如何跟踪从多处理池返回的异步结果

Question

我试图将多处理功能添加到某些功能无法修改的代码中。我想将这些功能作为作业异步提交到多处理池。我正在做类似此处所示代码的操作。但是，我不确定如何跟踪结果。我如何知道返回的结果与哪个应用函数相对应？

需要强调的要点是，我无法修改现有功能（其他东西依赖于它们的原样保留），并且结果可以按与功能作业应用于池的顺序不同的顺序返回。

感谢您对此的任何想法！

编辑：下面的一些尝试代码：

import multiprocessing
from multiprocessing import Pool
import os
import signal
import time
import inspect

def multiply(multiplicand1=0, multiplicand2=0):
    return multiplicand1*multiplicand2

def workFunctionTest(**kwargs):
    time.sleep(3)
    return kwargs

def printHR(object):
    """
    This function prints a specified object in a human readable way.
    """
    # dictionary
    if isinstance(object, dict):
        for key, value in sorted(object.items()):
            print u'{a1}: {a2}'.format(a1=key, a2=value)
    # list or tuple
    elif isinstance(object, list) or isinstance(object, tuple):
        for element in object:
            print element
    # other
    else:
        print object

class Job(object):
    def __init__(
        self,
        workFunction=workFunctionTest,
        workFunctionKeywordArguments={'testString': "hello world"},
        workFunctionTimeout=1,
        naturalLanguageString=None,
        classInstance=None,
        resultGetter=None,
        result=None
        ):
        self.workFunction=workFunction
        self.workFunctionKeywordArguments=workFunctionKeywordArguments
        self.workFunctionTimeout=workFunctionTimeout
        self.naturalLanguageString=naturalLanguageString
        self.classInstance=self.__class__.__name__
        self.resultGetter=resultGetter
        self.result=result
    def description(self):
        descriptionString=""
        for key, value in sorted(vars(self).items()):
            descriptionString+=str("{a1}:{a2} ".format(a1=key, a2=value))
        return descriptionString
    def printout(self):
        """
        This method prints a dictionary of all data attributes.
        """
        printHR(vars(self))

class JobGroup(object):
    """
    This class acts as a container for jobs. The data attribute jobs is a list of job objects.
    """
    def __init__(
        self,
        jobs=None,
        naturalLanguageString="null",
        classInstance=None,
        result=None
        ):
        self.jobs=jobs
        self.naturalLanguageString=naturalLanguageString
        self.classInstance=self.__class__.__name__
        self.result=result
    def description(self):
        descriptionString=""
        for key, value in sorted(vars(self).items()):
            descriptionString+=str("{a1}:{a2} ".format(a1=key, a2=value))
        return descriptionString
    def printout(self):
        """
        This method prints a dictionary of all data attributes.
        """
        printHR(vars(self))

def initialise_processes():
    signal.signal(signal.SIGINT, signal.SIG_IGN)

def execute(
        jobObject=None,
        numberOfProcesses=multiprocessing.cpu_count()
        ):
        # Determine the current function name.
    functionName=str(inspect.stack()[0][3])
    def collateResults(result):
        """
        This is a process pool callback function which collates a list of results returned.
        """
        # Determine the caller function name.
        functionName=str(inspect.stack()[1][3])
        print("{a1}: result: {a2}".format(a1=functionName, a2=result))
        results.append(result)
    def getResults(job):
        # Determine the current function name.
        functionName=str(inspect.stack()[0][3])
        while True:
            try:
                result=job.resultGetter.get(job.workFunctionTimeout)
                break
            except multiprocessing.TimeoutError:
                print("{a1}: subprocess timeout for job".format(a1=functionName, a2=job.description()))
        #job.result=result
        return result
    # Create a process pool.
    pool1 = multiprocessing.Pool(numberOfProcesses, initialise_processes)
    print("{a1}: pool {a2} of {a3} processes created".format(a1=functionName, a2=str(pool1), a3=str(numberOfProcesses)))
    # Unpack the input job object and submit it to the process pool.
    print("{a1}: unpacking and applying job object {a2} to pool...".format(a1=functionName, a2=jobObject))
    if isinstance(jobObject, Job):
        # If the input job object is a job, apply it to the pool with its associated timeout specification.
        # Return a list of results.
        job=jobObject
        print("{a1}: job submitted to pool: {a2}".format(a1=functionName, a2=job.description()))
        # Apply the job to the pool, saving the object pool.ApplyResult to the job object.
        job.resultGetter=pool1.apply_async(
                func=job.workFunction,
                kwds=job.workFunctionKeywordArguments
        )
        # Get results.
        # Acquire the job result with respect to the specified job timeout and apply this result to the job data attribute result.
        print("{a1}: getting results for job...".format(a1=functionName))
        job.result=getResults(job)
        print("{a1}: job completed: {a2}".format(a1=functionName, a2=job.description()))
        print("{a1}: job result: {a2}".format(a1=functionName, a2=job.result))
        # Return the job result from execute.
        return job.result
        pool1.terminate()
        pool1.join()
    elif isinstance(jobObject, JobGroup):
        # If the input job object is a job group, cycle through each job and apply it to the pool with its associated timeout specification.
        for job in jobObject.jobs:
            print("{a1}: job submitted to pool: {a2}".format(a1=functionName, a2=job.description()))
            # Apply the job to the pool, saving the object pool.ApplyResult to the job object.
            job.resultGetter=pool1.apply_async(
                    func=job.workFunction,
                    kwds=job.workFunctionKeywordArguments
            )
        # Get results.
        # Cycle through each job and and append the result for the job to a list of results.
        results=[]
        for job in jobObject.jobs:
            # Acquire the job result with respect to the specified job timeout and apply this result to the job data attribute result.
            print("{a1}: getting results for job...".format(a1=functionName))
            job.result=getResults(job)
            print("{a1}: job completed: {a2}".format(a1=functionName, a2=job.description()))
            #print("{a1}: job result: {a2}".format(a1=functionName, a2=job.result))
            # Collate the results.
            results.append(job.result)
        # Apply the list of results to the job group data attribute results.
        jobObject.results=results
        print("{a1}: job group results: {a2}".format(a1=functionName, a2=jobObject.results))
        # Return the job result list from execute.
        return jobObject.results
        pool1.terminate()
        pool1.join()
    else:
        # invalid input object
        print("{a1}: invalid job object {a2}".format(a1=functionName, a2=jobObject))

def main():
    print('-'*80)
    print("MULTIPROCESSING SYSTEM DEMONSTRATION\n")

    # Create a job.
    print("# creating a job...\n")
    job1=Job(
            workFunction=workFunctionTest,
            workFunctionKeywordArguments={'testString': "hello world"},
            workFunctionTimeout=4
    )
    print("- printout of new job object:")
    job1.printout()
    print("\n- printout of new job object in logging format:")
    print job1.description()

    # Create another job.
    print("\n# creating another job...\n")
    job2=Job(
            workFunction=multiply,
            workFunctionKeywordArguments={'multiplicand1': 2, 'multiplicand2': 3},
            workFunctionTimeout=6
    )
    print("- printout of new job object:")
    job2.printout()
    print("\n- printout of new job object in logging format:")
    print job2.description()

    # Create a JobGroup object.
    print("\n# creating a job group (of jobs 1 and 2)...\n")
    jobGroup1=JobGroup(
            jobs=[job1, job2],
    )
    print("- printout of new job group object:")
    jobGroup1.printout()
    print("\n- printout of new job group object in logging format:")
    print jobGroup1.description()

    # Submit the job group.
    print("\nready to submit job group")
    response=raw_input("\nPress Enter to continue...\n")
    execute(jobGroup1)

    response=raw_input("\nNote the results printed above. Press Enter to continue the demonstration.\n")

    # Demonstrate timeout.
    print("\n # creating a new job in order to demonstrate timeout functionality...\n")
    job3=Job(
            workFunction=workFunctionTest,
            workFunctionKeywordArguments={'testString': "hello world"},
            workFunctionTimeout=1
    )
    print("- printout of new job object:")
    job3.printout()
    print("\n- printout of new job object in logging format:")
    print job3.description()
    print("\nNote the timeout specification of only 1 second.")

    # Submit the job.
    print("\nready to submit job")
    response=raw_input("\nPress Enter to continue...\n")
    execute(job3)

    response=raw_input("\nNote the recognition of timeouts printed above. This concludes the demonstration.")
    print('-'*80)

if __name__ == '__main__':
    main()

---

编辑：由于以下陈述的原因，此问题被[保留]：

“要求代码的问题必须表现出对所解决问题的最低限度的理解。包括尝试的解决方案，为何不起作用以及预期的结果。另请参见：堆栈溢出问题清单”

这个问题不是在请求代码。它要求思想，一般指导。展示了对所考虑问题的最低理解（请注意正确使用术语“多处理”，“池”和“异步”，并注意对先前代码的引用）。关于尝试的解决方案，我承认尝试解决方案将是有益的。我现在添加了这样的代码。我希望我已经解决了引起[搁置]状态的关切。

Answer 1

没有看到实际的代码，我只能概括地回答。但是有两种通用的解决方案。

首先，不要使用acallback并忽略AsyncResults
，而是将它们存储在某种集合中。然后，您可以使用该集合。例如，如果您希望能够使用该函数作为键来查找该函数的结果，则只需dict使用以下函数创建一个键：

def in_parallel(funcs):
    results = {}
    pool = mp.Pool()
    for func in funcs:
        results[func] = pool.apply_async(func)
    pool.close()
    pool.join()
    return {func: result.get() for func, result in results.items()}

另外，您可以更改回调函数以按键将结果存储在集合中。例如：

def in_parallel(funcs):
    results = {}
    pool = mp.Pool()
    for func in funcs:
        def callback(result, func=func):
            results[func] = result
        pool.apply_async(func, callback=callback)
    pool.close()
    pool.join()
    return results

---

我将函数本身用作键。但是，您想使用索引来代替，这很容易。您拥有的任何值都可以用作键。

---

同时，您链接的示例实际上只是对一堆参数调用相同的函数，等待所有参数完成，然后以任意顺序将结果以可迭代的方式保留。imap_unordered确实是这样，但是简单得多。您可以用以下代码替换链接代码中的整个复杂的东西：

pool = mp.Pool()
results = list(pool.imap_unordered(foo_pool, range(10)))
pool.close()
pool.join()

然后，如果您希望结果按其原始顺序而不是任意顺序排列，则可以切换到imap或map代替。所以：

pool = mp.Pool()
results = pool.map(foo_pool, range(10))
pool.close()
pool.join()

如果您需要类似但又太复杂以至于无法融入该map范式的内容，concurrent.futures则可能会使您的生活比轻松multiprocessing。如果您使用的是Python
2.x，则必须安装backport。但是，然后您可以完成用AsyncResults或callbacks（或map）完成的工作，例如将一大堆期货组成一个大期货。请参阅链接文档中的示例。

---

最后一点：

需要强调的重点是我无法修改现有功能…

如果您无法修改函数，则可以随时对其进行包装。例如，假设我有一个返回数字平方的函数，但是我试图构建一个将数字异步映射到其平方的字典，所以我也需要将原始数字作为结果的一部分。这很容易：

def number_and_square(x):
    return x, square(x)

现在，我可以apply_async(number_and_square)代替square而获得所需的结果。

在上面的示例中，我没有这样做，因为在第一种情况下，我是从调用方将密钥存储到集合中的，而在第二种情况下，我将其绑定到了回调函数中。但是，将它们绑定到函数周围的包装就像这两个函数一样容易，并且在这两个函数都不存在的情况下可能是合适的。