Simple python database orchestration utility which makes it easy to add tables, insert, select, update, delete items with tables
Project description
aioaiopyql
Asyncio 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 aiopyql-db
Download & install Library from Github:
(env)$ git clone https://github.com/codemation/aiopyql.git
Use install script to install the aiopyql into the activated environment libraries
(env)$ cd aiopyql; sudo ./install.py install
Compatable Databases - Currently
- mysql
- sqlite
Getting Started
DB connection
Sqlite3: Default
from aiopyql import data
db = data.Database(
database="testdb"
)
Mysql
from aiopyql import data
db = data.Database(
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 aiopyql 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}
await 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
await 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 } await 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'] # synchronus - run outside of event loop Out: # {'limit': '36.00', 'time': 'end_of_trading_day'}
Select Data
Basic Usage:
All Rows & Columns in table
await db.tables['employees'].select('*')
All Rows & Specific Columns
await db.tables['employees'].select(
'id', 'name', 'position_id'
)
All Rows & Specific Columns with Matching Values
await db.tables['employees'].select(
'id', 'name', 'position_id',
where={'id': 1000}
)
All Rows & Specific Columns with Multple Matching Values
await 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
await 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
await 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
await db.tables['employees'].select(
'employees.name',
'positions.name',
join='positions',
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
await 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'}
]
Special Examples:
Bracket indexs can only be used for primary keys and return entire row, if existent
db.tables['employees'][1000] # Synchronus only
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
async 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']) async 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
await 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}
await 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 # Synchronus only
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] # Synchronus only
{
'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
await 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
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