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https://github.com/danfimov/taskiq-postgres

PostgreSQL integration for Taskiq with support for asyncpg, psycopg, psqlpy and aiopg drivers.
https://github.com/danfimov/taskiq-postgres

postgresql taskiq taskiq-broker taskiq-result-backend

Last synced: 5 months ago
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PostgreSQL integration for Taskiq with support for asyncpg, psycopg, psqlpy and aiopg drivers.

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PostgreSQL integration for Taskiq with support for asyncpg, psqlpy, psycopg and aiopg drivers.

## Features

- **PostgreSQL Broker** - high-performance message broker using PostgreSQL LISTEN/NOTIFY;
- **Result Backend** - persistent task result storage with configurable retention;
- **Scheduler Source** - cron-like task scheduling with PostgreSQL persistence;
- **Multiple Drivers** - support for asyncpg, psycopg3, psqlpy and aiopg;
- **Flexible Configuration** - customizable table names, field types, and connection options;
- **Multiple Serializers** - support for different serialization methods (Pickle, JSON, etc.).

See usage guide in [documentation](https://danfimov.github.io/taskiq-postgres/) or explore examples in [separate directory](https://github.com/danfimov/taskiq-postgres/examples).

## Installation

Depending on your preferred PostgreSQL driver, you can install this library with the corresponding extra:

```bash
# with asyncpg
pip install taskiq-postgres[asyncpg]

# with psqlpy
pip install taskiq-postgres[psqlpy]

# with psycopg3
pip install taskiq-postgres[psycopg]

# with aiopg
pip install taskiq-postgres[aiopg]
```

## Quick start

### Basic task processing

1. Define your broker with [asyncpg](https://github.com/MagicStack/asyncpg):

```python
# broker_example.py
import asyncio
from taskiq_pg.asyncpg import AsyncpgBroker, AsyncpgResultBackend

dsn = "postgres://taskiq_postgres:look_in_vault@localhost:5432/taskiq_postgres"
broker = AsyncpgBroker(dsn).with_result_backend(AsyncpgResultBackend(dsn))

@broker.task("solve_all_problems")
async def best_task_ever() -> None:
"""Solve all problems in the world."""
await asyncio.sleep(2)
print("All problems are solved!")

async def main():
await broker.startup()
task = await best_task_ever.kiq()
print(await task.wait_result())
await broker.shutdown()

if __name__ == "__main__":
asyncio.run(main())
```

2. Start a worker to process tasks (by default taskiq runs two instances of worker):

```bash
taskiq worker broker_example:broker
```

3. Run `broker_example.py` file to send a task to the worker:

```bash
python broker_example.py
```

Your experience with other drivers will be pretty similar. Just change the import statement and that's it.

### Task scheduling

1. Define your broker and schedule source:

```python
# scheduler_example.py
import asyncio
from taskiq import TaskiqScheduler
from taskiq_pg.asyncpg import AsyncpgBroker, AsyncpgScheduleSource

dsn = "postgres://taskiq_postgres:look_in_vault@localhost:5432/taskiq_postgres"
broker = AsyncpgBroker(dsn)
scheduler = TaskiqScheduler(
broker=broker,
sources=[AsyncpgScheduleSource(
dsn=dsn,
broker=broker,
)],
)

@broker.task(
task_name="solve_all_problems",
schedule=[
{
"cron": "*/1 * * * *", # type: str, either cron or time should be specified.
"cron_offset": None, # type: str | None, can be omitted. For example "Europe/Berlin".
"time": None, # type: datetime | None, either cron or time should be specified.
"args": [], # type list[Any] | None, can be omitted.
"kwargs": {}, # type: dict[str, Any] | None, can be omitted.
"labels": {}, # type: dict[str, Any] | None, can be omitted.
},
],
)
async def best_task_ever() -> None:
"""Solve all problems in the world."""
await asyncio.sleep(2)
print("All problems are solved!")

```

2. Start worker processes:

```bash
taskiq worker scheduler_example:broker
```

3. Run scheduler process:

```bash
taskiq scheduler scheduler_example:scheduler
```

## Motivation

There are too many libraries for PostgreSQL and Taskiq integration. Although they have different view on interface and different functionality.
To address this issue I created this library with a common interface for most popular PostgreSQL drivers that handle similarity across functionality of result backends, brokers and schedule sources.