我正在寻找一种方法来限制何时调用函数，但仅当输入参数不同时，即：
api_call("antoine")所以我希望api_call("oscar")被称为每秒60次，@app.task(rate_limit="60/s")
def sub_api_call(user):
do_the_api_call()
@app.task
def api_call(user):
sub_api_call(user)
for i in range(0,100):
api_call("antoine")
api_call("oscar")
每秒60次。
有关如何做到这一点的任何帮助？
- 编辑27/04/2015 我曾尝试在任务中使用rate_limit调用子任务，但它也不起作用：rate_limit总是应用于所有实例化的子任务或任务（这是逻辑的）。
var value;
var data = ["A", "B", "C", "D"];
// add a separate call to the chain for each piece of data
for (var i = 0; i < data.length; i++) {
value = data[i];
$.when($.Deferred().resolve("test"), $.Deferred().resolve(value))
.then(function(response, x) {
console.log(response + ':' + x);
});
}
最佳！
答案 0 :(得分：1)
我今天花了一些时间来解决这个问题，并想出了一个不错的解决方案。所有其他解决方案都存在以下问题之一：
基本上，你这样包装你的任务：
@app.task(bind=True, max_retries=10)
@throttle_task("2/s", key="domain", jitter=(2, 15))
def scrape_domain(self, domain):
do_stuff()
结果是您将任务限制为每个域参数每秒运行 2 次，随机重试抖动在 2-15 秒之间。 key 参数是可选的，但对应于任务中的一个参数。如果没有给出关键参数，它只会将任务限制到给定的速率。如果提供，则节流将应用于 (task, key) dyad。
另一种看待这个的方式是没有装饰器。这提供了更多的灵活性，但需要您自己进行重试。您可以执行以下操作：
@app.task(bind=True, max_retries=10)
def scrape_domain(self, domain):
proceed = is_rate_okay(self, "2/s", key=domain)
if proceed:
do_stuff()
else:
self.request.retries = task.request.retries - 1 # Don't count this as against max_retries.
return task.retry(countdown=random.uniform(2, 15))
我认为这与第一个示例相同。更长一点，更多分支，但更清楚地展示了它的工作原理。我希望自己总是使用装饰器。
这一切都通过在 redis 中保持计数来实现。实现非常简单。您在 redis 中为任务创建一个密钥（以及密钥参数，如果给定），并根据提供的时间表使 redis 密钥过期。如果用户将速率设置为 10/m，则您创建一个 60 秒的 redis 键，并且每次尝试使用正确名称的任务时都会增加它。如果您的增量器变得太高，请重试该任务。否则，运行它。
def parse_rate(rate: str) -> Tuple[int, int]:
"""
Given the request rate string, return a two tuple of:
<allowed number of requests>, <period of time in seconds>
(Stolen from Django Rest Framework.)
"""
num, period = rate.split("/")
num_requests = int(num)
if len(period) > 1:
# It takes the form of a 5d, or 10s, or whatever
duration_multiplier = int(period[0:-1])
duration_unit = period[-1]
else:
duration_multiplier = 1
duration_unit = period[-1]
duration_base = {"s": 1, "m": 60, "h": 3600, "d": 86400}[duration_unit]
duration = duration_base * duration_multiplier
return num_requests, duration
def throttle_task(
rate: str,
jitter: Tuple[float, float] = (1, 10),
key: Any = None,
) -> Callable:
"""A decorator for throttling tasks to a given rate.
:param rate: The maximum rate that you want your task to run. Takes the
form of '1/m', or '10/2h' or similar.
:param jitter: A tuple of the range of backoff times you want for throttled
tasks. If the task is throttled, it will wait a random amount of time
between these values before being tried again.
:param key: An argument name whose value should be used as part of the
throttle key in redis. This allows you to create per-argument throttles by
simply passing the name of the argument you wish to key on.
:return: The decorated function
"""
def decorator_func(func: Callable) -> Callable:
@functools.wraps(func)
def wrapper(*args, **kwargs) -> Any:
# Inspect the decorated function's parameters to get the task
# itself and the value of the parameter referenced by key.
sig = inspect.signature(func)
bound_args = sig.bind(*args, **kwargs)
task = bound_args.arguments["self"]
key_value = None
if key:
try:
key_value = bound_args.arguments[key]
except KeyError:
raise KeyError(
f"Unknown parameter '{key}' in throttle_task "
f"decorator of function {task.name}. "
f"`key` parameter must match a parameter "
f"name from function signature: '{sig}'"
)
proceed = is_rate_okay(task, rate, key=key_value)
if not proceed:
logger.info(
"Throttling task %s (%s) via decorator.",
task.name,
task.request.id,
)
# Decrement the number of times the task has retried. If you
# fail to do this, it gets auto-incremented, and you'll expend
# retries during the backoff.
task.request.retries = task.request.retries - 1
return task.retry(countdown=random.uniform(*jitter))
else:
# All set. Run the task.
return func(*args, **kwargs)
return wrapper
return decorator_func
def is_rate_okay(task: Task, rate: str = "1/s", key=None) -> bool:
"""Keep a global throttle for tasks
Can be used via the `throttle_task` decorator above.
This implements the timestamp-based algorithm detailed here:
https://www.figma.com/blog/an-alternative-approach-to-rate-limiting/
Basically, you keep track of the number of requests and use the key
expiration as a reset of the counter.
So you have a rate of 5/m, and your first task comes in. You create a key:
celery_throttle:task_name = 1
celery_throttle:task_name.expires = 60
Another task comes in a few seconds later:
celery_throttle:task_name = 2
Do not update the ttl, it now has 58s remaining
And so forth, until:
celery_throttle:task_name = 6
(10s remaining)
We're over the threshold. Re-queue the task for later. 10s later:
Key expires b/c no more ttl.
Another task comes in:
celery_throttle:task_name = 1
celery_throttle:task_name.expires = 60
And so forth.
:param task: The task that is being checked
:param rate: How many times the task can be run during the time period.
Something like, 1/s, 2/h or similar.
:param key: If given, add this to the key placed in Redis for the item.
Typically, this will correspond to the value of an argument passed to the
throttled task.
:return: Whether the task should be throttled or not.
"""
key = f"celery_throttle:{task.name}{':' + str(key) if key else ''}"
r = make_redis_interface("CACHE")
num_tasks, duration = parse_rate(rate)
# Check the count in redis
count = r.get(key)
if count is None:
# No key. Set the value to 1 and set the ttl of the key.
r.set(key, 1)
r.expire(key, duration)
return True
else:
# Key found. Check it.
if int(count) <= num_tasks:
# We're OK, run it.
r.incr(key, 1)
return True
else:
return False
答案 1 :(得分：0)
我认为这不可能通过Celery的内置任务限制器来实现。
假设您正在为API使用某种缓存，最好的解决方案可能是创建任务名称和args的哈希值，并将该键用于基于缓存的限制器。
如果您正在使用Redis，则可以设置60秒超时锁定，或使用增量计数器计算每分钟的呼叫数。
这篇文章可能会给你一些关于使用Redis分配限制Celery任务的指示：
https://callhub.io/blog/2014/02/03/distributed-rate-limiting-with-redis-and-celery/