qtasks 1.7.2

Queue Tasks Framework

QTasks

QTasks is a modern task queue framework for Python with a component-based architecture and a focus on extensibility, transparency, and control over task execution. The project is aimed at both small services and complex distributed systems where standard solutions are redundant or inflexible.

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Key features

• Component-based architecture Broker, Worker, Storage, GlobalConfig, Starter — each component is isolated and fully replaceable.

• Async and Sync tasks Support for asyncio, synchronous functions, and generators.

• Plugins instead of rigid logic Retry, concurrency, logging, and execution strategies are implemented through plugins.

• Typed data flow Data transfer between components via schemas (dataclasses).

• Transparent testing In-memory brokers and storages, component isolation, tests without running workers.

• CLI-first approach Manage startup and environment through CLI without hidden magic.

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When to choose QTasks

• You need full control over the queue architecture.
• You need to write your own brokers, workers, and plugins.
• Predictable behavior and minimalism of the core are important.
• The project is growing and requires scalability without rewriting the logic.

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Installation

Basic installation (Redis by default)

pip install qtasks

Additional brokers

RabbitMQ

pip install qtasks[rabbitmq]

Kafka

pip install qtasks[kafka]

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Quick start

from qtasks import QueueTasks

app = QueueTasks()

@app.task(name="echo")
def echo(text: str) -> str:
    return text

@app.task(name="divide")
def divide(a: int, b: int):
    return a / b

if __name__ == "__main__":
    app.run_forever()

Calling tasks:

# echo.add_task("Hello", timeout=50).returning -> "Hello"
# divide.add_task(1, 0, timeout=50).status -> "ERROR"

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Generators and streaming tasks

QTasks supports generator tasks without additional wrappers:

async def gen_handler(value: int) -> int:
    return value + 1

@app.task(generate_handler=gen_handler)
async def counter(n: int):
    for _ in range(n):
        n += 1
        yield n

Result of execution:

# await (counter.add_task(5, timeout=50)).returning -> [7, 8, 9, 10, 11]

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CLI

Starting the worker:

qtasks -A qtasks_app:app run

Or directly:

python -m qtasks -A qtasks_app:app run

In the future, the CLI will be expanded (inspect, stats, monitoring).

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Architecture (briefly)

┌──────────┐          ┌──────────┐
│  Broker  │ ───────▶ │          │
│          │          │          │
└────┬─────┘          │          │
     │                │ Storage  │
     ▼                │          │
┌──────────┐ ───────▶ │          │
│  Worker  │          │          │
│          │          └──────────┘
└──────────┘

Additionally:

• plugins;
• routers;
• background components;
• WebView (optional).

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Documentation

👉 https://docs.qtasks.tech

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Project status

• Active development
• Stable versions
• Python 3.8–3.12
• Open to contributions

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License

MIT License