Worker中的Python多处理和异常处理
我将python多处理库用于一个算法,在该算法中,我让许多工作人员处理某些数据并将结果返回给父进程。我使用multiprocessing.Queue将作业传递给工人,然后使用第二个队列收集结果 这一切都很好,直到工作人员无法处理某些数据块。在下面的简化示例中,每个工人有两个阶段:Worker中的Python多处理和异常处理,python,exception,error-handling,parallel-processing,multiprocessing,Python,Exception,Error Handling,Parallel Processing,Multiprocessing,我将python多处理库用于一个算法,在该算法中,我让许多工作人员处理某些数据并将结果返回给父进程。我使用multiprocessing.Queue将作业传递给工人,然后使用第二个队列收集结果 这一切都很好,直到工作人员无法处理某些数据块。在下面的简化示例中,每个工人有两个阶段: 初始化-可能会失败,在这种情况下,工作进程应该被销毁 数据处理-处理一个数据块可能会失败,在这种情况下,工作人员应该跳过该数据块并继续处理下一个数据 当这两个阶段中的任何一个都失败时,脚本完成后就会出现死锁。此代码
- 初始化-可能会失败,在这种情况下,工作进程应该被销毁
- 数据处理-处理一个数据块可能会失败,在这种情况下,工作人员应该跳过该数据块并继续处理下一个数据
import multiprocessing as mp
import random
workers_count = 5
# Probability of failure, change to simulate failures
fail_init_p = 0.2
fail_job_p = 0.3
#========= Worker =========
def do_work(job_state, arg):
if random.random() < fail_job_p:
raise Exception("Job failed")
return "job %d processed %d" % (job_state, arg)
def init(args):
if random.random() < fail_init_p:
raise Exception("Worker init failed")
return args
def worker_function(args, jobs_queue, result_queue):
# INIT
# What to do when init() fails?
try:
state = init(args)
except:
print "!Worker %d init fail" % args
return
# DO WORK
# Process data in the jobs queue
for job in iter(jobs_queue.get, None):
try:
# Can throw an exception!
result = do_work(state, job)
result_queue.put(result)
except:
print "!Job %d failed, skip..." % job
finally:
jobs_queue.task_done()
# Telling that we are done with processing stop token
jobs_queue.task_done()
#========= Parent =========
jobs = mp.JoinableQueue()
results = mp.Queue()
for i in range(workers_count):
mp.Process(target=worker_function, args=(i, jobs, results)).start()
# Populate jobs queue
results_to_expect = 0
for j in range(30):
jobs.put(j)
results_to_expect += 1
# Collecting the results
# What if some workers failed to process the job and we have
# less results than expected
for r in range(results_to_expect):
result = results.get()
print result
#Signal all workers to finish
for i in range(workers_count):
jobs.put(None)
#Wait for them to finish
jobs.join()
将多处理导入为mp
随机输入
工人人数=5
#失效概率、模拟失效的变更
失败初始p=0.2
作业失败\u p=0.3
#==========工人=========
def do_工作(作业状态,arg):
如果random.random()
关于该代码,我有两个问题:
init()
失败时,如何检测工作进程是否无效,而不是等待它完成do_work()
失败时,如何通知父进程结果队列中预期的结果会减少李>
谢谢你的帮助 我稍微修改了您的代码以使其正常工作(请参见下面的解释)
将多处理导入为mp
随机输入
工人人数=5
#失效概率、模拟失效的变更
失败初始p=0.5
作业失败\u p=0.4
#==========工人=========
def do_工作(作业状态,arg):
如果random.random()
我的变化:
作业
更改为普通的队列
(而不是可接合队列
)None
jobs)。请注意,并非所有这些都可以从队列中提取(以防工作进程初始化失败)顺便说一句,您的原始代码很好,并且易于使用。随机概率位非常酷。或者您可以将一个元组
(结果,错误)
(成功时错误为无)放入结果队列,以避免带内错误通信。
import multiprocessing as mp
import random
workers_count = 5
# Probability of failure, change to simulate failures
fail_init_p = 0.5
fail_job_p = 0.4
#========= Worker =========
def do_work(job_state, arg):
if random.random() < fail_job_p:
raise Exception("Job failed")
return "job %d processed %d" % (job_state, arg)
def init(args):
if random.random() < fail_init_p:
raise Exception("Worker init failed")
return args
def worker_function(args, jobs_queue, result_queue):
# INIT
# What to do when init() fails?
try:
state = init(args)
except:
print "!Worker %d init fail" % args
result_queue.put('init failed')
return
# DO WORK
# Process data in the jobs queue
for job in iter(jobs_queue.get, None):
try:
# Can throw an exception!
result = do_work(state, job)
result_queue.put(result)
except:
print "!Job %d failed, skip..." % job
result_queue.put('job failed')
#========= Parent =========
jobs = mp.Queue()
results = mp.Queue()
for i in range(workers_count):
mp.Process(target=worker_function, args=(i, jobs, results)).start()
# Populate jobs queue
results_to_expect = 0
for j in range(30):
jobs.put(j)
results_to_expect += 1
init_failures = 0
job_failures = 0
successes = 0
while job_failures + successes < 30 and init_failures < workers_count:
result = results.get()
init_failures += int(result == 'init failed')
job_failures += int(result == 'job failed')
successes += int(result != 'init failed' and result != 'job failed')
#print init_failures, job_failures, successes
for ii in range(workers_count):
jobs.put(None)