std::ptrdiff_t { chunk_index == remainder ]}; 了] 了 y 一Range adaptorfor dlstributing work across tasks (CZ) ASM comparison > GCC RISC-V 64-bit assembly manual_loop(long，long): jsc-chunk_evenly unnecessary waiting > Iftasks are scheduled as early as possible, then distributin8g work evenly across tasks can improve performance (O) Future Directions > Support random access in jsc: :chunk_even1y_view<>

. . . . . . . . . . . . . 13 2.2 Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Executing Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5 Periodic Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.6 Sets of tasks, Subtasks and Callbacks . . . . . . . . . . . . . . . . . . . . 42 2.7 HTTP Callback Tasks (Webhooks) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.8 Routing Tasks . . . . . . . . . . . . . . . . . . . . . . .

Celery Resources User Guide Overview Tasks Executing Tasks Workers Guide Periodic Tasks Sets of tasks, Subtasks and Callbacks HTTP Callback Tasks (Webhooks) Routing Tasks Monitoring Guide Optimizing Configuration Configuration and defaults Example configuration file Configuration Directives Cookbook Creating Tasks Running celeryd as a daemon Community Resources Resources News Contributing Community Code of Conduct Security Brokers Tasks Windows API Reference Task Decorators - celery.decorators Defining Tasks - celery.task.base Task Sets, Subtasks and Callbacks - celery.task.sets Executing Tasks - celery.execute

scheduling as well. The execution units, called tasks, are executed concurrently on one or more worker nodes using multiprocessing, Eventlet or gevent. Tasks can execute asynchronously (in the background) or synchronously (wait until ready). Celery is used in production systems to process millions of tasks a day. Celery is written in Python, but the protocol can be implemented in any language. It can also Release 2.3.5 1.1.1 Overview This is a high level overview of the architecture. The broker delivers tasks to the worker nodes. A worker node is a networked machine running celeryd. This can be one or more

but supports scheduling as well. The execution units, called tasks, are executed concurrently on a single or more worker servers. Tasks can execute asynchronously (in the background) or synchronously synchronously (wait until ready). Celery is already used in production to process millions of tasks a day. Celery is written in Python, but the protocol can be implemented in any language. It can also operate with 3 (stable) 1.1.1 Overview This is a high level overview of the architecture. The broker pushes tasks to the worker servers. A worker server is a networked machine running celeryd. This can be one or

scheduling as well. The execution units, called tasks, are executed concurrently on one or more worker nodes using multiprocessing, Eventlet or gevent. Tasks can execute asynchronously (in the background) or synchronously (wait until ready). Celery is used in production systems to process millions of tasks every hour. 3 Celery Documentation, Release 2.5.5 Celery is written in Python, but the protocol web2py-celery 1.1.2 Overview This is a high level overview of the architecture. The broker delivers tasks to the worker nodes. A worker node is a networked machine running celeryd. This can be one or more

Celery Next Steps Resources User Guide Application Tasks Calling Tasks Canvas: Designing Work-flows Workers Guide Daemonization Periodic Tasks Routing Tasks Monitoring and Management Guide Security Optimizing Configuration Where to go from here Troubleshooting Next Steps Using Celery in your Application Calling Tasks Canvas: Designing Work-flows Routing Remote Control Timezone Optimization What to do now? Resources Primary/Primary or Primary/Replica replication. Fast A single Celery process can process millions of tasks a minute, with sub- millisecond round-trip latency (using RabbitMQ, librabbitmq, and optimized settings)

. . . . . . . . . . . . . 13 2.2 Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Executing Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5 Periodic Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.6 Sets of tasks, Subtasks and Callbacks . . . . . . . . . . . . . . . . . . . . 43 2.7 HTTP Callback Tasks (Webhooks) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.8 Routing Tasks . . . . . . . . . . . . . . . . . . . . . . .

Primary/Primary or Primary/Replica replication. • Fast A single Celery process can process millions of tasks a minute, with sub-millisecond round-trip latency (using RabbitMQ, librabbitmq, and optimized settings) grouping, chaining, chunking, and more. Read more... . • Time & Rate Limits You can control how many tasks can be executed per second/minute/hour, or how long a task can be allowed to run, and this can be Scheduling You can specify the time to run a task in seconds or a datetime, or or you can use periodic tasks for recurring events based on a simple interval, or Crontab expressions supporting minute, hour, day

Celery Next Steps Resources User Guide Application Tasks Calling Tasks Canvas: Designing Work-flows Workers Guide Daemonization Periodic Tasks Routing Tasks Monitoring and Management Guide Security Optimizing Configuration Where to go from here Troubleshooting Next Steps Using Celery in your Application Calling Tasks Canvas: Designing Work-flows Routing Remote Control Timezone Optimization What to do now? Resources Primary/Primary or Primary/Replica replication. Fast A single Celery process can process millions of tasks a minute, with sub- millisecond round-trip latency (using RabbitMQ, librabbitmq, and optimized settings)