https://github.com/codemation/pyql
Library for managing data access to data-bases
https://github.com/codemation/pyql
Last synced: about 1 year ago
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Library for managing data access to data-bases
- Host: GitHub
- URL: https://github.com/codemation/pyql
- Owner: codemation
- Created: 2019-07-02T19:26:56.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-07-30T18:52:02.000Z (over 5 years ago)
- Last Synced: 2025-01-14T11:16:24.888Z (about 1 year ago)
- Language: Python
- Size: 142 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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