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# Async Framework

A Python library for Redis-based message queue processing with monitoring and retry capabilities. This framework provides a robust solution for distributed message processing with features like automatic retries, dead letter queues, and Prometheus metrics.

## Features

- Redis-based message broker and consumer
- Configurable number of workers
- JSON message format with standardized structure
- Efficient Redis connection pooling
- Prometheus metrics for monitoring
- Automatic and manual retry mechanisms
- Dead letter queue for failed messages
- Batch processing support
- Distributed environment ready

## Requirements

- Python 3.10+
- Redis server
- Dependencies (automatically installed):
  - redis>=4.5.0
  - pydantic>=2.0.0
  - prometheus-client>=0.17.0
  - tenacity>=8.2.0
  - structlog>=23.1.0

## Installation

### Installing from Source
1. Clone the repository:
```bash

git clone https://github.com/yourusername/async-framework.git

cd async-framework

```

2. Install the package:
```bash

pip install .

```

### Building the Package
To build the package as a wheel file:

1. Install build requirements:
```bash

pip install build wheel

```

2. Build the package:
```bash

python -m build

```

This will create two files in the `dist` directory:
- `async_framework-0.1.0-py3-none-any.whl` (Wheel file)
- `async_framework-0.1.0.tar.gz` (Source distribution)

### Installing the Built Package

You can install the wheel file in other projects using:
```bash

pip install async_framework-0.1.0-py3-none-any.whl

```

Or install directly from your private PyPI repository:
```bash

pip install async-framework

```

## Usage

### Configuration

```python

from async_framework import BrokerConfig, RedisConfig, RetryConfig



config = BrokerConfig(

    redis=RedisConfig(

        host="localhost",

        port=6379,

        password="optional_password",

        connection_pool_size=10

    ),

    retry=RetryConfig(

        max_retries=3,

        initial_delay=1.0,

        max_delay=60.0,

        backoff_factor=2.0

    ),

    num_workers=4,

    batch_size=10,

    polling_interval=1.0,

    metrics_port=8000

)

```

### Publishing Messages

```python

from async_framework import MessageBroker



# Initialize the broker

broker = MessageBroker(config)



# Publish a message

message = broker.publish(

    queue="my_queue",

    payload={"key": "value"},

    max_retries=3  # Optional, defaults to config value

)

```

### Consuming Messages

```python

from async_framework import MessageConsumer, Message



# Define a message handler

def process_message(message: Message):

    print(f"Processing message {message.id}: {message.payload}")

    # Your processing logic here

    # Raise an exception to trigger retry mechanism



# Initialize the consumer

consumer = MessageConsumer(

    config=config,

    queue="my_queue",

    handler=process_message

)



# Start consuming messages

consumer.start()



try:

    # Keep the main thread running

    while True:

        time.sleep(1)

except KeyboardInterrupt:

    consumer.stop()

```

### Monitoring

The framework exposes Prometheus metrics at `http://localhost:8000` (configurable via `metrics_port`):

- `messages_published_total` - Counter of published messages by queue
- `messages_processed_total` - Counter of processed messages by queue and status
- `message_processing_seconds` - Histogram of message processing time by queue

### Queue Structure

The framework uses the following Redis key patterns:

- `queue:{queue_name}` - Main message queue
- `retry:{queue_name}` - Retry queue with scheduled messages
- `dead_letter:{queue_name}` - Dead letter queue for failed messages

### Message Format

Messages are stored in JSON format with the following structure:

```json

{

    "id": "uuid",

    "queue": "queue_name",

    "payload": {

        // Your message data

    },

    "created_at": "2023-08-26T12:00:00Z",

    "retry_count": 0,

    "max_retries": 3,

    "next_retry_at": null,

    "error": null

}

```

## Development

1. Create a virtual environment:
```bash

python -m venv venv

source venv/bin/activate  # Linux/MacOS

# or

.\\venv\\Scripts\\activate  # Windows

```

2. Install development dependencies:
```bash

pip install -e ".[dev]"

```

3. Run tests:
```bash

pytest

```

## License

MIT License. See LICENSE file for details.