我正在一个服务器中使用cherrypy来实现类似REST的API.
这些反应意味着一些繁重的计算需要大约2秒钟
请求.为了进行这种计算,使用了一些更新的数据
一天一次.
数据在后台更新(大约需要半小时),
一旦更新,新数据的引用就会传递给
响应请求的函数.这只需要一个毫秒.
我需要的是确保每个请求都以
旧数据或新数据,但在更改数据引用时不会发生任何请求处理.理想情况下,我希望找到一种在更改数据引用时缓冲传入请求的方法,并确保在所有进程内请求完成后更改引用.
我当前(非)工作的最小例子如下：
@H_502_17@import time
import cherrypy
from cherrypy.process import plugins
theData = 0
def processData():
"""Backround task works for half hour three times a day,and when finishes it publish it in the engine buffer."""
global theData # using global variables to simplify the example
theData += 1
cherrypy.engine.publish("doChangeData",theData)
class DataPublisher(object):
def __init__(self):
self.data = 'initData'
cherrypy.engine.subscribe('doChangeData',self.changeData)
def changeData(self,newData):
cherrypy.engine.log("Changing data,buffering should start!")
self.data = newData
time.sleep(1) #exageration of the 1 milisec of the references update to visualize the problem
cherrypy.engine.log("Continue serving buffered and new requests.")
@cherrypy.expose
def index(self):
result = "I get "+str(self.data)
cherrypy.engine.log(result)
time.sleep(3)
return result
if __name__ == '__main__':
conf = {
'/': { 'server.socket_host': '127.0.0.1','server.socket_port': 8080}
}
cherrypy.config.update(conf)
btask = plugins.BackgroundTask(5,processData) #5 secs for the example
btask.start()
cherrypy.quickstart(DataPublisher())
如果我运行此脚本,并且还打开浏览器,请将localhost：8080放入并刷新
页面很多,我得到：
...
[17/Sep/2015:21:32:41] ENGINE Changing data,buffering should start!
127.0.0.1 - - [17/Sep/2015:21:32:41] "GET / HTTP/1.1" 200 7 "...
[17/Sep/2015:21:32:42] ENGINE I get 3
[17/Sep/2015:21:32:42] ENGINE Continue serving buffered and new requests.
127.0.0.1 - - [17/Sep/2015:21:24:44] "GET / HTTP/1.1" 200 7 "...
...
这意味着一些请求处理在之前和之后开始
数据引用开始或结束更改.我想避免这两种情况.
就像是：
...
127.0.0.1 - - [17/Sep/2015:21:32:41] "GET / HTTP/1.1" 200 7 "...
[17/Sep/2015:21:32:41] ENGINE Changing data,buffering should start!
[17/Sep/2015:21:32:42] ENGINE Continue serving buffered and new requests.
[17/Sep/2015:21:32:42] ENGINE I get 3
127.0.0.1 - - [17/Sep/2015:21:24:44] "GET / HTTP/1.1" 200 7 "...
...
我搜索了文档和网页,发现这些引用并不完全涵盖这种情况：
http://www.defuze.org/archives/198-managing-your-process-with-the-cherrypy-bus.html
How to execute asynchronous post-processing in CherryPy?
http://tools.cherrypy.org/wiki/BackgroundTaskQueue
Cherrypy : which solutions for pages with large processing time
How to stop request processing in Cherrypy?
更新(使用简单的解决方案)：
经过深思熟虑之后,我认为这个问题具有误导性,因为它包含了问题本身的一些实现要求,即：停止处理并开始缓冲.对于该问题,可以将需求简化为：确保使用旧数据或新数据处理每个请求.
对于后者,足以存储所使用数据的时间局部参考.此引用可用于所有请求处理,如果另一个线程更改self.data,则没有问题.对于python对象,垃圾收集器将处理旧数据.
具体来说,通过以下方式更改索引函数就足够了：
@H_502_17@@cherrypy.expose
def index(self):
tempData = self.data
result = "I started with %s"%str(tempData)
time.sleep(3) # Heavy use of tempData
result += " that changed to %s"%str(self.data)
result += " but I am still using %s"%str(tempData)
cherrypy.engine.log(result)
return result
结果我们会看到：
[21 / Sep / 2015：10：06：00]引擎我从1开始改为2,但我仍然使用1
我仍然希望保留原始(更具限制性)的问题和cyraxjoe的答案,因为我发现这些解决方案非常有用.
第一个是基于插件的.
基于插件还需要一种同步.它只能起作用,因为只有一个BackgroundTask进行修改(也只是一个原子操作).
@H_502_17@import time
import threading
import cherrypy
from cherrypy.process import plugins
UPDATE_INTERVAL = 0.5
REQUEST_DELAY = 0.1
UPDATE_DELAY = 0.1
THREAD_POOL_SIZE = 20
next_data = 1
class DataGateway(plugins.SimplePlugin):
def __init__(self,bus):
super(DataGateway,self).__init__(bus)
self.data = next_data
def start(self):
self.bus.log("Starting DataGateway")
self.bus.subscribe('dg:get',self._get_data)
self.bus.subscribe('dg:update',self._update_data)
self.bus.log("DataGateway has been started")
def stop(self):
self.bus.log("Stopping DataGateway")
self.bus.unsubscribe('dg:get',self._get_data)
self.bus.unsubscribe('dg:update',self._update_data)
self.bus.log("DataGateway has been stopped")
def _update_data(self,new_val):
self.bus.log("Changing data,buffering should start!")
self.data = new_val
time.sleep(UPDATE_DELAY)
self.bus.log("Continue serving buffered and new requests.")
def _get_data(self):
return self.data
def processData():
"""Backround task works for half hour three times a day,and when finishes it publish it in the engine buffer."""
global next_data
cherrypy.engine.publish("dg:update",next_data)
next_data += 1
class DataPublisher(object):
@property
def data(self):
return cherrypy.engine.publish('dg:get').pop()
@cherrypy.expose
def index(self):
result = "I get " + str(self.data)
cherrypy.engine.log(result)
time.sleep(REQUEST_DELAY)
return result
if __name__ == '__main__':
conf = {
'global': {
'server.thread_pool': THREAD_POOL_SIZE,'server.socket_host': '127.0.0.1','server.socket_port': 8080,}
}
cherrypy.config.update(conf)
DataGateway(cherrypy.engine).subscribe()
plugins.BackgroundTask(UPDATE_DELAY,processData).start()
cherrypy.quickstart(DataPublisher())
在这个版本中,同步来自于read& amp; write操作在cherrypy.engine线程上执行.所有内容都是在您刚刚操作发布到引擎中的插件DataGateway上抽象出来的.
第二种方法是使用Event a threading.Event对象.这是一种更加手动的方法,其额外的好处是,由于读取速度更快,因此它可能会更快,因为它不会在cherrypy.engine线程上执行.
threading.Event based(a.k.a.manual)
@H_502_17@import time
import cherrypy
import threading
from cherrypy.process import plugins
UPDATE_INTERVAL = 0.5
REQUEST_DELAY = 0.1
UPDATE_DELAY = 0.1
THREAD_POOL_SIZE = 20
next_data = 1
def processData():
"""Backround task works for half hour three times a day,and when finishes it publish it in the engine buffer."""
global next_data
cherrypy.engine.publish("doChangeData",next_data)
next_data += 1
class DataPublisher(object):
def __init__(self):
self._data = next_data
self._data_readable = threading.Event()
cherrypy.engine.subscribe('doChangeData',self.changeData)
@property
def data(self):
if self._data_readable.is_set():
return self._data
else:
self._data_readable.wait()
return self.data
@data.setter
def data(self,value):
self._data_readable.clear()
time.sleep(UPDATE_DELAY)
self._data = value
self._data_readable.set()
def changeData(self,buffering should start!")
self.data = newData
cherrypy.engine.log("Continue serving buffered and new requests.")
@cherrypy.expose
def index(self):
result = "I get " + str(self.data)
cherrypy.engine.log(result)
time.sleep(REQUEST_DELAY)
return result
if __name__ == '__main__':
conf = {
'global': {
'server.thread_pool': THREAD_POOL_SIZE,}
}
cherrypy.config.update(conf)
plugins.BackgroundTask(UPDATE_INTERVAL,processData).start()
cherrypy.quickstart(DataPublisher())
我已经在@property装饰器中添加了一些细节,但真正的要点是在threading.Event以及DataPublisher对象在工作线程之间共享的事实.
我还添加了两个示例中增加线程池大小所需的线程池配置.默认值为10.
作为一种测试我刚刚说过的方法,你可以执行这个Python 3脚本(如果你现在没有python3,你有一个安装它的借口)它会在给定线程池的情况下或多或少地同时执行100个请求.
测试脚本
@H_502_17@import time
import urllib.request
import concurrent.futures
URL = 'http://localhost:8080/'
TIMEOUT = 60
DELAY = 0.05
MAX_WORKERS = 20
REQ_RANGE = range(1,101)
def load_url():
with urllib.request.urlopen(URL,timeout=TIMEOUT) as conn:
return conn.read()
with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
futures = {}
for i in REQ_RANGE:
print("Sending req {}".format(i))
futures[executor.submit(load_url)] = i
time.sleep(DELAY)
results = []
for future in concurrent.futures.as_completed(futures):
try:
data = future.result().decode()
except Exception as exc:
print(exc)
else:
results.append((futures[future],data))
curr_max = 0
for i,data in sorted(results,key=lambda r: r[0]):
new_max = int(data.split()[-1])
assert new_max >= curr_max,"The data was not updated correctly"
print("Req {}: {}".format(i,data))
curr_max = new_max
根据日志确定您遇到问题的方式,这种问题不值得信任.特别是您无法控制请求在“访问”日志中记录的时间.我无法使用我的测试代码使代码失败但是在一般情况下确实存在竞争条件,在这个示例中它应该一直工作,因为代码只是生成atomic operation.只需要定期从一个属性分配中心点.
编辑：我编辑了基于插件的方法,因为它只能工作,因为只有一个地方正在执行插件,如果你创建另一个更新数据的后台任务,那么当你做的不仅仅是一个任务时,它可能会有问题.如果您将从一个BackgroundTask更新,无论代码是什么,您都可以找到它.