# AsyncDB #
AsyncDB is a collection of different Database Drivers using asyncio-based connections and binary connectors (as asyncpg) but providing an abstraction layer to easily connect to different data sources, a high-level abstraction layer for various non-blocking database connectors,
on other blocking connectors (like MS SQL Server) we are using ThreadPoolExecutors to run in a non-blocking manner.
### Why AsyncDB? ###
The finality of AsyncDB is to provide us with a subset of drivers (connectors) for accessing different databases and data sources for data interaction.
The main goal of AsyncDB is to use asyncio-based technologies.
### Getting Started ###
## Requirements
Python 3.9+
## Installation
<div class="termy">
```console
$ pip install asyncdb
---> 100%
Successfully installed asyncdb
```
Can also install only drivers required like:
```console
$ pip install asyncdb[pg] # this install only asyncpg
```
Or install all supported drivers as:
```console
$ pip install asyncdb[all]
```
### Requirements ###
* Python >= 3.8
* asyncio (https://pypi.python.org/pypi/asyncio/)
Currently AsyncDB supports the following databases:
* PostgreSQL (supporting two different connectors: asyncpg or aiopg)
* SQLite (requires aiosqlite)
* mySQL/MariaDB (requires aiomysql and mysqlclient)
* ODBC (using aioodbc)
* JDBC(using JayDeBeApi and JPype)
* RethinkDB (requires rethinkdb)
* Redis (requires aioredis)
* Memcache (requires aiomcache)
* MS SQL Server (non-asyncio using freeTDS and pymssql)
* Apache Cassandra (requires official cassandra driver)
* InfluxDB (using influxdb)
* CouchBase (using aiocouch)
* MongoDB (using motor and pymongo)
* SQLAlchemy (requires sqlalchemy async (+3.14))
* Oracle (requires oracledb)
### Quick Tutorial ###
```python
from asyncdb import AsyncDB
db = AsyncDB('pg', dsn='postgres://user:password@localhost:5432/database')
# Or you can also passing a dictionary with parameters like:
params = {
"user": "user",
"password": "password",
"host": "localhost",
"port": "5432",
"database": "database",
"DEBUG": True,
}
db = AsyncDB('pg', params=params)
async with await db.connection() as conn:
result, error = await conn.query('SELECT * FROM test')
```
And that's it!, we are using the same methods on all drivers, maintaining a consistent interface between all of them, facilitating the re-use of the same code for different databases.
Every Driver has a simple name to call it:
* pg: AsyncPG (PostgreSQL)
* postgres: aiopg (PostgreSQL)
* mysql: aiomysql (mySQL)
* influx: influxdb (InfluxDB)
* redis: redis-py (Redis)
* mcache: aiomcache (Memcache)
* odbc: aiodbc (ODBC)
* oracle: oracle (oracledb)
### Output Support ###
With Output Support results can be returned into a wide-range of variants:
```python
from datamodel import BaseModel
class Point(BaseModel):
col1: list
col2: list
col3: list
db = AsyncDB('pg', dsn='postgres://user:password@localhost:5432/database')
async with await d.connection() as conn:
# changing output format to Pandas:
conn.output_format('pandas') # change output format to pandas
result, error = await conn.query('SELECT * FROM test')
conn.output_format('csv') # change output format to CSV
result, _ = await conn.query('SELECT TEST')
conn.output_format('dataclass', model=Point) # change output format to Dataclass Model
result, _ = await conn.query('SELECT * FROM test')
```
Currently AsyncDB supports the following Output Formats:
* CSV (comma-separated or parametrized)
* JSON (using orjson)
* iterable (returns a generator)
* Recordset (Internal meta-Object for list of Records)
* Pandas (a pandas Dataframe)
* Datatable (Dt Dataframe)
* Dataclass (exporting data to a dataclass with -optionally- passing Dataclass instance)
* PySpark Dataframe
And others to come:
* Apache Arrow (using pyarrow)
* Polars (Using Python polars)
* Dask Dataframe
### Contribution guidelines ###
Please have a look at the Contribution Guide
* Writing tests
* Code review
### Who do I talk to? ###
* Repo owner or admin
* Other community or team contact
### License ###
AsyncDB is copyright of Jesus Lara (https://phenobarbital.info) and is licensed under BSD. I am providing code in this repository under an open source licenses, remember, this is my personal repository; the license that you receive is from me and not from my employeer.
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"description": "# AsyncDB #\n\nAsyncDB is a collection of different Database Drivers using asyncio-based connections and binary connectors (as asyncpg) but providing an abstraction layer to easily connect to different data sources, a high-level abstraction layer for various non-blocking database connectors,\non other blocking connectors (like MS SQL Server) we are using ThreadPoolExecutors to run in a non-blocking manner.\n\n### Why AsyncDB? ###\n\nThe finality of AsyncDB is to provide us with a subset of drivers (connectors) for accessing different databases and data sources for data interaction.\nThe main goal of AsyncDB is to use asyncio-based technologies.\n\n### Getting Started ###\n\n## Requirements\n\nPython 3.9+\n\n## Installation\n\n<div class=\"termy\">\n\n```console\n$ pip install asyncdb\n---> 100%\nSuccessfully installed asyncdb\n```\n\nCan also install only drivers required like:\n```console\n$ pip install asyncdb[pg] # this install only asyncpg\n```\nOr install all supported drivers as:\n\n```console\n$ pip install asyncdb[all]\n```\n\n### Requirements ###\n\n* Python >= 3.8\n* asyncio (https://pypi.python.org/pypi/asyncio/)\n\nCurrently AsyncDB supports the following databases:\n\n* PostgreSQL (supporting two different connectors: asyncpg or aiopg)\n* SQLite (requires aiosqlite)\n* mySQL/MariaDB (requires aiomysql and mysqlclient)\n* ODBC (using aioodbc)\n* JDBC(using JayDeBeApi and JPype)\n* RethinkDB (requires rethinkdb)\n* Redis (requires aioredis)\n* Memcache (requires aiomcache)\n* MS SQL Server (non-asyncio using freeTDS and pymssql)\n* Apache Cassandra (requires official cassandra driver)\n* InfluxDB (using influxdb)\n* CouchBase (using aiocouch)\n* MongoDB (using motor and pymongo)\n* SQLAlchemy (requires sqlalchemy async (+3.14))\n* Oracle (requires oracledb)\n\n### Quick Tutorial ###\n\n```python\nfrom asyncdb import AsyncDB\n\ndb = AsyncDB('pg', dsn='postgres://user:password@localhost:5432/database')\n\n# Or you can also passing a dictionary with parameters like:\nparams = {\n \"user\": \"user\",\n \"password\": \"password\",\n \"host\": \"localhost\",\n \"port\": \"5432\",\n \"database\": \"database\",\n \"DEBUG\": True,\n}\ndb = AsyncDB('pg', params=params)\n\nasync with await db.connection() as conn:\n result, error = await conn.query('SELECT * FROM test')\n```\nAnd that's it!, we are using the same methods on all drivers, maintaining a consistent interface between all of them, facilitating the re-use of the same code for different databases.\n\nEvery Driver has a simple name to call it:\n* pg: AsyncPG (PostgreSQL)\n* postgres: aiopg (PostgreSQL)\n* mysql: aiomysql (mySQL)\n* influx: influxdb (InfluxDB)\n* redis: redis-py (Redis)\n* mcache: aiomcache (Memcache)\n* odbc: aiodbc (ODBC)\n* oracle: oracle (oracledb)\n\n### Output Support ###\n\nWith Output Support results can be returned into a wide-range of variants:\n\n```python\nfrom datamodel import BaseModel\n\nclass Point(BaseModel):\n col1: list\n col2: list\n col3: list\n\ndb = AsyncDB('pg', dsn='postgres://user:password@localhost:5432/database')\nasync with await d.connection() as conn:\n # changing output format to Pandas:\n conn.output_format('pandas') # change output format to pandas\n result, error = await conn.query('SELECT * FROM test')\n conn.output_format('csv') # change output format to CSV\n result, _ = await conn.query('SELECT TEST')\n conn.output_format('dataclass', model=Point) # change output format to Dataclass Model\n result, _ = await conn.query('SELECT * FROM test')\n```\n\nCurrently AsyncDB supports the following Output Formats:\n\n* CSV (comma-separated or parametrized)\n* JSON (using orjson)\n* iterable (returns a generator)\n* Recordset (Internal meta-Object for list of Records)\n* Pandas (a pandas Dataframe)\n* Datatable (Dt Dataframe)\n* Dataclass (exporting data to a dataclass with -optionally- passing Dataclass instance)\n* PySpark Dataframe\n\nAnd others to come:\n* Apache Arrow (using pyarrow)\n* Polars (Using Python polars)\n* Dask Dataframe\n\n### Contribution guidelines ###\n\nPlease have a look at the Contribution Guide\n\n* Writing tests\n* Code review\n\n### Who do I talk to? ###\n\n* Repo owner or admin\n* Other community or team contact\n\n### License ###\n\nAsyncDB is copyright of Jesus Lara (https://phenobarbital.info) and is licensed under BSD. I am providing code in this repository under an open source licenses, remember, this is my personal repository; the license that you receive is from me and not from my employeer.\n",
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