Returns:

  • A pandas.DataFrame containing query results.

exec_procedure_from_db

Executes a stored procedure and optionally backs up a table.

Parameters:

  • procedure_name (str): Name of the stored procedure.
  • table_to_backup (str or None): Table to back up. Pass None to skip the backup.
  • env_file_name (str, optional): Path to the .env file for database credentials (default is .env).
  • params (dict, optional): Procedure parameters.

Returns:

  • A pandas.DataFrame containing procedure output.

get_query_from_file

Reads a SQL query from a file.

Parameters:

  • file_path (str): Path to the .sql or .txt file containing the query.

Returns:

  • A str containing the SQL query.

Example:

from SQLViaCode import get_query_from_file, get_query_from_db

query = get_query_from_file("queries/select_employees.sql")
result_df = await get_query_from_db(query, table_to_backup="employees")
print(result_df)

Installation

To install the package and dependencies:

pip install SQLViaCode

Dependencies

The project requires the following libraries:

  • pandas==2.2.3
  • SQLAlchemy==2.0.36
  • python-dotenv==1.0.1
  • pyodbc==5.2.0
  • aiofiles==24.1.0
  • tabulate==0.9.0
  • asyncpg==0.30.0 (for PostgreSQL)
  • aiomysql==0.2.0 (for MySQL)
  • aiosqlite==0.20.0 (for SQLite)

Environment Setup

Ensure you have a .env file in the root directory with the following database configuration:

DB_TYPE=your_db_type
USER=your_username
PASSWORD=your_password
HOST=your_host
NAME=your_database_name
DRIVER=your_driver

Example .env file for MSSQL:

DB_TYPE=mssql
USER=admin
PASSWORD=secretpassword
HOST=127.0.0.1
NAME=my_database
DRIVER=ODBC Driver 17 for SQL Server

Example .env file for SQLite:

DB_TYPE=sqlite
NAME=example.db

Usage Examples

Example 1: Executing a Query with Backup

from SQLViaCode import get_query_from_db

query = "SELECT * FROM employees"
result_df = await get_query_from_db(query, table_to_backup="employees")
print(result_df)

Example 2: Executing a Stored Procedure

from SQLViaCode import exec_procedure_from_db

procedure_name = "sp_get_employee_data"
params = {"department": "Sales"}
result_df = await exec_procedure_from_db(procedure_name, table_to_backup="employees", params=params)
print(result_df)

Example 3: Reading Query from File

from SQLViaCode import get_query_from_file, get_query_from_db

query = get_query_from_file("queries/select_employees.sql")
result_df = await get_query_from_db(query, table_to_backup="employees")
print(result_df)

Supported Databases

Database DB_TYPE Required Fields
SQLite sqlite NAME
PostgreSQL postgresql USER, PASSWORD, HOST, NAME
MySQL mysql USER, PASSWORD, HOST, NAME
MSSQL mssql USER, PASSWORD, HOST, NAME, DRIVER
Oracle oracle USER, PASSWORD, HOST, NAME

License

MIT License

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sql_via_code-0.1.3.tar.gz (9.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

sql_via_code-0.1.3-py3-none-any.whl (8.3 kB view details)

Uploaded Python 3

File details

Details for the file sql_via_code-0.1.3.tar.gz.

File metadata

  • Download URL: sql_via_code-0.1.3.tar.gz
  • Upload date:
  • Size: 9.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.6

File hashes

Hashes for sql_via_code-0.1.3.tar.gz
Algorithm Hash digest
SHA256 eb05e52886b2e653ec1b555857d34789e6386af92c6f8ed3ef8bed0d79233db5
MD5 75be78791817e9fcb7c3752aec66dcd5
BLAKE2b-256 7495cf4117201f2ad4d266edccfdefa1e185c2a99aba648cdd13ad0f186fa25a

See more details on using hashes here.

File details

Details for the file sql_via_code-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: sql_via_code-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 8.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.6

File hashes

Hashes for sql_via_code-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 a1b79d10f24a0f9afff553fcfd3a10df31a2b438081fa28bade2180d82555f70
MD5 09db3da59ddf218416ae83398d63201a
BLAKE2b-256 72a4d6be6eea4cce54c725c389621e2da9c501d6558415c554f411dea2557944

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page