Workers 指南¶

Workers Guide

启动 worker¶

Starting the worker

中文

守护进程化（Daemonizing）

你可能希望使用守护进程工具将 worker 以后台进程的方式启动。请参阅守护进程，了解如何使用常见的服务管理器将 worker 启动为守护进程。

你也可以通过以下命令在前台启动 worker：

$ celery -A proj worker -l INFO

要查看完整的命令行选项列表，请参阅 worker，或直接运行：

$ celery worker --help

你可以在同一台机器上启动多个 worker，但请确保通过 --hostname 参数为每个 worker 指定唯一的节点名：

$ celery -A proj worker --loglevel=INFO --concurrency=10 -n worker1@%h
$ celery -A proj worker --loglevel=INFO --concurrency=10 -n worker2@%h
$ celery -A proj worker --loglevel=INFO --concurrency=10 -n worker3@%h

hostname 参数支持以下变量展开：

%h：主机名，包含域名。
%n：仅主机名。
%d：仅域名。

假设当前主机名为 george.example.com，这些变量将被展开为：

变量	模板	展开结果
`%h`	`worker1@%h`	worker1@george.example.com
`%n`	`worker1@%n`	worker1@george
`%d`	`worker1@%d`	worker1@example.com

针对 https://pypi.org/project/supervisor/ 用户的注意事项

% 符号必须通过添加第二个 % 进行转义： %%h。

英文

Daemonizing

You probably want to use a daemonization tool to start the worker in the background. See 守护进程 for help starting the worker as a daemon using popular service managers.

You can start the worker in the foreground by executing the command:

$ celery -A proj worker -l INFO

For a full list of available command-line options see worker, or simply do:

$ celery worker --help

You can start multiple workers on the same machine, but be sure to name each individual worker by specifying a node name with the --hostname argument:

$ celery -A proj worker --loglevel=INFO --concurrency=10 -n worker1@%h
$ celery -A proj worker --loglevel=INFO --concurrency=10 -n worker2@%h
$ celery -A proj worker --loglevel=INFO --concurrency=10 -n worker3@%h

The hostname argument can expand the following variables:

%h: Hostname, including domain name.
%n: Hostname only.
%d: Domain name only.

If the current hostname is george.example.com, these will expand to:

Variable	Template	Result
`%h`	`worker1@%h`	worker1@george.example.com
`%n`	`worker1@%n`	worker1@george
`%d`	`worker1@%d`	worker1@example.com

Note for https://pypi.org/project/supervisor/ users

The % sign must be escaped by adding a second one: %%h.

重启 worker¶

Restarting the worker

中文

若要重启 worker，应先发送 TERM 信号终止原有进程，再启动新实例。开发环境下建议使用 celery multi 管理 worker：

$ celery multi start 1 -A proj -l INFO -c4 --pidfile=/var/run/celery/%n.pid
$ celery multi restart 1 --pidfile=/var/run/celery/%n.pid

对于生产部署，建议使用初始化脚本或进程管理工具（见守护进程）。

除了停止再启动外，还可以使用 HUP 信号直接重启 worker。需要注意的是，此方式由 worker 自行负责重启，可能不稳定，因此不建议在生产环境中使用：

$ kill -HUP $pid

备注

只有当 worker 在后台以守护进程（daemon）方式运行（即无控制终端）时， HUP 重启才有效。

macOS 平台因系统限制，禁用了 HUP 信号的处理。

英文

To restart the worker you should send the TERM signal and start a new instance. The easiest way to manage workers for development is by using celery multi:

$ celery multi start 1 -A proj -l INFO -c4 --pidfile=/var/run/celery/%n.pid
$ celery multi restart 1 --pidfile=/var/run/celery/%n.pid

For production deployments you should be using init-scripts or a process supervision system (see 守护进程).

Other than stopping, then starting the worker to restart, you can also restart the worker using the HUP signal. Note that the worker will be responsible for restarting itself so this is prone to problems and isn't recommended in production:

$ kill -HUP $pid

备注

Restarting by HUP only works if the worker is running in the background as a daemon (it doesn't have a controlling terminal).

HUP is disabled on macOS because of a limitation on that platform.

与broker服务器连接丢失时自动重新连接¶

Automatic re-connection on connection loss to broker

中文

Added in version 5.3.

除非将 broker_connection_retry_on_startup 设置为 False， Celery 会在首次连接中断后自动尝试重新连接到 broker。而 broker_connection_retry 控制后续连接中断时是否自动重试重新连接。

Added in version 5.1.

如果将 worker_cancel_long_running_tasks_on_connection_loss 设置为 True， Celery 也会取消所有当前正在运行的长时间运行任务。

Added in version 5.3.

由于消息中间件无法追踪连接丢失前已取出的任务数量， Celery 会将预取计数（prefetch count）减少为当前正在运行的任务数量乘以 worker_prefetch_multiplier 的结果。该预取计数将在每次完成一个连接丢失前启动的任务后，逐步恢复至允许的最大值。

此特性默认启用，可通过将 worker_enable_prefetch_count_reduction 设置为 False 来禁用。

英文

Added in version 5.3.

Unless broker_connection_retry_on_startup is set to False, Celery will automatically retry reconnecting to the broker after the first connection loss. broker_connection_retry controls whether to automatically retry reconnecting to the broker for subsequent reconnects.

Added in version 5.1.

If worker_cancel_long_running_tasks_on_connection_loss is set to True, Celery will also cancel any long running task that is currently running.

Added in version 5.3.

Since the message broker does not track how many tasks were already fetched before the connection was lost, Celery will reduce the prefetch count by the number of tasks that are currently running multiplied by worker_prefetch_multiplier. The prefetch count will be gradually restored to the maximum allowed after each time a task that was running before the connection was lost is complete.

This feature is enabled by default, but can be disabled by setting False to worker_enable_prefetch_count_reduction.

进程信号¶

Process Signals

中文

Worker 的主进程会覆盖以下信号处理行为：

英文

The worker's main process overrides the following signals:

`TERM`	Warm shutdown, wait for tasks to complete.
`QUIT`	Cold shutdown, terminate ASAP
`USR1`	Dump traceback for all active threads.
`USR2`	Remote debug, see `celery.contrib.rdb`.

文件路径中的变量¶

Variables in file paths

中文

--logfile、--pidfile 和 --statedb 等参数的文件路径可以包含变量占位符， Worker 会在运行时对其进行展开：

英文

The file path arguments for --logfile, --pidfile, and --statedb can contain variables that the worker will expand:

节点名称替换¶

Node name replacements

中文

%p：完整节点名（node name）。
%h：主机名（包含域名）。
%n：主机名（不包含域名）。
%d：域名部分。
%i：Prefork 池中子进程索引，主进程为 0。
%I：带分隔符的子进程索引。

例如，当前主机名为 george@foo.example.com 时，将展开为：

--logfile=%p.log -> george@foo.example.com.log
--logfile=%h.log -> foo.example.com.log
--logfile=%n.log -> george.log
--logfile=%d.log -> example.com.log

英文

%p: Full node name.
%h: Hostname, including domain name.
%n: Hostname only.
%d: Domain name only.
%i: Prefork pool process index or 0 if MainProcess.
%I: Prefork pool process index with separator.

For example, if the current hostname is george@foo.example.com then these will expand to:

--logfile=%p.log -> george@foo.example.com.log
--logfile=%h.log -> foo.example.com.log
--logfile=%n.log -> george.log
--logfile=%d.log -> example.com.log

Prefork 池进程索引¶

Prefork pool process index

中文

Prefork 子进程索引相关的占位符，会根据具体要打开该文件的进程展开为不同的文件名。

这可用于为每个子进程指定独立的日志文件。

需要注意的是：即使进程退出，或启用了 autoscale、maxtasksperchild、执行时间限制等机制，索引号也会保持在进程数限制内。换言之，该数字表示的是“进程索引”而非进程数量或进程 ID。

%i - 池中子进程索引，主进程为 0。

例如：使用 -n worker1@example.com -c2 -f %n-%i.log 时，会生成三个日志文件：
- worker1-0.log （主进程）
- worker1-1.log （子进程 1）
- worker1-2.log （子进程 2）
%I - 带分隔符的池中子进程索引。

例如：使用 -n worker1@example.com -c2 -f %n%I.log 时，会生成三个日志文件：
- worker1.log （主进程）
- worker1-1.log （子进程 1）
- worker1-2.log （子进程 2）

英文

The prefork pool process index specifiers will expand into a different filename depending on the process that'll eventually need to open the file.

This can be used to specify one log file per child process.

Note that the numbers will stay within the process limit even if processes exit or if autoscale/maxtasksperchild/time limits are used. That is, the number is the process index not the process count or pid.

%i - Pool process index or 0 if MainProcess.

Where -n worker1@example.com -c2 -f %n-%i.log will result in three log files:
- worker1-0.log (main process)
- worker1-1.log (pool process 1)
- worker1-2.log (pool process 2)
%I - Pool process index with separator.

Where -n worker1@example.com -c2 -f %n%I.log will result in three log files:
- worker1.log (main process)
- worker1-1.log (pool process 1)
- worker1-2.log (pool process 2)

并发¶

Concurrency

中文

默认情况下，Celery 使用多进程（multiprocessing）机制来并发执行任务，但你也可以使用 Eventlet。工作进程/线程的数量可以通过 --concurrency 参数进行设置，默认值为机器上可用的 CPU 数量。

进程数量（multiprocessing/prefork 池）

通常来说，进程数量越多性能越好，但当数量达到某个临界点后，增加进程数反而会导致性能下降。有一些证据表明，运行多个 Worker 实例有时比只运行一个 Worker 更高效。例如，运行 3 个 Worker，每个都有 10 个池进程。你需要自行尝试以找到最适合你场景的配置，因为这会受到应用类型、工作负载、任务运行时间以及其他因素的影响。

英文

By default multiprocessing is used to perform concurrent execution of tasks, but you can also use Eventlet. The number of worker processes/threads can be changed using the --concurrency argument and defaults to the number of CPUs available on the machine.

Number of processes (multiprocessing/prefork pool)

More pool processes are usually better, but there's a cut-off point where adding more pool processes affects performance in negative ways. There's even some evidence to support that having multiple worker instances running, may perform better than having a single worker. For example 3 workers with 10 pool processes each. You need to experiment to find the numbers that works best for you, as this varies based on application, work load, task run times and other factors.

远程控制¶

Remote control

中文

Added in version 2.0.

pool 支持:: prefork, eventlet, gevent, thread, 阻塞型: solo （参见注释）
broker 支持:: amqp, redis

Worker 支持通过高优先级的广播消息队列进行远程控制。这些命令可以发送给所有 Worker，也可以只发送给指定的 Worker 列表。

命令也可以请求回应。客户端可等待并收集这些回应。由于系统中没有中央服务可用来判断当前集群中有多少个 Worker，因此也无法准确预估将收到多少回应。为此，客户端提供了一个可配置的超时时间，即等待回应到达的时间上限（以秒为单位），默认值为 1 秒。如果在截止时间内 Worker 没有回应，这并不一定意味着该 Worker 没有响应，或者更糟糕的是已经挂掉，可能仅仅是因为网络延迟，或该 Worker 在处理命令时比较慢，因此应根据情况调整超时时间。

除了超时时间，客户端还可以设置最多等待多少个回应。如果指定了目标 Worker，这一限制将默认为目标主机数。

备注

solo 类型的池也支持远程控制命令，但如果某个任务正在执行，任何待处理的控制命令都会被阻塞。因此当 Worker 比较繁忙时， solo 的远程控制用途会受到限制。此时应在客户端中提高等待回应的超时时间。

英文

Added in version 2.0.

pool support:: prefork, eventlet, gevent, thread, blocking:solo (see note)
broker support:: amqp, redis

Workers have the ability to be remote controlled using a high-priority broadcast message queue. The commands can be directed to all, or a specific list of workers.

Commands can also have replies. The client can then wait for and collect those replies. Since there's no central authority to know how many workers are available in the cluster, there's also no way to estimate how many workers may send a reply, so the client has a configurable timeout — the deadline in seconds for replies to arrive in. This timeout defaults to one second. If the worker doesn't reply within the deadline it doesn't necessarily mean the worker didn't reply, or worse is dead, but may simply be caused by network latency or the worker being slow at processing commands, so adjust the timeout accordingly.

In addition to timeouts, the client can specify the maximum number of replies to wait for. If a destination is specified, this limit is set to the number of destination hosts.

备注

The solo pool supports remote control commands, but any task executing will block any waiting control command, so it is of limited use if the worker is very busy. In that case you must increase the timeout waiting for replies in the client.

`broadcast()` 函数¶

The broadcast() function

中文

以下是客户端用于向 Worker 发送指令的函数。某些远程控制命令也提供了更高级的接口，这些接口在内部会调用 broadcast()，例如 rate_limit() 和 ping()。

以下为发送 rate_limit 命令和参数的示例：

>>> app.control.broadcast('rate_limit',
...                          arguments={'task_name': 'myapp.mytask',
...                                     'rate_limit': '200/m'})

此操作将异步发送命令，并不会等待回应。若希望获取回应，可使用 reply 参数：

>>> app.control.broadcast('rate_limit', {
...     'task_name': 'myapp.mytask', 'rate_limit': '200/m'}, reply=True)
[{'worker1.example.com': 'New rate limit set successfully'},
{'worker2.example.com': 'New rate limit set successfully'},
{'worker3.example.com': 'New rate limit set successfully'}]

通过 destination 参数可以指定命令接收的 Worker 列表：

>>> app.control.broadcast('rate_limit', {
...     'task_name': 'myapp.mytask',
...     'rate_limit': '200/m'}, reply=True,
...                             destination=['worker1@example.com'])
[{'worker1.example.com': 'New rate limit set successfully'}]

当然，使用高级接口来设置速率限制更为方便，但也存在一些只能通过 broadcast() 发送的命令。

英文

This is the client function used to send commands to the workers. Some remote control commands also have higher-level interfaces using broadcast() in the background, like rate_limit(), and ping().

Sending the rate_limit command and keyword arguments:

>>> app.control.broadcast('rate_limit',
...                          arguments={'task_name': 'myapp.mytask',
...                                     'rate_limit': '200/m'})

This will send the command asynchronously, without waiting for a reply. To request a reply you have to use the reply argument:

>>> app.control.broadcast('rate_limit', {
...     'task_name': 'myapp.mytask', 'rate_limit': '200/m'}, reply=True)
[{'worker1.example.com': 'New rate limit set successfully'},
{'worker2.example.com': 'New rate limit set successfully'},
{'worker3.example.com': 'New rate limit set successfully'}]

Using the destination argument you can specify a list of workers to receive the command:

>>> app.control.broadcast('rate_limit', {
...     'task_name': 'myapp.mytask',
...     'rate_limit': '200/m'}, reply=True,
...                             destination=['worker1@example.com'])
[{'worker1.example.com': 'New rate limit set successfully'}]

Of course, using the higher-level interface to set rate limits is much more convenient, but there are commands that can only be requested using broadcast().

时间限制¶

Time Limits

中文

Added in version 2.0.

pool 支持:: prefork/gevent（见下文说明）

Soft，还是 hard？

时间限制由两个值控制：soft 和 hard。 soft 时间限制允许任务捕获异常进行清理；而 hard 限制无法被捕获，直接强制终止任务。

单个任务有可能会无限期运行，如果有许多任务都在等待某个永远不会发生的事件，那么就会导致 Worker 被阻塞，无法处理新的任务。防止此类情况的最佳做法是启用任务的时间限制。

时间限制（--time-limit）用于设置任务允许的最长期限（单位：秒），超过该时间后执行任务的进程将被终止并由新进程取代。你也可以启用 soft 时间限制（--soft-time-limit），它会抛出一个异常，任务可以在此异常中进行清理，然后再被硬性终止：

from myapp import app
from celery.exceptions import SoftTimeLimitExceeded

@app.task
def mytask():
    try:
        do_work()
    except SoftTimeLimitExceeded:
        clean_up_in_a_hurry()

你也可以使用 task_time_limit / task_soft_time_limit 设置来配置时间限制。此外，也可以在客户端使用 AsyncResult.get() 函数的 timeout 参数设置超时。

备注

时间限制目前不适用于不支持 SIGUSR1 信号的平台。

备注

gevent 池不支持 soft 时间限制；另外，如果任务处于阻塞状态，它也不会强制执行 hard 时间限制。

英文

Added in version 2.0.

pool support:: prefork/gevent (see note below)

Soft, or hard?

The time limit is set in two values, soft and hard. The soft time limit allows the task to catch an exception to clean up before it is killed: the hard timeout isn't catch-able and force terminates the task.

A single task can potentially run forever, if you have lots of tasks waiting for some event that'll never happen you'll block the worker from processing new tasks indefinitely. The best way to defend against this scenario happening is enabling time limits.

The time limit (--time-limit) is the maximum number of seconds a task may run before the process executing it is terminated and replaced by a new process. You can also enable a soft time limit (--soft-time-limit), this raises an exception the task can catch to clean up before the hard time limit kills it:

from myapp import app
from celery.exceptions import SoftTimeLimitExceeded

@app.task
def mytask():
    try:
        do_work()
    except SoftTimeLimitExceeded:
        clean_up_in_a_hurry()

Time limits can also be set using the task_time_limit / task_soft_time_limit settings. You can also specify time limits for client side operation using timeout argument of AsyncResult.get() function.

备注

Time limits don't currently work on platforms that don't support the SIGUSR1 signal.

备注

The gevent pool does not implement soft time limits. Additionally, it will not enforce the hard time limit if the task is blocking.

在运行时更改时间限制¶

Changing time limits at run-time

中文

Added in version 2.3.

broker 支持:: amqp, redis

有一个远程控制命令允许你动态修改任务的 soft 和 hard 时间限制 —— 名为 time_limit。

例如，将任务 tasks.crawl_the_web 的 soft 时间限制设置为 1 分钟， hard 时间限制设置为 2 分钟：

>>> app.control.time_limit('tasks.crawl_the_web',
...                        soft=60, hard=120, reply=True)
[{'worker1.example.com': {'ok': 'time limits set successfully'}}]

英文

Added in version 2.3.

broker support:: amqp, redis

There's a remote control command that enables you to change both soft and hard time limits for a task — named time_limit.

Example changing the time limit for the tasks.crawl_the_web task to have a soft time limit of one minute, and a hard time limit of two minutes:

>>> app.control.time_limit('tasks.crawl_the_web',
                        soft=60, hard=120, reply=True)
[{'worker1.example.com': {'ok': 'time limits set successfully'}}]

Only tasks that starts executing after the time limit change will be affected.

速率限制¶

Rate Limits

在运行时更改速率限制¶

Changing rate-limits at run-time

中文

只有在该时间限制变更之后开始执行的任务才会受到影响。

另一个示例是修改任务 myapp.mytask 的速率限制，使其最多每分钟执行 200 次：

>>> app.control.rate_limit('myapp.mytask', '200/m')

上述示例未指定目标 Worker，因此该更改会影响集群中的所有 Worker 实例。如果你只希望更改某些特定的 Worker，可以添加 destination 参数：

>>> app.control.rate_limit('myapp.mytask', '200/m',
...            destination=['celery@worker1.example.com'])

警告

如果 Worker 启用了 worker_disable_rate_limits 设置，则此命令将不会生效。

英文

Example changing the rate limit for the myapp.mytask task to execute at most 200 tasks of that type every minute:

>>> app.control.rate_limit('myapp.mytask', '200/m')

The above doesn't specify a destination, so the change request will affect all worker instances in the cluster. If you only want to affect a specific list of workers you can include the destination argument:

>>> app.control.rate_limit('myapp.mytask', '200/m',
...            destination=['celery@worker1.example.com'])

警告

This won't affect workers with the worker_disable_rate_limits setting enabled.

每个子进程的最大任务数设置¶

Max tasks per child setting

中文

Added in version 2.0.

pool 支持:: prefork

通过此选项，你可以配置单个 Worker 进程在被替换前最多可执行的任务数。

这在某些情况下非常有用，例如你使用了某些闭源的 C 扩展，并且它们存在你无法控制的内存泄漏问题。

该选项可以通过 Worker 命令行参数 --max-tasks-per-child 设置，也可以通过配置项 worker_max_tasks_per_child 设置。

英文

Added in version 2.0.

pool support:: prefork

With this option you can configure the maximum number of tasks a worker can execute before it's replaced by a new process.

This is useful if you have memory leaks you have no control over for example from closed source C extensions.

The option can be set using the workers --max-tasks-per-child argument or using the worker_max_tasks_per_child setting.

每个子进程的最大内存设置¶

Max memory per child setting

中文

Added in version 4.0.

pool 支持:: prefork

通过此选项，你可以设置 Worker 在驻留内存（resident memory）使用达到指定上限后被新进程替换。

这在你无法控制内存泄漏的情况下非常有用，例如使用了闭源的 C 扩展。

该选项可以通过 Worker 命令行参数 --max-memory-per-child 设置，也可以通过配置项 worker_max_memory_per_child 设置。

英文

Added in version 4.0.

pool support:: prefork

With this option you can configure the maximum amount of resident memory a worker can execute before it's replaced by a new process.

This is useful if you have memory leaks you have no control over for example from closed source C extensions.

The option can be set using the workers --max-memory-per-child argument or using the worker_max_memory_per_child setting.

自动扩缩¶

Autoscaling

中文

Added in version 2.2.

pool 支持:: prefork, gevent

autoscaler （自动伸缩器）组件用于根据负载动态调整 Worker 池的大小：

当有任务要处理时，autoscaler 会增加更多的池进程；
当负载降低时，它会开始减少池进程的数量。

可以使用 --autoscale 选项启用 autoscaler，该选项需要两个数字：最大和最小池进程数：

--autoscale=AUTOSCALE
    通过指定 max_concurrency,min_concurrency 来启用自动伸缩。例如：
    --autoscale=10,3 （始终保持至少 3 个进程，如有需要可扩展至最多 10 个）

你还可以通过继承 Autoscaler 来自定义 autoscaler 行为。可以使用的指标包括系统负载平均值、可用内存大小等。你可以通过配置项 worker_autoscaler 指定自定义 autoscaler。

英文

Added in version 2.2.

pool support:: prefork, gevent

The autoscaler component is used to dynamically resize the pool based on load:

The autoscaler adds more pool processes when there is work to do,
and starts removing processes when the workload is low.

It's enabled by the --autoscale option, which needs two numbers: the maximum and minimum number of pool processes:

--autoscale=AUTOSCALE
    Enable autoscaling by providing
    max_concurrency,min_concurrency.  Example:
    --autoscale=10,3 (always keep 3 processes, but grow to
    10 if necessary).

You can also define your own rules for the autoscaler by subclassing Autoscaler. Some ideas for metrics include load average or the amount of memory available. You can specify a custom autoscaler with the worker_autoscaler setting.

队列¶

Queues

中文

一个 Worker 实例可以消费任意数量的队列。默认情况下，它会消费 task_queues 设置中定义的所有队列（如果未定义则默认使用名为 celery 的队列）。

你可以在启动时使用 -Q 选项传入用逗号分隔的队列列表，指定要消费的队列：

$ celery -A proj worker -l INFO -Q foo,bar,baz

如果队列名称在 task_queues 中有定义，将使用该定义的配置；如果未定义，Celery 将根据 task_create_missing_queues 设置自动为你创建新队列。

你也可以在运行时通过远程控制命令 add_consumer 和 cancel_consumer 来动态添加或移除消费的队列。

英文

A worker instance can consume from any number of queues. By default it will consume from all queues defined in the task_queues setting (that if not specified falls back to the default queue named celery).

You can specify what queues to consume from at start-up, by giving a comma separated list of queues to the -Q option:

$ celery -A proj worker -l INFO -Q foo,bar,baz

If the queue name is defined in task_queues it will use that configuration, but if it's not defined in the list of queues Celery will automatically generate a new queue for you (depending on the task_create_missing_queues option).

You can also tell the worker to start and stop consuming from a queue at run-time using the remote control commands add_consumer and cancel_consumer.

队列：添加消费者¶

Queues: Adding consumers

中文

add_consumer 控制命令会通知一个或多个 Worker 开始消费某个队列。该操作是幂等的。

要让集群中所有 Worker 开始消费名为 foo 的队列，可以使用 celery control 命令：

$ celery -A proj control add_consumer foo
-> worker1.local: OK
    started consuming from u'foo'

如果只希望某个特定 Worker 执行此操作，可以使用 --destination 参数：

$ celery -A proj control add_consumer foo -d celery@worker1.local

同样的操作也可以通过 app.control.add_consumer() 方法动态完成：

>>> app.control.add_consumer('foo', reply=True)
[{u'worker1.local': {u'ok': u"already consuming from u'foo'"}}]

>>> app.control.add_consumer('foo', reply=True,
...                          destination=['worker1@example.com'])
[{u'worker1.local': {u'ok': u"already consuming from u'foo'"}}]

以上示例展示的是基于自动队列配置的用法，如果你需要更细粒度的控制，也可以自定义 exchange、routing_key 甚至其他选项：

>>> app.control.add_consumer(
...     queue='baz',
...     exchange='ex',
...     exchange_type='topic',
...     routing_key='media.*',
...     options={
...         'queue_durable': False,
...         'exchange_durable': False,
...     },
...     reply=True,
...     destination=['w1@example.com', 'w2@example.com'])

英文

The add_consumer control command will tell one or more workers to start consuming from a queue. This operation is idempotent.

To tell all workers in the cluster to start consuming from a queue named "foo" you can use the celery control program:

$ celery -A proj control add_consumer foo
-> worker1.local: OK
    started consuming from u'foo'

If you want to specify a specific worker you can use the --destination argument:

$ celery -A proj control add_consumer foo -d celery@worker1.local

The same can be accomplished dynamically using the app.control.add_consumer() method:

>>> app.control.add_consumer('foo', reply=True)
[{u'worker1.local': {u'ok': u"already consuming from u'foo'"}}]

>>> app.control.add_consumer('foo', reply=True,
...                          destination=['worker1@example.com'])
[{u'worker1.local': {u'ok': u"already consuming from u'foo'"}}]

By now we've only shown examples using automatic queues, If you need more control you can also specify the exchange, routing_key and even other options:

>>> app.control.add_consumer(
...     queue='baz',
...     exchange='ex',
...     exchange_type='topic',
...     routing_key='media.*',
...     options={
...         'queue_durable': False,
...         'exchange_durable': False,
...     },
...     reply=True,
...     destination=['w1@example.com', 'w2@example.com'])

队列：取消消费者¶

Queues: Canceling consumers

中文

你可以使用 cancel_consumer 控制命令，通过队列名称取消一个消费者（consumer）。

如果你想强制集群中所有 Worker 停止消费某个队列，可以使用 celery control 命令：

$ celery -A proj control cancel_consumer foo

你还可以使用 --destination 参数，指定一个或多个 Worker 来执行该命令：

$ celery -A proj control cancel_consumer foo -d celery@worker1.local

你也可以通过编程方式调用 app.control.cancel_consumer() 方法取消队列消费：

>>> app.control.cancel_consumer('foo', reply=True)
[{u'worker1.local': {u'ok': u"no longer consuming from u'foo'"}}]

英文

You can cancel a consumer by queue name using the cancel_consumer control command.

To force all workers in the cluster to cancel consuming from a queue you can use the celery control program:

$ celery -A proj control cancel_consumer foo

The --destination argument can be used to specify a worker, or a list of workers, to act on the command:

$ celery -A proj control cancel_consumer foo -d celery@worker1.local

You can also cancel consumers programmatically using the app.control.cancel_consumer() method:

>>> app.control.cancel_consumer('foo', reply=True)
[{u'worker1.local': {u'ok': u"no longer consuming from u'foo'"}}]

队列：活动队列列表¶

Queues: List of active queues

中文

你可以使用 active_queues 控制命令获取某个 Worker 当前正在消费的队列列表：

$ celery -A proj inspect active_queues
[...]

与其他远程控制命令一样，你也可以使用 --destination 参数来指定哪些 Worker 应当响应该请求：

$ celery -A proj inspect active_queues -d celery@worker1.local
[...]

你还可以通过编程方式调用 active_queues() 方法实现相同的功能：

>>> app.control.inspect().active_queues()
[...]

>>> app.control.inspect(['worker1.local']).active_queues()
[...]

英文

You can get a list of queues that a worker consumes from by using the active_queues control command:

$ celery -A proj inspect active_queues
[...]

Like all other remote control commands this also supports the --destination argument used to specify the workers that should reply to the request:

$ celery -A proj inspect active_queues -d celery@worker1.local
[...]

This can also be done programmatically by using the active_queues() method:

>>> app.control.inspect().active_queues()
[...]

>>> app.control.inspect(['worker1.local']).active_queues()
[...]

附加命令¶

Additional Commands

远程关闭¶

Remote shutdown

中文

以下命令可优雅地远程关闭 Worker：

>>> app.control.broadcast('shutdown') # 关闭所有 Worker
>>> app.control.broadcast('shutdown', destination='worker1@example.com')

英文

This command will gracefully shut down the worker remotely:

>>> app.control.broadcast('shutdown') # shutdown all workers
>>> app.control.broadcast('shutdown', destination='worker1@example.com')

Ping¶

中文

以下命令用于请求所有存活的 Worker 返回 ping 响应。 Worker 会回复字符串 'pong'，仅此而已。该方法默认超时时间为 1 秒，你也可以自定义超时：

>>> app.control.ping(timeout=0.5)
[{'worker1.example.com': 'pong'},
{'worker2.example.com': 'pong'},
{'worker3.example.com': 'pong'}]

ping() 也支持 destination 参数，因此你可以指定要发送 ping 的 Worker：

>>> ping(['worker2.example.com', 'worker3.example.com'])
[{'worker2.example.com': 'pong'},
{'worker3.example.com': 'pong'}]

英文

This command requests a ping from alive workers. The workers reply with the string 'pong', and that's just about it. It will use the default one second timeout for replies unless you specify a custom timeout:

>>> app.control.ping(timeout=0.5)
[{'worker1.example.com': 'pong'},
{'worker2.example.com': 'pong'},
{'worker3.example.com': 'pong'}]

ping() also supports the destination argument, so you can specify the workers to ping:

>>> ping(['worker2.example.com', 'worker3.example.com'])
[{'worker2.example.com': 'pong'},
{'worker3.example.com': 'pong'}]

启用/禁用事件¶

Enable/disable events

中文

你可以使用 enable_events 和 disable_events 命令来启用/禁用事件。这对于使用 celery events/celerymon 临时监控 Worker 非常有用。

>>> app.control.enable_events()
>>> app.control.disable_events()

英文

You can enable/disable events by using the enable_events, disable_events commands. This is useful to temporarily monitor a worker using celery events/celerymon.

>>> app.control.enable_events()
>>> app.control.disable_events()

编写您自己的远程控制命令¶

Writing your own remote control commands

中文

远程控制命令有两种类型：

Inspect 命令

不会有副作用，通常只是返回 Worker 中找到的一些值，比如当前已注册任务的列表、正在执行的任务列表等。
Control 命令

会有副作用，比如添加一个新的队列供消费。

远程控制命令会在控制面板中注册，并且接受一个参数：当前的 celery.worker.control.ControlDispatch 实例。从这里你可以访问活动的 Consumer，如果需要的话。

下面是一个控制命令的例子，用于增加任务的预取计数：

from celery.worker.control import control_command

@control_command(
    args=[('n', int)],
    signature='[N=1]',  # <- 用于命令行帮助。
)
def increase_prefetch_count(state, n=1):
    state.consumer.qos.increment_eventually(n)
    return {'ok': 'prefetch count incremented'}

确保将此代码添加到一个被 Worker 导入的模块中：这个模块可以与定义 Celery 应用程序的模块相同，或者你可以将该模块添加到 imports 设置中。

重新启动 Worker，以便注册控制命令，现在你可以使用 celery control 工具调用你的命令：

$ celery -A proj control increase_prefetch_count 3

你还可以将动作添加到 celery inspect 程序中，例如一个读取当前预取计数的命令：

from celery.worker.control import inspect_command

@inspect_command()
def current_prefetch_count(state):
    return {'prefetch_count': state.consumer.qos.value}

重新启动 Worker 后，你现在可以使用 celery inspect 程序查询此值：

$ celery -A proj inspect current_prefetch_count

英文

There are two types of remote control commands:

Inspect command

Does not have side effects, will usually just return some value found in the worker, like the list of currently registered tasks, the list of active tasks, etc.
Control command

Performs side effects, like adding a new queue to consume from.

Remote control commands are registered in the control panel and they take a single argument: the current celery.worker.control.ControlDispatch instance. From there you have access to the active Consumer if needed.

Here's an example control command that increments the task prefetch count:

from celery.worker.control import control_command

@control_command(
    args=[('n', int)],
    signature='[N=1]',  # <- used for help on the command-line.
)
def increase_prefetch_count(state, n=1):
    state.consumer.qos.increment_eventually(n)
    return {'ok': 'prefetch count incremented'}

Make sure you add this code to a module that is imported by the worker: this could be the same module as where your Celery app is defined, or you can add the module to the imports setting.

Restart the worker so that the control command is registered, and now you can call your command using the celery control utility:

$ celery -A proj control increase_prefetch_count 3

You can also add actions to the celery inspect program, for example one that reads the current prefetch count:

from celery.worker.control import inspect_command

@inspect_command()
def current_prefetch_count(state):
    return {'prefetch_count': state.consumer.qos.value}

After restarting the worker you can now query this value using the celery inspect program:

$ celery -A proj inspect current_prefetch_count

Workers 指南¶

启动 worker¶

停止 worker¶

关闭 worker¶

热关闭¶

冷关闭¶

软关闭¶

硬关闭¶

重启 worker¶

与broker服务器连接丢失时自动重新连接¶

进程信号¶

文件路径中的变量¶

节点名称替换¶

Prefork 池进程索引¶

并发¶

远程控制¶

broadcast() 函数¶

命令¶

revoke: 撤销任务¶

撤销多个任务¶

持久性撤销¶

revoke_by_stamped_headers: 通过标记的标头撤销任务¶

通过标记的标头撤销多个任务¶

时间限制¶

在运行时更改时间限制¶

速率限制¶

在运行时更改速率限制¶

每个子进程的最大任务数设置¶

每个子进程的最大内存设置¶

自动扩缩¶

队列¶

队列：添加消费者¶

队列：取消消费者¶

队列：活动队列列表¶

审查 worker¶

已注册队列的转储¶

当前正在执行的任务的转储¶

转储计划 (ETA) 任务¶

转储预留任务¶

统计信息¶

附加命令¶

远程关闭¶

Ping¶

启用/禁用事件¶

编写您自己的远程控制命令¶

`broadcast()` 函数¶

`revoke`: 撤销任务¶

`revoke_by_stamped_headers`: 通过标记的标头撤销任务¶