How to make celery's main worker process execute only one task of the same type at the same time
To ensure that a Celery worker's main process executes only one instance of a specific task type at a time, you can utilize a combination of Celery's concurrency settings and custom task pre-processing logic.
**Approach 1: Utilizing Celery's Concurrency Settings**
Celery provides concurrency settings that allow you to control the number of worker processes and threads, potentially limiting the execution of specific task types. However, this approach has limitations and might not be suitable for all scenarios.
1. **Configure Concurrency Settings:**
In your Celery configuration file (`celeryconfig.py`), modify the `worker_concurrency` and `worker_autoscale` settings. For instance, to limit the main process to one worker and disable autoscaling, set:
```python
worker_concurrency = 1
worker_autoscale = False
```
2. **Implement Task Pre-processing:**
Define a custom task pre-processing function that checks for existing instances of the same task type and prevents execution if one is already running.
```python
def check_for_running_task(task):
# Check if an instance of the task is already running
# ... (implement check logic)
if is_task_running:
# Prevent execution if a task is already running
raise Celery.exceptions.AlreadyStarted("Task already running")
@app.task(before_start=check_for_running_task)
def my_task(arg1, arg2):
# Task code goes here
pass
```
**Approach 2: Implementing Custom Worker Logic**
An alternative approach involves creating a custom worker class that manages the execution of tasks and ensures only one instance of a specific task type runs at a time.
1. **Create Custom Worker Class:**
Create a custom worker class that inherits from Celery's `Worker` class. Override the `process_task` method to control task execution.
```python
class SingleInstanceWorker(Worker):
def process_task(self, task, *args, **kwargs):
# Check if an instance of the task is already running
# ... (implement check logic)
if is_task_running:
# Skip execution if a task is already running
return
# Execute the task
super().process_task(task, *args, **kwargs)
```
2. **Start Custom Worker:**
When starting the Celery worker, specify the custom worker class using the `-W` option:
```bash
celery worker -W my_project.celery:SingleInstanceWorker
```
Remember that these approaches introduce some overhead and might impact overall performance. Evaluate your specific requirements and choose the method that best suits your needs.