A fast and easy-to-use asyncio ORM(Object-relational Mapper) for performing C.R.U.D. ops within RBDMS tables using python
Project description
aiopyql
A fast and easy-to-use asyncio ORM(Object-relational Mapper) for performing C.R.U.D. ops within RBDMS tables using python.
Key Features
- fast
- asyncio ready
- database / table query cache
- SQL-like query syntax
- Schema discovery
Instalation
$ virtualenv -p python3.7 aiopyql-env
$ source aiopyql-env/bin/activate
Install with PIP
(aiopyql-env)$ pip install aiopyql
Download & install Library from repo:
(aiopyql-env)$ git clone https://github.com/codemation/aiopyql.git
Use install script to install the aiopyql into the activated environment libraries
(aiopyql-env)$ cd aiopyql; sudo ./install.py install
Compatable Databases
- mysql
- sqlite
Getting Started
A Database object can be created both in and out of an event loop, but the Database.create() factory coro ensures load_tables() is processed to load existing tables.
import asyncio
from aiopyql import data
async def main():
#sqlite connection
sqlite_db = await data.Database.create(
database="testdb"
)
# create table
await db.create_table(
'keystore',
[
('key', str, 'UNIQUE NOT NULL'),
('value', str)
],
'key',
cache_enabled=True
)
# insert
await db.tables['keystore'].insert(
key='foo',
value={'bar': 30}
)
# update
await db.tables['keystore'].update(
value={'bar': 31},
where={'key': 'foo'}
)
# delete
await db.tables['keystore'].delete(
where={'key': 'foo'}
)
loop = asyncio.new_event_loop()
loop.run_until_complete(main())
Recipies
See other usage examples in recipies.
Mysql
Note: if no type specified, default is sqlite
import asyncio
from aiopyql import data
async def main():
mysql_db = await data.Database.create(
database='mysql_database',
user='mysqluser',
password='my-secret-pw',
host='localhost',
port=3306,
type='mysql'
)
loop = asyncio.new_event_loop()
loop.run_until_complete(main())
Existing tables schemas within databases are loaded when database object is instantiated via .create and ready for use immedielty.
Database Read Cache
Database read cache provides read query based caching. This cache is accessed when a duplicate query is received before any table changes. This is capable of providing relief for more expensive & less explicit querries that might span multiple tables ( via table joins ).
A Database read cach entry will be invalidated if an INSERT - UPDATE - DELETE query runs against a table referenced by the cached entry.
Usage
import asyncio
from aiopyql import data
async def main():
sqlite_db = await data.Database.create(
database="testdb", # if no type specified, default is sqlite
cache_enabled=True, # Default False
cache_length=256 # Default 128 if cache is enabled
)
Enable on existing Database
sqlite_db.enable_cache()
Disable Cache & clear cached entries from memory
sqlite_db.disable_cache()
Table Cache
Key Features:
- Row based read cache, which returns cached rows based on table primary key
- 'select *' querries will load both Database & Table Cache
- updates to table also update existing cache entries
- database cache invalidation is separated from table cache invalidation
- Last-Accessed-Last-Out expiration - frequently accessed data remains cached
Usage:
await db.create_table(
'keystore',
[
('key', str, 'UNIQUE NOT NULL'),
('value', str)
],
'key',
cache_enabled=True
cache_length=256
)
Enable Cache on existing table
# turn on
db.tables['keystore'].enable_cache()
Disable & Remove cached entries
db.tables['keystore'].disable_cache()
Cache Load Events
- A complete row is accessed via select = '*', with our without conditions
- A complete row is inserted # Complete meaning value for all rows in table
Cache Update Events
- An update is issued which includes conditions matching a cached row' primary key
Cache Delete Events
- A Delete is issued against a row with cached primary key
- Table max_cache_len is reached and the row was the oldest of the last referenced keys
Forking & Cache Safety
The Database object can be safely forked by a parent process IF CACHE IS DISABLED. Cache from one process should be be trusted as consistent with another process when a change occurs, as invalidation does not propagate to all forks.
Cache can be safely used amoung co-routines within the same event_loop.
Table Create
Usage
db.create_table(
'table_name',
[
('col_name', <col_type[int,str,float,byte, bool]>, 'col_mods'),
.
..
],
prim_key='<col_name>',
foreign_keys={
'<col_name>': {
'table': '<ref_table_name>'
'ref': '<ref_table_column>',
'mods': 'ON UPDATE CASCADE'
}
},
cache_enabled = True | False(default),
max_cache_len = 125(default)
)
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
Some Column modifiers apply for column options i.e
AUTOINCREMENT (sqlite)
AUTO_INCREMENT (mysql)
See DB documentation for reference.
Optional:
cache_enabled = True | False (Default)
max_cache_len = 125 (Default)
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
await 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
await db.create_table(
'departments',
[
('id', int, 'UNIQUE'),
('name', str)
],
'id' # Primary Key
)
await 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'
}
},
cache_enabled=True,
cache_length=128
)
await 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'
}
}
cache_enabled=True,
cache_length=256
)
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)
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
)
sel = await db.tables['stocks'][1]
print(sel['trans']['condition'])
{'limit': '36.00', 'time': 'end_of_trading_day'}
Select Data
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')
SELECT *
FROM
employees
JOIN positions ON
employees.position_id = positions.id
[
{
'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' # # possible only if foreign key relation exists between employees & positions
)
SELECT
employees.name,positions.name
FROM employees
JOIN positions ON
employees.position_id = positions.id
[
{'employees.name': 'Frank Franklin', 'positions.name': 'Director'},
{'employees.name': 'Eli Doe', 'positions.name': 'Manager'},
...
]
Basic Join with conditions
join='positions' will only work if the calling table "await db.tables['employees']" has a foreign-key reference to table 'positions'
await db.tables['employees'].select(
'employees.name',
'positions.name',
join='positions', # made possible if foreign key relation exists between employees & positions
where={
'positions.name': 'Director'}
)
SELECT
employees.name,
positions.name
FROM
employees
JOIN positions ON
employees.position_id = positions.id
WHERE
positions.name='Director'
[
{'employees.name': 'Frank Franklin', 'positions.name': 'Director'},
{'employees.name': 'Elly Doe', 'positions.name': 'Director'},
..
]
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'}
)
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'
[
{'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'}
]
Operators
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
await db.tables['table'].select(
'*',
where=[[condition1], [condition2], [condition3]]
)
await db.tables['table'].select(
'*',
where=[
['col1', 'like', 'abc*'], # Wildcards
['col2', '<', 10], # Value Comparison
['col3', 'not in', ['a', 'b', 'c'] ] # Inclusion / Exclusion
]
)
List Syntax - Examples:
Search for rows which contain specified chars using wild card '*'
find_employee = await db.tables['employees'].select(
'id',
'name',
where=[
['name', 'like', '*ank*'] # Double Wild Card - Search
]
)
SELECT id,name FROM employees WHERE name like '%ank%'
[
{'id': 1016, 'name': 'Frank Franklin'},
{'id': 1018, 'name': 'Joe Franklin'},
{'id': 1034, 'name': 'Dana Franklin'},
{'id': 1036, 'name': 'Jane Franklin'},
{'id': 1043, 'name': 'Eli Franklin'},
]
Delete Rows matching value comparison
delete_department = await db.tables['departments'].delete(
where=[
['id', '<', 2000] # Value Comparison
]
)
DELETE FROM departments WHERE id < 2000
Select Rows using Join and exluding rows with sepcific values
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'] # Exclusion within Join
],
[
'positions.department_id', '<>', 2001 # Exclusion via NOT EQUAL
]
]
)
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
await db.tables['employees'][1000]
SELECT * FROM employees
WHERE id=1000
{'id': 1000, 'name': 'Frank Franklin', 'position_id': 100101}
Note: As db.tables['employees'][1000] returns an 'awaitable', sub keys cannot be specified until the object has been 'awaited'
# Incorrect
emp_id = await db.tables['employees'][1000]['id']
__main__:1: RuntimeWarning: coroutine was never awaited
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'coroutine' object is not subscriptable
# Correct
sel = await db.tables['employees'][1000]
emp_id = sel['id]
Iterate through table - grab all rows - allowing client side filtering
async for row in db.tables['employees']:
print(row['id], row['name'])
SELECT * FROM employees
1000 Frank Franklin
1001 Eli Doe
1002 Chris Smith
1003 Clara Carson
Using list comprehension
sel = [tuple(row['id'], row['name']) async for row in db.tables['employees']]
SELECT * FROM employees
[
(1000, 'Frank Franklin'),
(1001, 'Eli Doe'),
(1002, 'Chris Smith'),
(1003, 'Clara Carson'),
...
]
Update Data
In-line
await db.tables['stocks'].update(
symbol='NTAP',
trans='SELL',
where={'order_num': 1}
)
UPDATE stocks
SET
symbol = 'NTAP',
trans = 'SELL'
WHERE
order_num=1
Un-Pack
# JSON Serializable Data
tx_data = {
'type': 'BUY',
'condition': {
'limit': '36.00',
'time': 'end_of_trading_day'
}
}
to_update = {
'symbol': 'NTAP',
'trans': tx_data # dict
}
await db.tables['stocks'].update(
**to_update,
where={'order_num': 1}
)
UPDATE stocks
SET
symbol = 'NTAP',
trans = '{"type": "BUY", "condition": {"limit": "36.00", "time": "end_of_trading_day"}}'
WHERE
order_num=1
Using set_item
await db.tables['table'].set_item('primary_key': {'column': 'value'})
#JSON Serializable Data
tx_data = {
'type': 'BUY',
'condition': {
'limit': '36.00',
'time': 'end_of_trading_day'
}
}
to_update = {
'symbol': 'NTAP',
'trans': tx_data, # dict
'qty': 500}
await db.tables['stocks'].set_item(2, to_update)
# two resulting db querries
# checks 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
await db.tables['stocks'][2]
# beutified
{
'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}
)
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