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
$ pip install asyncdb
---> 100%
Successfully installed asyncdb
Can also install only drivers required like:
$ pip install asyncdb[pg] # this install only asyncpg
Or install all supported drivers as:
$ 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 (optional, requires aiomcache:
pip install asyncdb[memcache]) - 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)
- Redpanda (Kafka-compatible, requires aiokafka)
Platform Support
AsyncDB's core package (pip install asyncdb) and the default extra
(pip install asyncdb[default]) are supported on Linux, macOS, and Windows
for Python 3.10–3.14. uvloop remains an optional performance extra
(pip install asyncdb[uvloop]): it is only available on POSIX platforms and
is never required to import or run AsyncDB. On Windows, uvloop activation
is automatically and safely skipped, and the standard asyncio event loop
policy is used instead.
The default extra covers SQLite (aiosqlite), RethinkDB, InfluxDB, Redis,
MS SQL Server (pymssql), Delta Lake, and DuckDB, in addition to the
PostgreSQL support (asyncpg) that ships with the core package.
Other provider extras (Cassandra, ScyllaDB, MySQL/MySQLdb, ODBC, Oracle,
JDBC, ClickHouse, etc.) depend on native or platform-sensitive client
libraries and are not guaranteed to be installable or fully supported on
every platform, including Windows. Selecting a driver whose optional
dependency is not installed raises a focused error naming the provider and
the extra required to install it (e.g. pip install asyncdb[mysql]).
Quick Tutorial
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)
- memcache: aiomcache (Memcache, async, no system libraries required)
- odbc: aiodbc (ODBC)
- oracle: oracle (oracledb)
- redpanda: Redpanda/Kafka (aiokafka)
Output Support
With Output Support results can be returned into a wide-range of variants:
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.
2 maintainers
Metadata
2 maintainers
Release files for asyncdb 2.16.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 6.9 MB
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