aiopyql 0.333

A fast and easy-to-use asyncio ORM(Object-relational Mapper) for performing C.R.U.D. ops within RBDMS tables using python

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 - Currently

• 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

• FastAPI

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

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

Optional:

• cache_enabled = True|False
• max_cache_len = 125 #Default

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    
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}
)