pyql

A simple ORM(Object-relational mapping) for accessing, inserting, updating, deleting data within RBDMS tables using python

Instalation

$ python3 -m venv env

$ source my-project/bin/activate

Install with PIP

 (env)$ pip install pyql-db   

Download & install Library from Github:

(env)$ git clone https://github.com/codemation/pyql.git

Use install script to install the pyql into the activated environment libraries

(env)$ cd pyql; sudo ./install.py install

Compatable Databases - Currently

• mysql
• sqlite

Getting Started

DB connection

    import sqlite3
    from pyql import data

    db = data.Database(
        sqlite3.connect, 
        database="testdb"
        )

    from pyql import data
    import mysql.connector

    db = data.Database(
        mysql.connector.connect,
        database='mysql_database',
        user='mysqluser',
        password='my-secret-pw',
        host='localhost',
        type='mysql'
        )

Existing tables schemas within databases are loaded when database object is instantiated and ready for use immedielty.

Table Create

Requires List of at least 2 item tuples, max 3

('column_name', type, 'modifiers')

• column_name - str - database column name exclusions apply
• types: str, int, float, byte, bool, None # JSON dumpable dicts fall under str types
• modifiers: NOT NULL, UNIQUE, AUTO_INCREMENT

Note Some differences may apply for column options i.e AUTOINCREMENT(sqlite) vs AUTO_INCREMENT(mysql) - See DB documentation for reference.

Note: Unique constraints are not validated by pyql but at db, so if modifier is supported it will be added when table is created.

# Table Create    
db.create_table(
    'stocks', 
    [    
        ('order_num', int, 'AUTO_INCREMENT'),
        ('date', str),
        ('trans', str),
        ('symbol', str),
        ('qty', float),
        ('price', str)
    ], 
    'order_num' # Primary Key 
)

mysql> describe stocks;
+-----------+---------+------+-----+---------+----------------+
| Field     | Type    | Null | Key | Default | Extra          |
+-----------+---------+------+-----+---------+----------------+
| order_num | int(11) | NO   | PRI | NULL    | auto_increment |
| date      | text    | YES  |     | NULL    |                |
| trans     | text    | YES  |     | NULL    |                |
| condition | text    | YES  |     | NULL    |                |
| symbol    | text    | YES  |     | NULL    |                |
| qty       | double  | YES  |     | NULL    |                |
| price     | text    | YES  |     | NULL    |                |
+-----------+---------+------+-----+---------+----------------+
6 rows in set (0.00 sec)

Creating Tables with Foreign Keys

db.create_table(
    'departments', 
    [    
        ('id', int, 'UNIQUE'),
        ('name', str)

    ], 
    'id' # Primary Key 
)

db.create_table(
    'positions', 
    [    
        ('id', int, 'UNIQUE'),
        ('name', str),
        ('department_id', int)
    ], 
    'id', # Primary Key
    foreign_keys={
        'department_id': {
                'table': 'departments', 
                'ref': 'id',
                'mods': 'ON UPDATE CASCADE ON DELETE CASCADE'
        }
    }
)

db.create_table(
    'employees', 
    [    
        ('id', int, 'UNIQUE'),
        ('name', str),
        ('position_id', int)
    ], 
    'id', # Primary Key
    foreign_keys={
        'position_id': {
                'table': 'positions', 
                'ref': 'id',
                'mods': 'ON UPDATE CASCADE ON DELETE CASCADE'
        }
    }
)

Insert Data

Requires key-value pairs - may be input using dict or the following

Un-packing

# Note order_num is not required as auto_increment was specified
trade = {'date': '2006-01-05', 'trans': 'BUY', 'symbol': 'RHAT', 'qty': 100.0, 'price': 35.14}
db.tables['stocks'].insert(
    **trade
)

query:
    INSERT INTO 
        stocks (date, trans, symbol, qty, price) 
        VALUES ("2006-01-05", "BUY", "RHAT", 100, 35.14)

In-Line

# Note order_num is not required as auto_increment was specified
db.tables['stocks'].insert(
    date='2006-01-05', 
    trans='BUY',
    symbol='RHAT',
    qty=200.0,
    price=65.14
)

query:
    INSERT INTO stocks (date, trans, symbol, qty, price) VALUES ("2006-01-05", "BUY", "RHAT", 200, 65.14)

Inserting Special Data

• Columns of type string can hold JSON dumpable python dictionaries as JSON strings and are automatically converted back into dicts when read.

• Nested Dicts are also Ok, but all items should be JSON compatible data types

    tx_data = {
        'type': 'BUY', 
        'condition': {
                    'limit': '36.00', 
                    'time': 'end_of_trading_day'
        }
    }
  
    trade = {
        'order_num': 1, 'date': '2006-01-05', 
        'trans': tx_data, # 
        'symbol': 'RHAT', 
        'qty': 100, 'price': 35.14, 'after_hours': True
    }
  
    db.tables['stocks'].insert(**trade)
    query:
        INSERT INTO 
            stocks (order_num, date, trans, symbol, qty, price, after_hours) 
            VALUES (1, "2006-01-05", '{"type": "BUY", "condition": {"limit": "36.00", "time": "end_of_trading_day"}}', "RHAT", 100, 35.14, True)
    result:
        In:
            db.tables['stocks'][1]['trans']['condition']
        Out: #
            {'limit': '36.00', 'time': 'end_of_trading_day'}
  

Select Data

Basic Usage:

All Rows & Columns in table

db.tables['employees'].select('*')

All Rows & Specific Columns

db.tables['employees'].select(
    'id', 
    'name', 
    'position_id'
)

All Rows & Specific Columns with Matching Values

db.tables['employees'].select(
    'id', 
    'name', 
    'position_id', 
    where={'id': 1000}
)

All Rows & Specific Columns with Multple Matching Values

db.tables['employees'].select(
    'id', 
    'name', 
    'position_id', 
    where={
        'id': 1000, 
        'name': 'Frank Franklin'
        }
    )

Advanced Usage:

All Rows & Columns from employees, Combining ALL Rows & Columns of table positions (if foreign keys match)

# Basic Join
db.tables['employees'].select(
    '*', 
    join='positions'
)
query:
    SELECT * FROM employees JOIN positions ON employees.position_id = positions.id
output:
    [{
        'employees.id': 1000, 'employees.name': 'Frank Franklin', 
        'employees.position_id': 100101, 'positions.name': 'Director', 
        'positions.department_id': 1001},
        ...
    ]

All Rows & Specific Columns from employees, Combining All Rows & Specific Columns of table positions (if foreign keys match)

# Basic Join 
db.tables['employees'].select(
    'employees.name', 
    'positions.name', 
    join='positions'
)
query:
    SELECT 
        employees.name,
        positions.name 
    FROM 
        employees 
    JOIN 
        positions 
    ON 
        employees.position_id = positions.id
output:
    [
        {'employees.name': 'Frank Franklin', 'positions.name': 'Director'}, 
        {'employees.name': 'Eli Doe', 'positions.name': 'Manager'},
        ...
    ]

All Rows & Specific Columns from employees, Combining All Rows & Specific Columns of table positions (if foreign keys match) with matching 'position.name' value

# Basic Join with conditions
db.tables['employees'].select(
    'employees.name', 
    'positions.name', 
    join='positions', # Possible due to foreign key relationship 
    where={
        'positions.name': 'Director'
        }
    )
query:
    SELECT 
        employees.name,
        positions.name 
    FROM 
        employees 
    JOIN positions ON 
        employees.position_id = positions.id 
    WHERE positions.name='Director'
output:
    [
        {'employees.name': 'Frank Franklin', 'positions.name': 'Director'}, 
        {'employees.name': 'Elly Doe', 'positions.name': 'Director'},
        ..
    ]

All Rows & Specific Columns from employees, Combining Specific Rows & Specific Columns of tables positions & departments

Note: join='x_table' will only work if the calling table has a f-key reference to table 'x_table'

# Multi-table Join with conditions
db.tables['employees'].select(
    'employees.name', 
    'positions.name', 
    'departments.name', 
    join={
        'positions': {'employees.position_id': 'positions.id'}, 
        'departments': {'positions.department_id': 'departments.id'}
    }, 
    where={'positions.name': 'Director'})
query:
    SELECT 
        employees.name,
        positions.name,
        departments.name 
    FROM 
        employees 
    JOIN positions ON 
        employees.position_id = positions.id 
    JOIN departments ON 
        positions.department_id = departments.id 
    WHERE 
        positions.name='Director'
result:
    [
        {'employees.name': 'Frank Franklin', 'positions.name': 'Director', 'departments.name': 'HR'}, 
        {'employees.name': 'Elly Doe', 'positions.name': 'Director', 'departments.name': 'Sales'}
    ]

Special Note: When performing multi-table joins, joining columns must be explicity provided. The key-value order is not explicity important, but will determine which column name is present in returned rows

join={'y_table': {'y_table.id': 'x_table.y_id'}}
result:
    [
        {'x_table.a': 'val1', 'y_table.id': 'val2'},
        {'x_table.a': 'val1', 'y_table.id': 'val3'}
    ]

OR

join={'y_table': {'x_table.y_id': 'y_table.id'}}
result:
    [
        {'x_table.a': 'val1', 'x_table.y_id': 'val2'},
        {'x_table.a': 'val1', 'x_table.y_id': 'val3'}
    ]

Operator Syntax

The Following operators are supported within the list query syntax

'=', '==', '<>', '!=', '>', '>=', '<', '<=', 'like', 'in', 'not in', 'not like'

Operator Syntax Requires a list-of-lists and supports multiple combined conditions

#Syntax

db.tables['table'].select(
    '*',
    where=[[condition1], [condition2], [condition3]]
)


db.tables['table'].select(
    '*',
    where=[
        ['col1', 'like', 'abc*'],
        ['col2', '<', 10],
        ['col3', 'not in', ['a', 'b', 'c'] ]
    ]
)

Examples:

find_employee = db.tables['employees'].select(
    'id', 
    'name',
    where=[
        ['name', 'like', '*ank*']
    ]
)
query:
    SELECT id,name FROM employees WHERE name like '%ank%'
result:
    [{'id': 1016, 'name': 'Frank Franklin'}, {'id': 1018, 'name': 'Joe Franklin'}, {'id': 1020, 'name': 'Frank Franklin'}, {'id': 1034, 'name': 'Dana Franklin'}, {'id': 1036, 'name': 'Jane Franklin'}, {'id': 1042, 'name': 'Frank Franklin'}, {'id': 1043, 'name': 'Eli Franklin'}, {'id': 1052, 'name': 'Eli Franklin'}, {'id': 1057, 'name': 'Eli Franklin'}]



delete_department = db.tables['departments'].delete(
    where=[
        ['id', '<', 2000]
    ]
)
query:
    DELETE 
        FROM 
            departments 
        WHERE 
            id < 2000


join_sel = db.tables['employees'].select(
    '*', 
    join={
        'positions': {
            'employees.position_id':'positions.id', 
            'positions.id': 'employees.position_id'
        }
    },
    where=[
        [
            'positions.name', 'not in', ['Manager', 'Intern', 'Rep']
        ],
        [
            'positions.department_id', '<>', 2001 # not equal
        ]
    ]
)
query:
    SELECT 
        * 
    FROM 
        employees 
    JOIN 
        positions 
        ON 
            employees.position_id = positions.id  
        AND  
            positions.id = employees.position_id 
    WHERE 
        positions.name not in ('Manager', 'Intern', 'Rep') 
    AND 
        positions.department_id <> 2001

Special Examples:

Bracket indexs can only be used for primary keys and return entire row, if existent

db.tables['employees'][1000]
query:
    SELECT * FROM employees WHERE id=1000
result:
    {'id': 1000, 'name': 'Frank Franklin', 'position_id': 100101}

Iterate through table - grab all rows - allowing client side filtering

for row in db.tables['employees']:
    print(row['id], row['name'])
query:
    SELECT * FROM employees
result:
    1000 Frank Franklin
    1001 Eli Doe
    1002 Chris Smith
    1003 Clara Carson

Using list comprehension

sel = [(row['id'], row['name']) for row in db.tables['employees']]
query:
    SELECT * FROM employees
result:
    [
        (1000, 'Frank Franklin'), 
        (1001, 'Eli Doe'), 
        (1002, 'Chris Smith'), 
        (1003, 'Clara Carson'),
        ...
    ]

Update Data

Define update values in-line or un-pack

db.tables['stocks'].update(symbol='NTAP',trans='SELL', where={'order_num': 1})
query:
    UPDATE stocks SET symbol = 'NTAP', trans = 'SELL' WHERE order_num=1

Un-Pack

#JSON capable Data 
tx_data = {'type': 'BUY', 'condition': {'limit': '36.00', 'time': 'end_of_trading_day'}}
to_update = {'symbol': 'NTAP', 'trans': tx_data}
where = {'order_num': 1}

db.tables['stocks'].update(**to_update, where=where)
query:
    UPDATE 
        stocks 
    SET 
        symbol = 'NTAP', 
        trans = '{"type": "BUY", "condition": {"limit": "36.00", "time": "end_of_trading_day"}}' 
    WHERE 
        order_num=1

Bracket Assigment - Primary Key name assumed inside Brackets for value

#JSON capable Data 

tx_data = {'type': 'BUY', 'condition': {'limit': '36.00', 'time': 'end_of_trading_day'}}
to_update = {'symbol': 'NTAP', 'trans': tx_data, 'qty': 500}

db.tables['stocks'][2] = to_update

query:
    # check that primary_key value 2 exists
    SELECT 
        * 
    FROM 
        stocks 
    WHERE 
        order_num=2

    # update 
    UPDATE 
        stocks 
    SET 
        symbol = 'NTAP', 
        trans = '{"type": "BUY", "condition": {"limit": "36.00", "time": "end_of_trading_day"}}', 
        qty = 500 
    WHERE 
        order_num=2

result:
    db.tables['stocks'][2]
    {
        'order_num': 2, 
        'date': '2006-01-05', 
        'trans': {'type': 'BUY', 'condition': {'limit': '36.00', 'time': 'end_of_trading_day'}}, 
        'symbol': 'NTAP', 
        'qty': 500, 
        'price': 35.16, 
        'after_hours': True
    }

Delete Data

db.tables['stocks'].delete(where={'order_num': 1})

Other

Table Exists

'employees' in db
query:
    show tables
result:
    True

Primary Key Exists:

1000 in db.tables['employees']
query:
    SELECT * FROM employees WHERE id=1000
result:
    True