dbmasta 0.1.19

A simple MariaDB/Postgres client based on SQLAlchemy core

dbmasta (simple mariadb/postgres interface)

Overview

This Python package provides a simple interface for interacting with MariaDB databases using SQLAlchemy Core. It abstracts some common database operations into more manageable Python methods, allowing for easy database queries, inserts, updates, and deletes.

Installation

To install this package, run the following pip command. Note: this requires SQLAlchemy 2.0.27 or greater

pip install dbmasta

Basic Usage

Configuration

First, configure the database client with the necessary credentials:

from dbmasta import DataBase, AsyncDataBase

# Initialize the database client
db = DataBase(
    dict(
        username='username', 
        password='password', 
        host='host', 
        port=3306, 
        default_database='database_name'
        )
    )
# Async Version
db = AsyncDataBase(
    dict(
        username='username', 
        password='password', 
        host='host', 
        port=3306, 
        default_database='database_name'
        )
    )


# Initiliaze using environment variables
db = DataBase.env()
# Async Version
db = AsyncDataBase.env()

Executing Queries

You can execute a simple SELECT query to fetch data:

import datetime as dt

# Create parameters
params = {
    "date": db.before(dt.date(2024,1,1), inclusive=True)
}

# Execute the query
dbr = db.select("database", "table", params)

# Examine the results
if dbr.successful:
    print(dbr.records)
else:
    print(dbr.error_info)

Complex Queries

The following query would generate this text:

import datetime as dt

# Create parameters
params = {
    "_OR_": db.or_(
        [
            {"date": db.after(dt.date(2020,1,1)), "category": "sales"},
            {"date": db.before(dt.date(2020,1,1)), "category": db.not_(db.in_, ["purchases","adjustments","sales"])},
        ]
    ),
    "_AND_": db.and_(
        [
            {"keyfield": db.starts_with("SJ")},
            {"keyfield": db.not_(db.ends_with("2E"))}
        ]
    )
    "status": "under_review"
}

# Execute the query
dbr = db.select("database", "table", params)

# Examine the results
if dbr.successful:
    print(dbr.records)
else:
    print(dbr.error_info)

The raw text of the query can be retrieved from the attribute dbr.raw_query from the DataBaseResponse object, which the DataBase.select method returns. The text in the above example would be as follows:

SELECT * FROM `database`.`table`
WHERE ((`date` > '2020-01-01' and `category`='sales') or 
(`date` < '2020-01-01' and `category` not in ('sales')))
AND `keyfield` LIKE 'SJ%' AND `keyfield` NOT LIKE '%2E'
AND `status`='under_review';

Or in simple terms... Get all records under_review where the keyfield starts with SJ, but doesn't end with 2E. Pull these if either:

• dated after 2020-01-01 and categorized as a sale
• dated before 2020-01-01 and not categorized as sale,purchase or adjustment.

Result Modification from DataBase.select

In addition to complex conditions for filtering records, you can:

• sort records

  db.select(..., order_by="column_name", reverse=True)
  

• limit and offset results

  # for offset pagination
  db.select(..., limit=100, offset=0)
  

• filter columns

  # only receive the data for the fields you provide
  db.select(..., columns=["keyfield", "name", "date"])
  

• get textual output (without executing)

  # this will not execute the query, but will return the raw query needed to execute
  raw_textual_query = db.select(..., textual=True)
  print(raw_textual_query)
  new_query = f"INSERT INTO `filteredtable` ({raw_textual_query[:-1]});
  dbr = db.run(new_query)
  

• get model output by providing a model factory

  from pydantic import BaseModel
  import datetime as dt
  
  class Record(BaseModel):
      keyfield: str
      date: dt.date
      status: str
  
  model_factory = lambda row: Record(**row)
  
  # only receive the data for the fields you provide
  dbr = db.select(..., response_model=model_factory)
  # each record will be an instance of Record