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安装
pipenv install django-celery
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主配置文件导入celery配置
# settings.py from .celeryconfig import * BROKER_BECKEND = 'redis' BROKER_URL = 'redis://192.168.2.128:6379/1' CELERY_RESULT_BACKEND = 'redis://192.168.2.128:6379/2'
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celery配置文件
#celeryconfig.py from datetime import timedelta import djcelery # setup_loader用于扫描所有app下tasks.py文件中的task djcelery.setup_loader() # 设置不同的任务队列,将普通任务和定时任务分开,redis中实际key如下 # 1) "_kombu.binding.work_queue" # 2) "_kombu.binding.beat_tasks" CELERY_QUEUES = { 'beat_tasks': { 'exchange': 'beat_tasks', 'exchange_type': 'direct', 'binding_key': 'beat_tasks' }, 'work_queue': { 'exchange': 'work_queue', 'exchange_type': 'direct', 'binding_key': 'work_queue' }, } # 设置默认队列 CELERY_DEFAULT_QUEUE = 'work_queue' CELERY_IMPORTS = ( 'course.tasks', ) # 有些情况下可以防止死锁 CELERY_FORCE_EXECV = True # 设置并发的worker数量 CELERY_CONCURRENCY = 4 # 允许重试 CELERY_ACKS_LATE = True # 每个worker最多执行100个任务被销毁,可以防止内存泄漏 CELERY_MAX_TASKS_PER_CHILD = 100 # 单个任务的最大运行时间 CELERY_TASK_TIME_LIMIT = 12 * 30 # 定时任务配置 CELERYBEAT_SCHEDULE = { 'task1': { 'task': 'course-task', 'schedule': timedelta(seconds=5), 'options': { 'queue': 'beat_tasks' } } }
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app中创建tasks.py并创建task
# (app)course->tasks.py import time from celery.task import Task class CourseTask(Task): name = 'course-task' def run(self, *args, **kwargs): print('start course task') time.sleep(4) print('args={}, kwargs={}'.format(args, kwargs)) print('end course task')
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app视图函数调用task
# (app)course->views.py from django.http import JsonResponse from course.tasks import CourseTask def do(request): # 执行异步任务 print('start do request') # CourseTask.delay() CourseTask.apply_async(args=('hello',), queue='work_queue') print('end do request') return JsonResponse({'result': 'ok'})
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启动命令
python manage.py celery worker -l INFO python manage.py celery beat -l INFO