| Name | async-q JSON |
| Version |
0.2
JSON |
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| home_page | |
| Summary | A simple worker task queue with async support |
| upload_time | 2023-09-12 16:34:29 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | |
| keywords |
async
workers
queue
redis
distributed
|
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# AsyncQ
A simple worker task queue with async support
## Installation
```bash
pip install async_q
```
## Features
1. Submit asynchronously task
2. Task routing
3. Distributed async worker process
## Use Case: Asynchronous Task Queue for I/O-Bound Task Processing
### Overview
In scenarios where you need to execute I/O-bound tasks asynchronously within a single application, the "async_q" library provides a straightforward solution. This use case illustrates how to create and utilize the library for such purposes.
### Prerequisites
Before using the "async_q" library, ensure you have Python 3.8 and Redis (Version 5.0 to current) installed, along with the required dependencies.
### Setting Up the Application
#### 1. Creating the Async Task Queue App
Begin by creating an instance of the `AsyncTaskQueue` to set up your application. In this example, we will name our app "async_q_app" and configure it to use Redis as the message broker.
```python
# main_app.py
from async_q import AsyncTaskQueue, RedisBuilder
# Define Async Task Queue App
async_q_app = AsyncTaskQueue(
redis_builder=RedisBuilder(
port='6379',
)
)
```
#### 2. Defining the Task Function
Next, define a task function that will be submitted to the queue for processing. In this example, we have a task function named `my_task`. This function simulates I/O waiting with a specified delay.
```python
# my_task.py
import logging
import asyncio
from async_q import submit_task
# For initializing the app
from main_app import async_q_app
async def my_task(idx, delay=2, *args, **kwargs):
# Simulate I/O waiting
await asyncio.sleep(delay)
logging.info(f'{idx} has finished the task. Task ID: {kwargs.get("task_id")}')
```
#### 3. Submitting Tasks
To submit tasks for processing, you can use the `submit_task` function. In this example, we submit 20 tasks to be processed by the queue.
```python
if __name__ == '__main__':
for i in range(20):
submit_task(my_task, kwargs={'idx': i, 'delay': 10})
```
In the previous example, the `submit_task` function also includes a `queue` argument with the default value set to `default` . This offers flexibility in case you want to specify a different queue for a particular task, but if no queue is provided, it will use the default value of `default`.
```python
if __name__ == '__main__':
for i in range(20):
# Submit a task with a default queue value
submit_task(my_task, kwargs={'idx': i, 'delay': 10, 'queue': 'default'})
```
### Running Worker Processes
#### 4. Initializing Worker Processes
In order to efficiently handle incoming tasks, it's crucial to set up and launch worker processes. The level of concurrency can be precisely determined by employing the `-c` flag. In the following example, we will initiate five worker processes that target the `async_q_app` module specified in `main_app.py`. By default, these workers will exclusively process tasks from the `default` queue.
```bash
$ python -m async_q -a main_app.py:async_q_app -c 5
```
However, you also have the flexibility to assign specific workers to process tasks from different queues, as demonstrated below:
```bash
$ python -m async_q -a main_app.py:async_q_app -c 5 -q mail_ps
```
By making these adjustments, you can tailor the behavior of your worker processes to suit your application's specific requirements.
#### 5. Submitting Tasks for Processing
With the worker processes running, you can now submit tasks for processing. Use the following command to execute the `my_task.py` script, which submits tasks to the queue.
```bash
$ python my_task.py
```
### Result
The worker processes will asynchronously process the submitted tasks with the specified delays. You can monitor the progress and completion of tasks through log messages generated by the `my_task` function. The "Async Queue" library is suitable for I/O-bound workloads that benefit from asynchronous processing within a single application.
## Todo
1. Test and check back compatibility
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"description": "# AsyncQ\r\nA simple worker task queue with async support\r\n## Installation\r\n```bash\r\npip install async_q\r\n```\r\n\r\n## Features\r\n1. Submit asynchronously task \r\n2. Task routing\r\n3. Distributed async worker process\r\n\r\n## Use Case: Asynchronous Task Queue for I/O-Bound Task Processing\r\n\r\n### Overview\r\nIn scenarios where you need to execute I/O-bound tasks asynchronously within a single application, the \"async_q\" library provides a straightforward solution. This use case illustrates how to create and utilize the library for such purposes.\r\n\r\n### Prerequisites\r\nBefore using the \"async_q\" library, ensure you have Python 3.8 and Redis (Version 5.0 to current) installed, along with the required dependencies.\r\n\r\n\r\n### Setting Up the Application\r\n\r\n#### 1. Creating the Async Task Queue App\r\nBegin by creating an instance of the `AsyncTaskQueue` to set up your application. In this example, we will name our app \"async_q_app\" and configure it to use Redis as the message broker.\r\n\r\n```python\r\n# main_app.py\r\n\r\nfrom async_q import AsyncTaskQueue, RedisBuilder\r\n\r\n# Define Async Task Queue App\r\nasync_q_app = AsyncTaskQueue(\r\n redis_builder=RedisBuilder(\r\n port='6379',\r\n )\r\n)\r\n```\r\n\r\n#### 2. Defining the Task Function\r\nNext, define a task function that will be submitted to the queue for processing. In this example, we have a task function named `my_task`. This function simulates I/O waiting with a specified delay.\r\n\r\n```python\r\n# my_task.py\r\n\r\nimport logging\r\nimport asyncio\r\nfrom async_q import submit_task\r\n\r\n# For initializing the app\r\nfrom main_app import async_q_app\r\n\r\nasync def my_task(idx, delay=2, *args, **kwargs):\r\n # Simulate I/O waiting\r\n await asyncio.sleep(delay)\r\n\r\n logging.info(f'{idx} has finished the task. Task ID: {kwargs.get(\"task_id\")}')\r\n```\r\n\r\n#### 3. Submitting Tasks\r\nTo submit tasks for processing, you can use the `submit_task` function. In this example, we submit 20 tasks to be processed by the queue.\r\n\r\n```python\r\nif __name__ == '__main__':\r\n for i in range(20):\r\n submit_task(my_task, kwargs={'idx': i, 'delay': 10})\r\n```\r\n\r\nIn the previous example, the `submit_task` function also includes a `queue` argument with the default value set to `default` . This offers flexibility in case you want to specify a different queue for a particular task, but if no queue is provided, it will use the default value of `default`.\r\n\r\n```python\r\nif __name__ == '__main__':\r\n for i in range(20):\r\n # Submit a task with a default queue value\r\n submit_task(my_task, kwargs={'idx': i, 'delay': 10, 'queue': 'default'})\r\n```\r\n\r\n### Running Worker Processes\r\n\r\n\r\n\r\n#### 4. Initializing Worker Processes\r\n\r\nIn order to efficiently handle incoming tasks, it's crucial to set up and launch worker processes. The level of concurrency can be precisely determined by employing the `-c` flag. In the following example, we will initiate five worker processes that target the `async_q_app` module specified in `main_app.py`. By default, these workers will exclusively process tasks from the `default` queue.\r\n\r\n```bash\r\n$ python -m async_q -a main_app.py:async_q_app -c 5\r\n```\r\n\r\nHowever, you also have the flexibility to assign specific workers to process tasks from different queues, as demonstrated below:\r\n\r\n```bash\r\n$ python -m async_q -a main_app.py:async_q_app -c 5 -q mail_ps\r\n```\r\n\r\nBy making these adjustments, you can tailor the behavior of your worker processes to suit your application's specific requirements.\r\n\r\n#### 5. Submitting Tasks for Processing\r\nWith the worker processes running, you can now submit tasks for processing. Use the following command to execute the `my_task.py` script, which submits tasks to the queue.\r\n\r\n```bash\r\n$ python my_task.py\r\n```\r\n\r\n### Result\r\nThe worker processes will asynchronously process the submitted tasks with the specified delays. You can monitor the progress and completion of tasks through log messages generated by the `my_task` function. The \"Async Queue\" library is suitable for I/O-bound workloads that benefit from asynchronous processing within a single application.\r\n\r\n## Todo\r\n1. Test and check back compatibility\r\n\r\n",
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