Using Celery on processes and gevent in tasks at the same time

Using Celery on processes and gevent in tasks at the same time

Using Celery with both processes and gevent within tasks can be achieved by setting up Celery with a custom pool that combines process-based and gevent-based concurrency. This can be useful when you want to utilize the advantages of both process-based parallelism and lightweight, greenlet-based concurrency in your Celery tasks.

Here's a high-level overview of how you can set up Celery to use both processes and gevent:

  1. Install Dependencies:

    You will need to install Celery and gevent if you haven't already. You can do this using pip:

    pip install celery gevent

  2. Custom Pool:

    Create a custom Celery worker pool that combines the gevent and prefork (or solo) pool types. The prefork pool is for process-based concurrency, and the gevent pool is for greenlet-based concurrency.

    # custom_pool.py from celery.concurrency import get_implementation from celery.concurrency.base import BasePool from celery.platforms import signals class CustomPool(BasePool): Pool = None def __init__(self, *args, **kwargs): if get_implementation() == 'gevent': self.Pool = self._get_gevent_pool() else: self.Pool = self._get_prefork_pool() super(CustomPool, self).__init__(*args, **kwargs) def _get_gevent_pool(self): from gevent.pool import Pool return Pool def _get_prefork_pool(self): from multiprocessing import Pool return Pool def on_start(self): signals.worker_process_init.connect(self.on_worker_process_init) def on_stop(self): signals.worker_process_shutdown.connect(self.on_worker_process_shutdown) self.Pool.close() self.Pool.join() def on_worker_process_init(self, **kwargs): self.Pool._processes = self.limit def on_worker_process_shutdown(self, **kwargs): self.Pool.terminate() self.Pool.join()

  3. Celery Configuration:

    Configure Celery to use your custom pool in your Celery configuration file (usually celery.py or celeryconfig.py):

    # celeryconfig.py from custom_pool import CustomPool CELERY_IMPORTS = ('your_module',) CELERY_ACCEPT_CONTENT = ['json'] CELERY_TASK_SERIALIZER = 'json' CELERY_RESULT_SERIALIZER = 'json' CELERYD_POOL = CustomPool

  4. Define and Run Tasks:

    You can now define and run your Celery tasks as usual. Celery will use the custom pool you've configured to execute tasks with either process-based or greenlet-based concurrency, depending on the worker's concurrency model.

    # your_module.py from celery import Celery app = Celery('your_module') @app.task def your_task(): # Your task logic here

  5. Start Celery Worker:

    Start a Celery worker using the -P (or --pool) option to specify the concurrency pool:

    celery -A your_module worker --loglevel=info -P custom_pool

With this setup, Celery will use gevent for tasks when running in a gevent-based worker and processes for tasks when running in a process-based worker. This approach allows you to leverage both process-level parallelism and lightweight greenlet-based concurrency as needed for different types of tasks within the same Celery application.

Examples

  1. How to use Celery with process-based concurrency?

    • Description: This snippet demonstrates how to set up Celery to use processes for concurrency by configuring the Celery worker with --pool=processes.
    • # Command to start Celery with process-based concurrency celery -A myapp worker --pool=processes --concurrency=4

  2. How to use gevent in Celery tasks for asynchronous I/O?

    • Description: This snippet demonstrates how to enable gevent in Celery tasks for handling asynchronous I/O operations.
    • from celery import Celery import gevent app = Celery('myapp', broker='pyamqp://guest@localhost//') @app.task def async_task(url): # Simulate asynchronous network operation gevent.sleep(2) return f"Processed {url}"

  3. How to run Celery with gevent pool for concurrent tasks?

    • Description: This snippet shows how to start Celery with a gevent-based pool to handle concurrent tasks with non-blocking behavior.
    • # Command to start Celery with gevent pool celery -A myapp worker --pool=gevent --concurrency=10

  4. How to use Celery with mixed concurrency pools (processes and gevent)?

    • Description: This snippet demonstrates a way to configure Celery to use mixed concurrency pools, where tasks can choose different execution models.
    • from celery import Celery from celery.concurrency.gevent import TaskPool as GeventTaskPool app = Celery('myapp', broker='pyamqp://guest@localhost//') app.conf.update( worker_pool_restarts=True, worker_concurrency=4, worker_pool='gevent', # Using gevent for the pool ) @app.task def io_bound_task(): # Simulate I/O-bound operations with gevent gevent.sleep(2) return "I/O-bound task complete" @app.task def cpu_bound_task(): # Simulate CPU-bound operations return sum(range(100000))

  5. How to configure Celery worker with separate process and gevent pools?

    • Description: This snippet shows how to set up two separate Celery workers: one with process-based concurrency and another with gevent-based concurrency.
    • # Start Celery worker with process-based concurrency celery -A myapp worker --pool=processes --concurrency=4 -n worker_process # Start Celery worker with gevent-based concurrency celery -A myapp worker --pool=gevent --concurrency=10 -n worker_gevent

  6. How to set up Celery tasks to use different concurrency pools (process and gevent)?

    • Description: This snippet demonstrates how to use task routing to direct specific tasks to different Celery workers with varying concurrency pools.
    • from celery import Celery app = Celery('myapp', broker='pyamqp://guest@localhost//') app.conf.update( task_routes={ 'myapp.cpu_bound_task': {'queue': 'cpu_tasks'}, # Process-based worker 'myapp.io_bound_task': {'queue': 'io_tasks'}, # Gevent-based worker } ) @app.task def cpu_bound_task(): # Simulate CPU-bound operations return sum(range(100000)) @app.task def io_bound_task(): # Simulate I/O-bound operations import gevent gevent.sleep(2) return "I/O-bound task complete"

  7. How to create a Celery worker with custom concurrency pools (gevent for specific tasks)?

    • Description: This snippet demonstrates using custom configuration to set a Celery worker's concurrency pool to gevent while ensuring compatibility with different task types.
    • from celery import Celery import gevent app = Celery('myapp', broker='pyamqp://guest@localhost//') app.conf.update( worker_concurrency=10, worker_pool='gevent', # Using gevent for the pool ) @app.task def io_bound_task(): # Simulate asynchronous network operations gevent.sleep(2) return "I/O-bound task complete" @app.task def cpu_bound_task(): # Simulate CPU-bound operations return sum(range(100000))

  8. How to manage Celery worker scaling with gevent-based pool?

    • Description: This snippet demonstrates how to manage worker scaling for a Celery worker using gevent-based concurrency, allowing for a larger number of tasks with non-blocking behavior.
    • from celery import Celery app = Celery('myapp', broker='pyamqp://guest@localhost//') # Configure Celery worker to use gevent pool and set high concurrency app.conf.update( worker_concurrency=20, worker_pool='gevent', # Use gevent-based concurrency ) @app.task def io_bound_task(): # Simulate I/O-bound operations import gevent gevent.sleep(2) return "I/O-bound task complete"

  9. How to set up Celery tasks for optimal concurrency with mixed workloads?

    • Description: This snippet shows how to set up Celery tasks for optimal concurrency with a mix of CPU-bound and I/O-bound tasks, using different concurrency pools as needed.
    • from celery import Celery import gevent app = Celery('myapp', broker='pyamqp://guest@localhost//') # Configure Celery worker with mixed workloads app.conf.update( worker_concurrency=10, worker_pool='gevent', # Use gevent for non-blocking I/O tasks ) @app.task def io_bound_task(): # Simulate I/O-bound operations gevent.sleep(2) return "I/O-bound task complete" @app.task def cpu_bound_task(): # Simulate CPU-bound operations return sum(range(100000))

  10. How to use Celery with mixed process and gevent pools for different workloads?

    • Description: This snippet demonstrates setting up Celery with mixed concurrency pools, allowing certain tasks to run on process-based workers and others on gevent-based workers for optimal handling of mixed workloads.
    • # Start Celery worker with process-based concurrency for CPU-bound tasks celery -A myapp worker --pool=processes --concurrency=4 -n cpu_worker -Q cpu_tasks # Start Celery worker with gevent-based concurrency for I/O-bound tasks celery -A myapp worker --pool=gevent --concurrency=20 -n io_worker -Q io_tasks

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